diff --git a/R/Centrer.red.R b/R/Centrer.red.R index 83173c2..a5670c7 100644 --- a/R/Centrer.red.R +++ b/R/Centrer.red.R @@ -3,33 +3,33 @@ Centrer.red <- packages<-c('svDialogs') #faire l analyse par groupe # regler le probleme des noms list()->Resultats - X<-txt_other_data - while(any(X==txt_other_data)){nom <- Filter( function(x) 'data.frame' %in% class( get(x) ), ls(envir=.GlobalEnv) ) - if(info==TRUE) {print((ask_chose_database))} - nom<-dlgList(c(nom,txt_other_data) , multiple = FALSE, title=txt_dataframe_choice)$res + X<-.dico[["txt_other_data"]] + while(any(X==.dico[["txt_other_data"]])){nom <- Filter( function(x) 'data.frame' %in% class( get(x) ), ls(envir=.GlobalEnv) ) + if(info==TRUE) {print((.dico[["ask_chose_database"]]))} + nom<-dlgList(c(nom,.dico[["txt_other_data"]]) , multiple = FALSE, title=.dico[["txt_dataframe_choice"]])$res if(length(nom)==0) return(preprocess()) data<-get(nom) - if(info==TRUE) {print((ask_chose_variables))} - X<-dlgList(names(data), multiple = TRUE, title=txt_variables)$res + if(info==TRUE) {print((.dico[["ask_chose_variables"]]))} + X<-dlgList(names(data), multiple = TRUE, title=.dico[["txt_variables"]])$res if(length(X)==0) X<-donnees() - if(any(sapply(data[,X], class) %in% c("integer", "numeric")==FALSE)) {print(desc_at_least_one_non_numeric) - X<-txt_other_data + if(any(sapply(data[,X], class) %in% c("integer", "numeric")==FALSE)) {print(.dico[["desc_at_least_one_non_numeric"]]) + X<-.dico[["txt_other_data"]] str(data)} } - if(info==TRUE) {writeLines(desc_center_and_center_reduce_explaination)} - dlgList(c(txt_center, txt_center_reduce, txt_inferior_proba, txt_superior_proba), preselect=txt_center_reduce, multiple = TRUE, title=ask_what_to_do)$res->choix + if(info==TRUE) {writeLines(.dico[["desc_center_and_center_reduce_explaination"]])} + dlgList(c(.dico[["txt_center"]], .dico[["txt_center_reduce"]], .dico[["txt_inferior_proba"]], .dico[["txt_superior_proba"]]), preselect=.dico[["txt_center_reduce"]], multiple = TRUE, title=.dico[["ask_what_to_do"]])$res->choix if(length(choix)==0) return(preprocess()) for(i in 1:length(choix)){ - if(choix[i]==txt_center) {S<-FALSE - nn<-txt_center}else {S<-TRUE - nn<-txt_centered_dot_reduced} + if(choix[i]==.dico[["txt_center"]]) {S<-FALSE + nn<-.dico[["txt_center"]]}else {S<-TRUE + nn<-.dico[["txt_centered_dot_reduced"]]} scale(data[,X], scale=S)->centree matrix(centree, ncol=length(X))->centree - if(choix[i]==txt_superior_proba|choix[i]==txt_inferior_proba){ - if(choix[i]==txt_superior_proba){ + if(choix[i]==.dico[["txt_superior_proba"]]|choix[i]==.dico[["txt_inferior_proba"]]){ + if(choix[i]==.dico[["txt_superior_proba"]]){ nn<-"p.sup" lower<-FALSE }else { @@ -44,6 +44,6 @@ Centrer.red <- assign(nom, data, envir=.GlobalEnv) View(data) - Resultats<-paste(desc_succesful_operation) + Resultats<-paste(.dico[["desc_succesful_operation"]]) return(Resultats) } diff --git a/R/SelectionV.R b/R/SelectionV.R index 2a406df..e1526c4 100644 --- a/R/SelectionV.R +++ b/R/SelectionV.R @@ -6,20 +6,20 @@ SelectionV <- list()->Resultats choix.data()->data if(length(data)==0) return(preprocess()) - if(info==TRUE) print(ask_variables) - X<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), txt_other_data), multiple = TRUE, - title=txt_variable)$res + if(info==TRUE) print(.dico[["ask_variables"]]) + X<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), .dico[["txt_other_data"]]), multiple = TRUE, + title=.dico[["txt_variable"]])$res if(length(X)==0) return(preprocess()) - if( X== txt_other_data) return(SelectionV()) + if( X== .dico[["txt_other_data"]]) return(SelectionV()) listes<-data.frame(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), 1:length(data)) subset(listes, listes[,1] %in% X)[,2]->X data[,X]->data - fichier<- dlgInput(ask_filename, txt_selection)$res - if(length(fichier)==0) fichier<-txt_selection + fichier<- dlgInput(.dico[["ask_filename"]], .dico[["txt_selection"]])$res + if(length(fichier)==0) fichier<-.dico[["txt_selection"]] strsplit(fichier, ":")->fichier tail(fichier[[1]],n=1)->fichier assign(x=fichier, value=data, envir=.GlobalEnv) - View(data, txt_selected_data) - Resultats<-paste(desc_variables_are_in, fichier) + View(data, .dico[["txt_selected_data"]]) + Resultats<-paste(.dico[["desc_variables_are_in"]], fichier) return(Resultats) } diff --git a/R/VI.multiples.R b/R/VI.multiples.R index 4f9afe0..47e477f 100644 --- a/R/VI.multiples.R +++ b/R/VI.multiples.R @@ -4,7 +4,7 @@ VI.multiples <- nvar<-length(data) try(psych::outlier(data, bad=T, na.rm=T,plot=T),silent=T)->essai if(class(essai)=='try-error'){ - msgBox(desc_singular_matrix_mahalanobis_on_max_info) + msgBox(.dico[["desc_singular_matrix_mahalanobis_on_max_info"]]) data->data2 rankifremoved <- sapply(1:ncol(data2), function (x) qr(data2[,-x])$rank) which(rankifremoved == max(rankifremoved))->rangs @@ -23,13 +23,13 @@ VI.multiples <- if(class(essai)=='try-error') { corr.test(data2)$r->matrice if(any(abs(matrice)==1)) { - msgBox(desc_perfectly_correlated_variables_in_matrix_trying_to_solve) + msgBox(.dico[["desc_perfectly_correlated_variables_in_matrix_trying_to_solve"]]) which(abs(matrice)==1, arr.ind=TRUE)->un un<-un[-which(un[,1]==un[,2]),] data2[,-un[,2]]->data2 try(psych::outlier(data2), silent=T)->essai if(class(essai)=='try-error') { - writeLines(desc_cannot_compute_mahalanobis) + writeLines(.dico[["desc_cannot_compute_mahalanobis"]]) 0->data$D.Mahalanobis } }else{essai-> data$D.Mahalanobis} } else{ essai-> data$D.Mahalanobis @@ -42,30 +42,30 @@ VI.multiples <- data[which(data$D.Mahalanobis>seuil),]->outliers length(outliers[,1])/length(data[,1])*100->pourcent - msgBox(paste(round(pourcent,2), desc_percentage_outliers)) + msgBox(paste(round(pourcent,2), .dico[["desc_percentage_outliers"]])) if(pourcent!=0){ - writeLines(desc_outliers_removal_implications) + writeLines(.dico[["desc_outliers_removal_implications"]]) - suppr<- dlgList(c(txt_suppress_all_outliers, txt_suppress_outliers_manually), - preselect=c(txt_suppress_all_outliers), multiple = FALSE, title=ask_how_to_remove)$res + suppr<- dlgList(c(.dico[["txt_suppress_all_outliers"]], .dico[["txt_suppress_outliers_manually"]]), + preselect=c(.dico[["txt_suppress_all_outliers"]]), multiple = FALSE, title=.dico[["ask_how_to_remove"]])$res if(length(suppr)==0) return(NULL) - if(suppr==txt_suppress_all_outliers) {data[which(data$D.Mahalanobisdata - outliers->Resultats[[txt_labeled_outliers]]}else{ + if(suppr==.dico[["txt_suppress_all_outliers"]]) {data[which(data$D.Mahalanobisdata + outliers->Resultats[[.dico[["txt_labeled_outliers"]]]]}else{ suppression<-"yes" outliers<-data.frame() while(suppression=="yes"){ print(data[which.max(data$D.Mahalanobis),]) - cat (ask_press_enter_to_continue) + cat (.dico[["ask_press_enter_to_continue"]]) line <- readline() - dlgMessage(ask_suppress_this_obs, "yesno")$res->suppression + dlgMessage(.dico[["ask_suppress_this_obs"]], "yesno")$res->suppression if(suppression=="yes") {rbind(outliers, data[which.max(data$D.Mahalanobis),])->outliers data[-which.max(data$D.Mahalanobis),]->data } } - Resultats[[txt_labeled_outliers]]<-outliers + Resultats[[.dico[["txt_labeled_outliers"]]]]<-outliers } } Resultats$data<-data diff --git a/R/analyse.R b/R/analyse.R index b8e3c48..466b509 100644 --- a/R/analyse.R +++ b/R/analyse.R @@ -1,37 +1,37 @@ analyse <- function(html=T){options (warn=-1) require(svDialogs) - dlgList(c(txt_descriptive_statistics,txt_chi_squared,txt_correlations, - txt_student_t, txt_anova_ancova, - txt_regressions, - txt_analysis_factor_component, - txt_fiability_analysis), preselect=NULL, multiple = FALSE, title=ask_analysis_type)$res->choix + dlgList(c(.dico[["txt_descriptive_statistics"]],.dico[["txt_chi_squared"]],.dico[["txt_correlations"]], + .dico[["txt_student_t"]], .dico[["txt_anova_ancova"]], + .dico[["txt_regressions"]], + .dico[["txt_analysis_factor_component"]], + .dico[["txt_fiability_analysis"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_analysis_type"]])$res->choix if(length(choix)==0) return(easieR()) - if(choix==txt_chi_squared) chi(html=html)->Resultats - if(choix==txt_student_t) test.t(html=html)->Resultats - if(choix==txt_anova_ancova) { + if(choix==.dico[["txt_chi_squared"]]) chi(html=html)->Resultats + if(choix==.dico[["txt_student_t"]]) test.t(html=html)->Resultats + if(choix==.dico[["txt_anova_ancova"]]) { Filter( function(x) 'aovplus' %in% class( get(x) ), ls(envir=.GlobalEnv))->nom1 if(length(nom1)==0) { ez.anova(html=html)->Resultats } else { html=html - dlgList(c(txt_principal_analysis, - txt_complementary_results), + dlgList(c(.dico[["txt_principal_analysis"]], + .dico[["txt_complementary_results"]]), preselect=NULL, multiple = FALSE, - title=ask_analysis_type)$res->choix - if(choix==txt_principal_analysis) { + title=.dico[["ask_analysis_type"]])$res->choix + if(choix==.dico[["txt_principal_analysis"]]) { ez.anova(html=html)->Resultats } else { aov.plus(html=html)->Resultats } } } - if(choix==txt_correlations) choix.corr(html=html)->Resultats - if(choix==txt_regressions) choix.reg(html=html)->Resultats - #if(choix==txt_logistic_regressions) regressions.log()->Resultats - if(choix==txt_analysis_factor_component) factor.an(html=html)->Resultats - if(choix==txt_fiability_analysis) fiabilite(html=html)->Resultats - if(choix==txt_descriptive_statistics) stat.desc(html=html)->Resultats + if(choix==.dico[["txt_correlations"]]) choix.corr(html=html)->Resultats + if(choix==.dico[["txt_regressions"]]) choix.reg(html=html)->Resultats + #if(choix==.dico[["txt_logistic_regressions"]]) regressions.log()->Resultats + if(choix==.dico[["txt_analysis_factor_component"]]) factor.an(html=html)->Resultats + if(choix==.dico[["txt_fiability_analysis"]]) fiabilite(html=html)->Resultats + if(choix==.dico[["txt_descriptive_statistics"]]) stat.desc(html=html)->Resultats return(Resultats) } diff --git a/R/aov.plus.R b/R/aov.plus.R index dbe9d4f..8bae53a 100644 --- a/R/aov.plus.R +++ b/R/aov.plus.R @@ -9,11 +9,11 @@ aov.plus <- if(is.null(aov.plus.list)){ Filter( function(x) 'aovplus' %in% class( get(x) ), ls(envir=.GlobalEnv))->nom1 if(length(nom1)==0) { - writeLines(desc_no_compatible_object_in_mem_for_aov) + writeLines(.dico[["desc_no_compatible_object_in_mem_for_aov"]]) return(ez.anova())} if(length(nom1)==1) aov.plus.list<-get(nom1) else{ - if(info=='TRUE') writeLines(ask_wanted_model) - nom1 <- dlgList(nom1, multiple = FALSE, title=ask_model)$res + if(info=='TRUE') writeLines(.dico[["ask_wanted_model"]]) + nom1 <- dlgList(nom1, multiple = FALSE, title=.dico[["ask_model"]])$res if(length(nom1)==0) {nom1<-NULL aov.plus.list<-NULL} if(!is.null(nom1)) aov.plus.list<-get(nom1) @@ -23,24 +23,24 @@ aov.plus <- if(length(aov.plus.list)==3){ - writeLines(ask_complete_or_outliers) - type<-dlgList(names(aov.plus.list)[2:3], multiple = FALSE, title=ask_which_data_to_analyse)$res - if(length(type)==0) return(desc_user_exited_aov_plus) - if(type==txt_complete_dataset) aov.plus.list[[2]]->aov.plus.list else aov.plus.list[[2]]->aov.plus.list + writeLines(.dico[["ask_complete_or_outliers"]]) + type<-dlgList(names(aov.plus.list)[2:3], multiple = FALSE, title=.dico[["ask_which_data_to_analyse"]])$res + if(length(type)==0) return(.dico[["desc_user_exited_aov_plus"]]) + if(type==.dico[["txt_complete_dataset"]]) aov.plus.list[[2]]->aov.plus.list else aov.plus.list[[2]]->aov.plus.list }else aov.plus.list[[2]]->aov.plus.list - writeLines(desc_this_function_means_and_sd_adjusted_interaction_effect_possible) - choix<-dlgList(c(txt_means_adjusted_standard_errors,txt_contrasts), - multiple = TRUE, title=ask_which_data_to_analyse)$res + writeLines(.dico[["desc_this_function_means_and_sd_adjusted_interaction_effect_possible"]]) + choix<-dlgList(c(.dico[["txt_means_adjusted_standard_errors"]],.dico[["txt_contrasts"]]), + multiple = TRUE, title=.dico[["ask_which_data_to_analyse"]])$res if(length(choix)==0) return(analyse()) Resultats<-list() noms<-names(summary(aov.plus.list[["em.out"]]))[which(sapply(summary(aov.plus.list[["em.out"]]),class) =="factor")] - if(any(choix==txt_means_adjusted_standard_errors)){ - writeLines(ask_which_factors_combination_for_adjust_means) - facteurs<-dlgList(noms, multiple = TRUE, title=ask_what_to_print)$res + if(any(choix==.dico[["txt_means_adjusted_standard_errors"]])){ + writeLines(.dico[["ask_which_factors_combination_for_adjust_means"]]) + facteurs<-dlgList(noms, multiple = TRUE, title=.dico[["ask_what_to_print"]])$res if(length(facteurs)==0) return(aov.plus()) formula<-paste0('~',facteurs[[1]]) if(length(facteurs)>1){ @@ -48,35 +48,35 @@ aov.plus <- formula<-paste(formula, '+', facteurs[i]) }} recordPlot()->graphe - Resultats[[txt_adjusted_means_graph]]<-emmip(object= aov.plus.in[[txt_complete_dataset]]$em.out,as.formula(formula) , CIs=T) - em.out<-emmeans(object= aov.plus.in[[txt_complete_dataset]]$em.out,as.formula(formula), CIs=T) - Resultats[[txt_adjusted_means]]<-data.frame(em.out) + Resultats[[.dico[["txt_adjusted_means_graph"]]]]<-emmip(object= aov.plus.in[[.dico[["txt_complete_dataset"]]]]$em.out,as.formula(formula) , CIs=T) + em.out<-emmeans(object= aov.plus.in[[.dico[["txt_complete_dataset"]]]]$em.out,as.formula(formula), CIs=T) + Resultats[[.dico[["txt_adjusted_means"]]]]<-data.frame(em.out) } - if(any(choix==txt_contrasts)){ - writeLines(ask_specify_contrasts) + if(any(choix==.dico[["txt_contrasts"]])){ + writeLines(.dico[["ask_specify_contrasts"]]) if(length(choix)==0) return(aov.plus()) p.adjust<-dlgList(c("holm", "hochberg", "hommel", "bonferroni", "fdr","tukey","scheffe", - "sidak","dunnettx","mvt" ,"none" ), preselect="holm", multiple = FALSE, title=ask_correction_type)$res + "sidak","dunnettx","mvt" ,"none" ), preselect="holm", multiple = FALSE, title=.dico[["ask_correction_type"]])$res if(length(p.adjust)==0) p.adjust<-"none" - cont.data<-data.frame(aov.plus.in[[txt_complete_dataset]]$em.out) + cont.data<-data.frame(aov.plus.in[[.dico[["txt_complete_dataset"]]]]$em.out) cont.data<-cont.data[, noms] cont.data<-fix(cont.data) suppress<-which(colSums(is.na(cont.data)) > 0) if(length(suppress>0)) cont.data<-cont.data[,-suppress] Resultats$Contrates$coefficients<-cont.data - emm.out<-contrast(aov.plus.in[[txt_complete_dataset]]$em.out, + emm.out<-contrast(aov.plus.in[[.dico[["txt_complete_dataset"]]]]$em.out, method= list(cont.data[, which(sapply(cont.data, class)=="numeric")]), adjust=p.adjust) emm.out<-data.frame(emm.out) - names(emm.out)[6]<-txt_p_dot_val # TODO translation + names(emm.out)[6]<-.dico[["txt_p_dot_val"]] # TODO translation emm.out$contrast<-names(cont.data)[which(sapply(cont.data, class)=="numeric")] Resultats$Contrates$contrastes<-emm.out } - ref1(packages)->Resultats[[desc_references]] - .add.result(Resultats=Resultats, name =paste(txt_anova_plus, Sys.time() )) + ref1(packages)->Resultats[[.dico[["desc_references"]]]] + .add.result(Resultats=Resultats, name =paste(.dico[["txt_anova_plus"]], Sys.time() )) # if(sauvegarde==T) save(Resultats=Resultats ,choix ="Resultats.aov.plus", env=.e) if(html) ez.html(Resultats) return(Resultats) diff --git a/R/chi.R b/R/chi.R index 5c24a26..8b6fa85 100644 --- a/R/chi.R +++ b/R/chi.R @@ -1,11 +1,11 @@ chi <- function(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=TRUE, n.boot=NULL, priorConcentration =1, - SampleType=NULL,fixedMargin=NULL, choix2=c(txt_non_parametric_test,txt_robusts_tests_with_bootstraps, txt_bayesian_factors) ,rscale=2^0.5/2, html=T){ + SampleType=NULL,fixedMargin=NULL, choix2=c(.dico[["txt_non_parametric_test"]],.dico[["txt_robusts_tests_with_bootstraps"]], .dico[["txt_bayesian_factors"]]) ,rscale=2^0.5/2, html=T){ # X = character or vector. First set of variables # Y = character or vector. Second set of variables # Effectifs = character. Name of weighting variable. Must be positive integer # p = vector of probabilities. Must be equal to 1. The lenght must be equel to number of levels of X - # choix = character. One among txt_chi_adjustement, txt_chi_independance, or txt_mcnemar_test + # choix = character. One among .dico[["txt_chi_adjustement"]], .dico[["txt_chi_independance"]], or .dico[["txt_mcnemar_test"]] # data = name of the dataframe # B = number of bootstrap fro computing p.values by Monte-Carlo simulation # priorConcentration : prior concentration paramter, set to 1 by default (see ?contingencyTableBF) @@ -14,9 +14,9 @@ chi <- # rscale : prior scale. A number of preset values can be given as strings chi.in<-function(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL){ if(!is.null(choix)) dial<-F else dial<-T - if(is.null(choix) || (choix %in%c(txt_chi_adjustement, txt_chi_independance, txt_mcnemar_test)==FALSE)){ - if(info) writeLines(ask_chi_squared_type) - choix<- dlgList(c(txt_chi_adjustement, txt_chi_independance, txt_mcnemar_test), preselect=txt_chi_independance, multiple = FALSE, title=txt_chi_squared_type)$res + if(is.null(choix) || (choix %in%c(.dico[["txt_chi_adjustement"]], .dico[["txt_chi_independance"]], .dico[["txt_mcnemar_test"]])==FALSE)){ + if(info) writeLines(.dico[["ask_chi_squared_type"]]) + choix<- dlgList(c(.dico[["txt_chi_adjustement"]], .dico[["txt_chi_independance"]], .dico[["txt_mcnemar_test"]]), preselect=.dico[["txt_chi_independance"]], multiple = FALSE, title=.dico[["txt_chi_squared_type"]])$res if(length(choix)==0) return(NULL) } @@ -24,25 +24,25 @@ chi <- if(length(data)==0) return(NULL) data[[1]]->nom data[[2]]->data - msg3<-ask_first_categorical_set - if(choix==txt_chi_independance) multiple<-T else multiple<-F - X<-.var.type(X=X, info=info, data=data, type="factor", check.prod=F, message=msg3, multiple=multiple, title=txt_variables, out=NULL) + msg3<-.dico[["ask_first_categorical_set"]] + if(choix==.dico[["txt_chi_independance"]]) multiple<-T else multiple<-F + X<-.var.type(X=X, info=info, data=data, type="factor", check.prod=F, message=msg3, multiple=multiple, title=.dico[["txt_variables"]], out=NULL) if(is.null(X)) { chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats)} X$data->data X$X->X - if(choix!=txt_chi_adjustement){ - msg4<-ask_second_categorical_set - Y<-.var.type(X=Y, info=info, data=data, type="factor", check.prod=F, message=msg4, multiple=multiple, title=txt_variables, out=NULL) + if(choix!=.dico[["txt_chi_adjustement"]]){ + msg4<-.dico[["ask_second_categorical_set"]] + Y<-.var.type(X=Y, info=info, data=data, type="factor", check.prod=F, message=msg4, multiple=multiple, title=.dico[["txt_variables"]], out=NULL) if(is.null(Y)) { chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats)} Y$data->data Y$X->Y - if(choix==txt_mcnemar_test & any(sapply(data[,c(X,Y)],nlevels)!=2)) { - msgBox(desc_mcnemar_need_2x2_table_yours_are_different) + if(choix==.dico[["txt_mcnemar_test"]] & any(sapply(data[,c(X,Y)],nlevels)!=2)) { + msgBox(.dico[["desc_mcnemar_need_2x2_table_yours_are_different"]]) print(table(data[,X], data[,Y], dnn=c(X,Y))) chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats) @@ -50,16 +50,16 @@ chi <- } if(dial){ - if(info==T) writeLines(ask_ponderate_analysis_by_a_sample_var) - Effectifs<-dlgList(c(txt_yes, txt_no), multiple = F, preselect=txt_no, title=ask_specify_sample)$res + if(info==T) writeLines(.dico[["ask_ponderate_analysis_by_a_sample_var"]]) + Effectifs<-dlgList(c(.dico[["txt_yes"]], .dico[["txt_no"]]), multiple = F, preselect=.dico[["txt_no"]], title=.dico[["ask_specify_sample"]])$res if(length(Effectifs)==0) { chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats)} - if(Effectifs==txt_no) Effectifs<-NULL} + if(Effectifs==.dico[["txt_no"]]) Effectifs<-NULL} if(!is.null(Effectifs)){ - msg5<-ask_chose_sample_variables - .var.type(X=Effectifs, info=T, data=data, type="integer", message=msg5,multiple=F, title=ask_specify_sample_variable, out=c(X, Y))->Effectifs + msg5<-.dico[["ask_chose_sample_variables"]] + .var.type(X=Effectifs, info=T, data=data, type="integer", message=msg5,multiple=F, title=.dico[["ask_specify_sample_variable"]], out=c(X, Y))->Effectifs if(is.null(Effectifs)) { chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats)} @@ -68,87 +68,87 @@ chi <- # check variable if(!is.null(Effectifs)) sum(data[,Effectifs])->tot else length(data[,1])->tot - if(choix!=txt_chi_adjustement) { + if(choix!=.dico[["txt_chi_adjustement"]]) { expand.grid(X, Y)->comb comb[which(as.vector(comb[,1])!=as.vector(comb[,2])),]->comb if(any(apply(comb, 1, function(x) prod(sapply(data[,x],nlevels)))>tot)){ which(apply(comb, 1, function(x) prod(sapply(data[,x],nlevels)))>tot)->trop for(i in length(trop):1){ - msg6<-paste0(desc_insufficient_sample_for_combinations_between, comb[trop[i],1], desc_and_variable_y, comb[trop[i],2], desc_this_analysis_will_not_be_performed) + msg6<-paste0(.dico[["desc_insufficient_sample_for_combinations_between"]], comb[trop[i],1], .dico[["desc_and_variable_y"]], comb[trop[i],2], .dico[["desc_this_analysis_will_not_be_performed"]]) msgBox(msg6) comb[ -which(dimnames(comb)[[1]]==names(trop)[i]),]->comb } if(length(comb[,1])==0) { - msgBox(desc_no_analysis_can_be_performed_given_your_data) + msgBox(.dico[["desc_no_analysis_can_be_performed_given_your_data"]]) return(NULL) } } } - if(choix==txt_chi_adjustement) { + if(choix==.dico[["txt_chi_adjustement"]]) { if(dial==F & is.null(p)) rep(1/nlevels(data[,X]),times=nlevels(data[,X]))->p if(sum(p)!=1 | any(p)>1 | any(p)<0) p<-NULL while(is.null(p)){ - if(info==T) writeLines(ask_probabilities_for_modalities) - dlgForm(setNames(as.list(rep(1/nlevels(data[,X]),times=nlevels(data[,X]))), levels(data[,X])), desc_probabilities_vector_please_no_fraction)$res->niveaux + if(info==T) writeLines(.dico[["ask_probabilities_for_modalities"]]) + dlgForm(setNames(as.list(rep(1/nlevels(data[,X]),times=nlevels(data[,X]))), levels(data[,X])), .dico[["desc_probabilities_vector_please_no_fraction"]])$res->niveaux stack(niveaux)[,1]->p if(sum(p)!=1 ||length(p)!=nlevels(data[,X]) | any(p)>1 | any(p)<0){ - if( dlgMessage(desc_proba_sum_is_not_one_or_not_enough_proba,"okcancel")$res=="cancel") { + if( dlgMessage(.dico[["desc_proba_sum_is_not_one_or_not_enough_proba"]],"okcancel")$res=="cancel") { chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats)} else return(NULL) } } } - if(choix==txt_mcnemar_test) robust<-F else robust<-T - if(choix==txt_chi_adjustement) Bayes<-F else Bayes<-T - msg.options<-desc_in_that_case_non_parametric_is_classical_chi_squared + if(choix==.dico[["txt_mcnemar_test"]]) robust<-F else robust<-T + if(choix==.dico[["txt_chi_adjustement"]]) Bayes<-F else Bayes<-T + msg.options<-.dico[["desc_in_that_case_non_parametric_is_classical_chi_squared"]] .ez.options(options='choix', n.boot=n.boot,param=F, non.param=T, robust=robust, Bayes=Bayes, msg.options1=NULL, msg.options2=msg.options, info=T, dial=dial, choix=choix2)->Options if(is.null(Options)){ chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats)} if(dial==T || any(SampleType %in% c("poisson", "jointMulti","hypergeom", "indepMulti"))==F || SampleType=="indepMulti" & any(fixedMargin %in% c("rows","cols"))==F){ - if(any(Options$choix==txt_bayesian_factors) && choix== txt_chi_independance ){ + if(any(Options$choix==.dico[["txt_bayesian_factors"]]) && choix== .dico[["txt_chi_independance"]] ){ if(info==T) { - writeLines(ask_sampling_type) - cat(desc_if_non_fixed_sample_poisson_law) + writeLines(.dico[["ask_sampling_type"]]) + cat(.dico[["desc_if_non_fixed_sample_poisson_law"]]) print(matrix(c(100,50,200,100), nrow=2, ncol=2, dimnames=list(c("A.1", "A.2"), c("B.1", "B.2")) )) - writeLines(desc_distribution_is_joint_multinomial) + writeLines(.dico[["desc_distribution_is_joint_multinomial"]]) print(matrix(c(100,100,100,100), nrow=2, ncol=2, dimnames=list(c("A.1", "A.2"), c("B.1", "B.2")) )) - writeLines(desc_distribution_is_independant_multinomial) + writeLines(.dico[["desc_distribution_is_independant_multinomial"]]) print(matrix(c(15,40,55, 85,60,145, 100,100,200), nrow=3, ncol=3, dimnames=list(c("A.1", "A.2", "total"), c("B.1", "B.2", "total")) )) - writeLines(desc_identical_option_total_sample) - writeLines(desc_distribution_is_hypergeometric_when) + writeLines(.dico[["desc_identical_option_total_sample"]]) + writeLines(.dico[["desc_distribution_is_hypergeometric_when"]]) print(matrix(c(15,85,100, 85,15,100, 100,100,200), nrow=3, ncol=3, dimnames=list(c("A.1", "A.2", "total"), c("B.1", "B.2", "total")) )) } SampleType<-c() FM<-c() for(i in 1:length(comb[,1])){ - if(nlevels(data[,as.character(comb[i,1])])==2 && nlevels(data[,as.character(comb[i,2])])==2) possible<- c(txt_poisson_total_not_fixed_sample, txt_jointmulti_total_fixed_sample, - paste(txt_indepmulti_total_fixed_rows_cols, comb[i,1]), - paste(txt_indepmulti_fixed_sample_rows_cols, comb[i,2]), - txt_hypergeom_total_sample_fixed_rows_cols) else { - possible<- c(txt_poisson_total_not_fixed_sample, txt_jointmulti_total_fixed_sample, - paste(txt_indepmulti_total_fixed_rows_cols, comb[i,1]), - paste(txt_indepmulti_fixed_sample_rows_cols, comb[i,2])) + if(nlevels(data[,as.character(comb[i,1])])==2 && nlevels(data[,as.character(comb[i,2])])==2) possible<- c(.dico[["txt_poisson_total_not_fixed_sample"]], .dico[["txt_jointmulti_total_fixed_sample"]], + paste(.dico[["txt_indepmulti_total_fixed_rows_cols"]], comb[i,1]), + paste(.dico[["txt_indepmulti_fixed_sample_rows_cols"]], comb[i,2]), + .dico[["txt_hypergeom_total_sample_fixed_rows_cols"]]) else { + possible<- c(.dico[["txt_poisson_total_not_fixed_sample"]], .dico[["txt_jointmulti_total_fixed_sample"]], + paste(.dico[["txt_indepmulti_total_fixed_rows_cols"]], comb[i,1]), + paste(.dico[["txt_indepmulti_fixed_sample_rows_cols"]], comb[i,2])) } - SampleType1<-dlgList(possible, preselect=txt_total_sample_not_fixed, multiple = FALSE, title=paste(txt_experimental_pan_between, comb[i,1], desc_and,comb[i,2], "?"))$res + SampleType1<-dlgList(possible, preselect=.dico[["txt_total_sample_not_fixed"]], multiple = FALSE, title=paste(.dico[["txt_experimental_pan_between"]], comb[i,1], .dico[["desc_and"]],comb[i,2], "?"))$res if(length(SampleType1)==0) {chi.in(X=NULL, Y=NULL, Effectifs=NULL, p=NULL, choix=NULL, data=NULL, info=T, n.boot=NULL, SampleType=NULL, FM=NULL, choix2=NULL)->Resultats return(Resultats)} - ifelse(SampleType1 == paste(txt_indepmulti_total_fixed_rows_cols, comb[i,1]), fixedMargin<-"rows", - ifelse(SampleType1 == paste(txt_indepmulti_fixed_sample_rows_cols, comb[i,2]), fixedMargin<-"cols", fixedMargin<-0)) + ifelse(SampleType1 == paste(.dico[["txt_indepmulti_total_fixed_rows_cols"]], comb[i,1]), fixedMargin<-"rows", + ifelse(SampleType1 == paste(.dico[["txt_indepmulti_fixed_sample_rows_cols"]], comb[i,2]), fixedMargin<-"cols", fixedMargin<-0)) FM<-c(FM,fixedMargin ) #ST<- switch(SampleType1, txt_poisson_total_not_fixed_sample= "poisson", # txt_jointmulti_total_fixed_sample="jointMulti", # "hypergeom - Effectif total fixe pour les lignes et les colonnes"= "hypergeom") - if (SampleType1==txt_poisson_total_not_fixed_sample) "poisson"->ST - if (SampleType1==txt_jointmulti_total_fixed_sample) "jointMulti"->ST - if (SampleType1==txt_hypergeom_total_sample_fixed_rows_cols) "hypergeom"->ST - if(SampleType1==paste(txt_indepmulti_total_fixed_rows_cols, comb[i,1])) ST<-"indepMulti" - if(SampleType1==paste(txt_indepmulti_fixed_sample_rows_cols, comb[i,2])) ST<-"indepMulti" + if (SampleType1==.dico[["txt_poisson_total_not_fixed_sample"]]) "poisson"->ST + if (SampleType1==.dico[["txt_jointmulti_total_fixed_sample"]]) "jointMulti"->ST + if (SampleType1==.dico[["txt_hypergeom_total_sample_fixed_rows_cols"]]) "hypergeom"->ST + if(SampleType1==paste(.dico[["txt_indepmulti_total_fixed_rows_cols"]], comb[i,1])) ST<-"indepMulti" + if(SampleType1==paste(.dico[["txt_indepmulti_fixed_sample_rows_cols"]], comb[i,2])) ST<-"indepMulti" SampleType<-c(SampleType, ST) } @@ -159,7 +159,7 @@ chi <- Resultats$analyse<-choix Resultats$data<-data Resultats$nom.data<-nom - if(choix==txt_chi_adjustement) Resultats$Variables<-X else Resultats$Variables<-comb + if(choix==.dico[["txt_chi_adjustement"]]) Resultats$Variables<-X else Resultats$Variables<-comb Resultats$Effectifs<-Effectifs Resultats$p<-p Resultats$choix<-Options$choix @@ -175,28 +175,28 @@ chi <- V<-round((x/((min(dims)-1)*n))^0.5,3) V.sq<-round(V^2,3) resultats<-data.frame("V"=V, "V.carre"=V.sq) - names(resultats) <-c("V", txt_cramer_v_square) + names(resultats) <-c("V", .dico[["txt_cramer_v_square"]]) return(resultats)} chi.out<-function(data=NULL, X=NULL, Y=NULL, p=NULL, choix=NULL, Effectifs=NULL, n.boot=NULL, SampleType=NULL, fixedMargin=NULL, choix2=NULL, rscale=2^0.5/2,priorConcentration=1){ Resultats<-list() - if(choix==txt_chi_adjustement){ + if(choix==.dico[["txt_chi_adjustement"]]){ if(!is.null(Effectifs)){ tapply(data[,Effectifs], data[,X],sum,na.rm=TRUE)->tab rbind(tab,p, p*sum(data[,Effectifs]))->Distribution} else { table(data[,X])->tab rbind(tab, p, sum(tab)*p)->Distribution} - dimnames(Distribution)[[1]]<-c(txt_observed, txt_probabilities,txt_expected) - Resultats[[txt_synthesis_table]]<-Distribution + dimnames(Distribution)[[1]]<-c(.dico[["txt_observed"]], .dico[["txt_probabilities"]],.dico[["txt_expected"]]) + Resultats[[.dico[["txt_synthesis_table"]]]]<-Distribution chi<-chisq.test(tab, p=p, B=n.boot) - Resultats[[txt_chi_dot_squared_adjustment]]<-data.frame(chi.deux=round(chi$statistic,3), ddl=chi$parameter) - names(Resultats[[txt_chi_dot_squared_adjustment]])<-c(txt_chi_dot_squared, txt_df) - if(any(choix2== txt_non_parametric_test)) Resultats[[txt_chi_dot_squared_adjustment]][[txt_p_dot_val]]<-round(chi$p.value,4) + Resultats[[.dico[["txt_chi_dot_squared_adjustment"]]]]<-data.frame(chi.deux=round(chi$statistic,3), ddl=chi$parameter) + names(Resultats[[.dico[["txt_chi_dot_squared_adjustment"]]]])<-c(.dico[["txt_chi_dot_squared"]], .dico[["txt_df"]]) + if(any(choix2== .dico[["txt_non_parametric_test"]])) Resultats[[.dico[["txt_chi_dot_squared_adjustment"]]]][[.dico[["txt_p_dot_val"]]]]<-round(chi$p.value,4) if(!is.null(n.boot) && n.boot>1){ - Resultats[[txt_chi_dot_squared_adjustment]][[txt_p_estimation_with_monter_carlo]]<-round(chisq.test(tab, B=n.boot, simulate.p.value=T, correct=F)$p.value,4)} + Resultats[[.dico[["txt_chi_dot_squared_adjustment"]]]][[.dico[["txt_p_estimation_with_monter_carlo"]]]]<-round(chisq.test(tab, B=n.boot, simulate.p.value=T, correct=F)$p.value,4)} } - if((choix!=txt_chi_adjustement)){ + if((choix!=.dico[["txt_chi_adjustement"]])){ if (is.null(Effectifs)) tab<-table(data[,X],data[ ,Y], dnn=c(X, Y))else { tab<-tapply(data[,Effectifs],list(data[,X],data[,Y]),sum,na.rm=TRUE) tab[is.na(tab)] <- 0 @@ -205,29 +205,29 @@ chi <- } # graphique spineplot(tab, col=topo.colors(nlevels(data[,Y]))) - table.margins(tab)->Resultats[[txt_observed_sample]] - if(choix==txt_chi_independance){ + table.margins(tab)->Resultats[[.dico[["txt_observed_sample"]]]] + if(choix==.dico[["txt_chi_independance"]]){ mon.chi<-chisq.test(tab, B=n.boot, correct=F) - mon.chi$expected->Resultats[[txt_expected_sample]] - if(any(choix2 %in% c(txt_non_parametric_test,txt_robusts_tests_with_bootstraps))) { + mon.chi$expected->Resultats[[.dico[["txt_expected_sample"]]]] + if(any(choix2 %in% c(.dico[["txt_non_parametric_test"]],.dico[["txt_robusts_tests_with_bootstraps"]]))) { SY<-data.frame( txt_chi_dot_squared=round(mon.chi$statistic,4), txt_df=mon.chi$parameter, Cramer(mon.chi)) - names(SY)<-c(txt_chi_dot_squared, txt_df, "V.Cramer", V.sq) - if(any(choix2==txt_non_parametric_test)) SY[[txt_p_dot_val]]<-round(mon.chi$p.value,4) + names(SY)<-c(.dico[["txt_chi_dot_squared"]], .dico[["txt_df"]], "V.Cramer", V.sq) + if(any(choix2==.dico[["txt_non_parametric_test"]])) SY[[.dico[["txt_p_dot_val"]]]]<-round(mon.chi$p.value,4) try(fisher.test(tab),silent=T)->fisher if(class(fisher)!='try-error') SY$Fisher.Exact.Test=round(fisher$p.value,4) if(all(dim(tab)==2)){ mon.chi<-chisq.test(tab, B=n.boot, correct=T) AY<-data.frame(txt_chi_dot_squared=round(mon.chi$statistic,4),txt_df=mon.chi$parameter, Cramer(mon.chi),valeur.p=round(mon.chi$p.value,4) ,Fisher.Exact.Test="" ) - names(AY)<-c(txt_chi_dot_squared,txt_df,"V.Cramer", V.sq, txt_p_dot_val,"Fisher.Exact.Test") - if(any(choix2==txt_non_parametric_test)) AY[[txt_p_dot_val]]<-round(mon.chi$p.value,4) + names(AY)<-c(.dico[["txt_chi_dot_squared"]],.dico[["txt_df"]],"V.Cramer", V.sq, .dico[["txt_p_dot_val"]],"Fisher.Exact.Test") + if(any(choix2==.dico[["txt_non_parametric_test"]])) AY[[.dico[["txt_p_dot_val"]]]]<-round(mon.chi$p.value,4) SY<-rbind(SY, AY) - dimnames(SY)[[1]]<-c(txt_without_yates_correction, txt_with_yates_correction) - } else dimnames(SY)[[1]][1]<-c(txt_without_yates_correction) + dimnames(SY)[[1]]<-c(.dico[["txt_without_yates_correction"]], .dico[["txt_with_yates_correction"]]) + } else dimnames(SY)[[1]][1]<-c(.dico[["txt_without_yates_correction"]]) if(!is.null(n.boot) && n.boot>1){ - SY[[txt_p_value_with_monte_carlo]]<-chisq.test(tab, B=n.boot, simulate.p.value=T, correct=F)$p.value + SY[[.dico[["txt_p_value_with_monte_carlo"]]]]<-chisq.test(tab, B=n.boot, simulate.p.value=T, correct=F)$p.value } - Resultats[[txt_principal_analysis]]<-SY + Resultats[[.dico[["txt_principal_analysis"]]]]<-SY # Rapport de vraisemblance RV<-2* sum(mon.chi$observed[which(mon.chi$observed!=0)] * log(mon.chi$observed[which(mon.chi$observed!=0)]/mon.chi$expected[which(mon.chi$observed!=0)],base=exp(1))) @@ -236,58 +236,58 @@ chi <- q<-mon.chi$expected/sum(mon.chi$expected) RVES<-(-1/(log(min(q[which(p!=0)]), base=exp(1)))) *sum(p *log(p[which(p!=0)]/q[which(p!=0)], base=exp(1))) # ES from JOHNSTON et al. 2006 RV<-data.frame(txt_chi_dot_squared=RV, txt_df=mon.chi$parameter, txt_p_dot_val=round(PRV,4), txt_effect_size_dot=round(RVES,4)) - names(RV)<-c(txt_chi_dot_squared, txt_df, txt_p_dot_val, txt_effect_size_dot) - Resultats[[txt_likelihood_ratio_g_test]]<-RV + names(RV)<-c(.dico[["txt_chi_dot_squared"]], .dico[["txt_df"]], .dico[["txt_p_dot_val"]], .dico[["txt_effect_size_dot"]]) + Resultats[[.dico[["txt_likelihood_ratio_g_test"]]]]<-RV } # facteur bayesien - if(any(choix2==txt_bayesian_factors)) { + if(any(choix2==.dico[["txt_bayesian_factors"]])) { if(!is.null(fixedMargin) && fixedMargin==0) fixedMargin<-NULL bf<-contingencyTableBF(tab, sampleType = SampleType, fixedMargin = fixedMargin, priorConcentration=priorConcentration) bf<-ifelse(extractBF(bf, onlybf=T)>1000, ">1000", ifelse(extractBF(bf, onlybf=T)<.001, "<0.001",round(extractBF(bf, onlybf=T),4))) bf<-data.frame(txt_bayesian_factor=c(bf, ifelse(class(bf)=="character", "<0.001", round(1/bf,4)),SampleType)) - names(bf) <- c(txt_bayesian_factor) - dimnames(bf)[[1]]<-c(txt_supports_alternative, txt_supports_null, txt_type) - Resultats[[txt_bayesian_factor]]<-bf + names(bf) <- c(.dico[["txt_bayesian_factor"]]) + dimnames(bf)[[1]]<-c(.dico[["txt_supports_alternative"]], .dico[["txt_supports_null"]], .dico[["txt_type"]]) + Resultats[[.dico[["txt_bayesian_factor"]]]]<-bf } # Odd ratio as.matrix(tab)->tab if(all(dim(tab)>2) |any(mon.chi$observed==0)) { - desc_odd_ratio_cannot_be_computed->Resultats[[txt_odd_ratio]] + .dico[["desc_odd_ratio_cannot_be_computed"]]->Resultats[[.dico[["txt_odd_ratio"]]]] }else{ if(length(tab[1,])>2) tab<-apply(tab,1, rev) - Resultats[[txt_odd_ratio]]<- oddsratio.wald(x=tab,conf.level = 0.95,rev = c("neither"),correction = FALSE,verbose = FALSE)$measure + Resultats[[.dico[["txt_odd_ratio"]]]]<- oddsratio.wald(x=tab,conf.level = 0.95,rev = c("neither"),correction = FALSE,verbose = FALSE)$measure } - if(any(choix2 %in% c(txt_non_parametric_test,txt_robusts_tests_with_bootstraps))) { - if(is.null(SY[[txt_p_value_with_monte_carlo]])) p<-SY[[txt_p_dot_val]] else p<-SY[[txt_p_value_with_monte_carlo]] + if(any(choix2 %in% c(.dico[["txt_non_parametric_test"]],.dico[["txt_robusts_tests_with_bootstraps"]]))) { + if(is.null(SY[[.dico[["txt_p_value_with_monte_carlo"]]]])) p<-SY[[.dico[["txt_p_dot_val"]]]] else p<-SY[[.dico[["txt_p_value_with_monte_carlo"]]]] if(any(p<0.05)) { - round(mon.chi$residuals,3)->Resultats[[txt_residue]] - round((mon.chi$observed-mon.chi$expected)/(mon.chi$expected^0.5),3)->Resultats[[txt_residue_standardized]] - round(mon.chi$stdres,3)->Resultats[[txt_residue_standardized_adjusted]] - p.adjust(2*(1-pnorm(abs(Resultats[[txt_residue_standardized_adjusted]]))), method="holm")->valeur.p + round(mon.chi$residuals,3)->Resultats[[.dico[["txt_residue"]]]] + round((mon.chi$observed-mon.chi$expected)/(mon.chi$expected^0.5),3)->Resultats[[.dico[["txt_residue_standardized"]]]] + round(mon.chi$stdres,3)->Resultats[[.dico[["txt_residue_standardized_adjusted"]]]] + p.adjust(2*(1-pnorm(abs(Resultats[[.dico[["txt_residue_standardized_adjusted"]]]]))), method="holm")->valeur.p matrix(valeur.p, nrow=nrow(tab))->valeur.p dimnames(tab)->dimnames(valeur.p) - round(valeur.p,4)->Resultats[[txt_residues_significativity_holm_correction]] + round(valeur.p,4)->Resultats[[.dico[["txt_residues_significativity_holm_correction"]]]] } } - round(table.margins(prop.table(mon.chi$observed))*100,1)->Resultats[[txt_percentage_total]] - round(sweep(addmargins(mon.chi$observed, 1, list(list(All = sum, N = function(x) sum(x)^2/100))), 2,apply(mon.chi$observed, 2, sum)/100, "/"), 1)->Resultats[[txt_percentage_col]] - round(sweep(addmargins(mon.chi$observed, 2, list(list(All = sum, N = function(x) sum(x)^2/100))), 1,apply(mon.chi$observed, 1, sum)/100, "/"), 1)->Resultats[[txt_percentage_row]] + round(table.margins(prop.table(mon.chi$observed))*100,1)->Resultats[[.dico[["txt_percentage_total"]]]] + round(sweep(addmargins(mon.chi$observed, 1, list(list(All = sum, N = function(x) sum(x)^2/100))), 2,apply(mon.chi$observed, 2, sum)/100, "/"), 1)->Resultats[[.dico[["txt_percentage_col"]]]] + round(sweep(addmargins(mon.chi$observed, 2, list(list(All = sum, N = function(x) sum(x)^2/100))), 1,apply(mon.chi$observed, 1, sum)/100, "/"), 1)->Resultats[[.dico[["txt_percentage_row"]]]] } - if(choix==txt_mcnemar_test){ - if(any(choix2== txt_non_parametric_test)) { + if(choix==.dico[["txt_mcnemar_test"]]){ + if(any(choix2== .dico[["txt_non_parametric_test"]])) { MCN<-mcnemar.test(tab, correct=F) MCN<-data.frame(txt_chi_dot_squared=round(MCN$statistic,3), txt_df=MCN$parameter, txt_p_dot_val= round(MCN$p.value,4)) - names(MCN)<-c(txt_chi_dot_squared, txt_df, txt_p_dot_val) + names(MCN)<-c(.dico[["txt_chi_dot_squared"]], .dico[["txt_df"]], .dico[["txt_p_dot_val"]]) MCN2<-mcnemar.test(tab, correct=T) MCN2<-data.frame(txt_chi_dot_squared=round(MCN2$statistic,3), txt_df=MCN2$parameter, txt_p_dot_val= round(MCN2$p.value,4)) - names(MCN2)<-c(txt_chi_dot_squared, txt_df, txt_p_dot_val) + names(MCN2)<-c(.dico[["txt_chi_dot_squared"]], .dico[["txt_df"]], .dico[["txt_p_dot_val"]]) MCN<-rbind(MCN, MCN2) - dimnames(MCN)[[1]]<-c(txt_mcnemar_test_without_yates_correction, txt_mcnemar_test_with_continuity_correction ) - MCN->Resultats[[txt_mcnemar_test_with_yates_correction]] # test de McNemar + dimnames(MCN)[[1]]<-c(.dico[["txt_mcnemar_test_without_yates_correction"]], .dico[["txt_mcnemar_test_with_continuity_correction"]] ) + MCN->Resultats[[.dico[["txt_mcnemar_test_with_yates_correction"]]]] # test de McNemar } - if(any(choix2==txt_bayesian_factors)) { + if(any(choix2==.dico[["txt_bayesian_factors"]])) { bf<-proportionBF(y=tab[1,2], tab[1,2]+tab[2,1], p=0.5,rscale=rscale) erreur<-bf@numerator[[1]]@analysis$properror erreur<-ifelse(erreur<.0001, "<0.0001", erreur) @@ -295,15 +295,15 @@ chi <- samples =proportionBF(y = tab[1,2], N = tab[1,2]+tab[2,1], p = .5, posterior = TRUE, iterations = 10000) plot(samples[,"p"]) bf<-data.frame(txt_bayesian_factor=c(bf, ifelse(class(bf)=="character", "<0.001", round(1/bf,4)), erreur, rscale)) - names(bf)<-c(txt_bayesian_factor) - dimnames(bf)[[1]]<-c(txt_supports_alternative, txt_supports_null, txt_error, "rscale") - Resultats[[txt_bayesian_factors]]<-bf + names(bf)<-c(.dico[["txt_bayesian_factor"]]) + dimnames(bf)[[1]]<-c(.dico[["txt_supports_alternative"]], .dico[["txt_supports_null"]], .dico[["txt_error"]], "rscale") + Resultats[[.dico[["txt_bayesian_factors"]]]]<-bf } if( all(dimnames(tab)[[1]]==dimnames(tab)[[2]])) { - Resultats$Avertissement<- desc_cells_for_mcnemar + Resultats$Avertissement<- .dico[["desc_cells_for_mcnemar"]] } else { - Resultats$Avertissement<-ask_mcnemar_repeated_measure + Resultats$Avertissement<-.dico[["ask_mcnemar_repeated_measure"]] } } @@ -322,7 +322,7 @@ chi <- chi.in(X=X, Y=Y, Effectifs=Effectifs,p=p, choix=choix, data=data, info=info, n.boot=n.boot, SampleType=SampleType, FM=fixedMargin, choix2=choix2)->chi.options if(is.null(chi.options)) return(analyse()) - if(chi.options$analyse!=txt_chi_adjustement){ + if(chi.options$analyse!=.dico[["txt_chi_adjustement"]]){ try( windows(record=T), silent=T)->win if(class(win)=='try-error') quartz() } @@ -334,7 +334,7 @@ chi <- } else {X<-chi.options$Variables Y<-NULL} - if(length(X)>1) Resultats[[txt_alpha_warning]]<-paste(desc_alpha_increased_with_value_equals_to, 100*(1-0.95^length(X)), "%", sep=" ") + if(length(X)>1) Resultats[[.dico[["txt_alpha_warning"]]]]<-paste(.dico[["desc_alpha_increased_with_value_equals_to"]], 100*(1-0.95^length(X)), "%", sep=" ") for(i in 1:length(X)) { as.character(X[i])->Xi as.character(Y[i])->Yi @@ -342,11 +342,11 @@ chi <- Effectifs =chi.options$Effectifs, n.boot=chi.options$n.boot, choix2=chi.options$choix, SampleType=chi.options$SampleType[i], fixedMargin=chi.options$fixedMargin[i], rscale=rscale, priorConcentration =priorConcentration) Resultats[[i]]<-chi.results - if(chi.options$analyse==txt_chi_adjustement) nom<-paste(desc_chi_squared_adjustment_on_variable_x, X, sep =" ") - if(chi.options$analyse==txt_chi_independance) nom<-paste(txt_chi_results_between_var_x, Xi, - desc_and_variable_y, Yi,sep=" ") - if(chi.options$analyse==txt_mcnemar_test) nom<-paste(txt_mcnemar_results_between_var_x, Xi, - desc_and_variable_y, Yi,sep=" ") + if(chi.options$analyse==.dico[["txt_chi_adjustement"]]) nom<-paste(.dico[["desc_chi_squared_adjustment_on_variable_x"]], X, sep =" ") + if(chi.options$analyse==.dico[["txt_chi_independance"]]) nom<-paste(.dico[["txt_chi_results_between_var_x"]], Xi, + .dico[["desc_and_variable_y"]], Yi,sep=" ") + if(chi.options$analyse==.dico[["txt_mcnemar_test"]]) nom<-paste(.dico[["txt_mcnemar_results_between_var_x"]], Xi, + .dico[["desc_and_variable_y"]], Yi,sep=" ") names(Resultats)[i]<-nom } @@ -367,7 +367,7 @@ chi <- .add.result(Resultats=Resultats, name =paste(chi.options$analyse, Sys.time() )) - ref1(packages)->Resultats[[txt_references]] + ref1(packages)->Resultats[[.dico[["txt_references"]]]] ### Obtenir les Resultats if(html) try(ez.html(Resultats)) return(Resultats) diff --git a/R/choix.corr.R b/R/choix.corr.R index a7bbc7e..0924701 100644 --- a/R/choix.corr.R +++ b/R/choix.corr.R @@ -8,10 +8,10 @@ # \n la matrice de correlation permet de contrĂ´ler l'erreur de 1e espece et est adaptee pour un grand nombre de correlations # \n la comparaison de correlations permet de comparer 2 correlations dependantes ou independantes # \n Le choix + autre correlations + permet d'avoir les correlation tetrachoriques et polychoriques") -# dlgList(c(txt_detailed_corr_analysis, -# txt_correlations_matrix, -# txt_compare_two_correlations, -# txt_other_correlations), preselect=NULL, multiple = FALSE, title=ask_which_analysis)$res->choix +# dlgList(c(.dico[["txt_detailed_corr_analysis"]], +# .dico[["txt_correlations_matrix"]], +# .dico[["txt_compare_two_correlations"]], +# .dico[["txt_other_correlations"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_which_analysis"]])$res->choix # if(length(choix)==0) return(analyse()) # switch(choix, # txt_detailed_corr_analysis=corr.complet(html=html)->Resultats, @@ -26,16 +26,16 @@ choix.corr <- c('svDialogs')->packages if(any(lapply(packages, require, character.only=T))==FALSE) {install.packages(packages) require(packages)} - writeLines(desc_corr_detailed_analysis) - dlgList(c(txt_detailed_corr_analysis, - txt_correlations_matrix, - txt_compare_two_correlations, - txt_other_correlations), preselect=NULL, multiple = FALSE, title=ask_which_analysis)$res->choix + writeLines(.dico[["desc_corr_detailed_analysis"]]) + dlgList(c(.dico[["txt_detailed_corr_analysis"]], + .dico[["txt_correlations_matrix"]], + .dico[["txt_compare_two_correlations"]], + .dico[["txt_other_correlations"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_which_analysis"]])$res->choix if(length(choix)==0) return(analyse()) - if(choix==txt_detailed_corr_analysis) corr.complet(html=html)->Resultats - if(choix==txt_correlations_matrix) corr.matrice(html=html)->Resultats - if(choix==txt_compare_two_correlations) comp.corr(html=html)->Resultats - if(choix==txt_other_correlations) tetrapoly(html=html)->Resultats + if(choix==.dico[["txt_detailed_corr_analysis"]]) corr.complet(html=html)->Resultats + if(choix==.dico[["txt_correlations_matrix"]]) corr.matrice(html=html)->Resultats + if(choix==.dico[["txt_compare_two_correlations"]]) comp.corr(html=html)->Resultats + if(choix==.dico[["txt_other_correlations"]]) tetrapoly(html=html)->Resultats return(Resultats) } diff --git a/R/choix.data.R b/R/choix.data.R index 5010586..998dc79 100644 --- a/R/choix.data.R +++ b/R/choix.data.R @@ -7,14 +7,14 @@ choix.data <- list()->Resultats Filter( function(x) 'data.frame' %in% class( get(x) ), ls(envir=.GlobalEnv))->nom1 if(length(nom1)==0) { - writeLines(desc_no_data_in_R_memory) + writeLines(.dico[["desc_no_data_in_R_memory"]]) import() choix.data(data=NULL,info=T, nom=nom)->Resultats return(Resultats)} if(any(!is.null(data)) && data%in% nom1) data->nom1 if(length(nom1)==1) data<-get(nom1) else{ - if(info=="TRUE") writeLines(ask_chose_database) - nom1 <- dlgList(nom1, multiple = FALSE, title=ask_data)$res + if(info=="TRUE") writeLines(.dico[["ask_chose_database"]]) + nom1 <- dlgList(nom1, multiple = FALSE, title=.dico[["ask_data"]])$res if(length(nom1)==0) {nom1<-NULL data<-NULL} if(!is.null(nom1)) data<-get(nom1) diff --git a/R/choix.reg.R b/R/choix.reg.R index 63c28aa..d7ad627 100644 --- a/R/choix.reg.R +++ b/R/choix.reg.R @@ -3,13 +3,13 @@ choix.reg <- try(library(svDialogs), silent=T)->test2 if(class(test2)== 'try-error') return(ez.install()) - dlgList(c(txt_regressions, - txt_mediation_effect, - txt_logistic_regressions), preselect=txt_regressions, multiple = FALSE, title=ask_which_regression_type)$res->choix + dlgList(c(.dico[["txt_regressions"]], + .dico[["txt_mediation_effect"]], + .dico[["txt_logistic_regressions"]]), preselect=.dico[["txt_regressions"]], multiple = FALSE, title=.dico[["ask_which_regression_type"]])$res->choix if(length(choix)==0) return(analyse()) - if(choix==txt_regressions) regressions(html=html)->Resultats - if(choix==txt_mediation_effect) ez.mediation(html=html)->Resultats - if(choix==txt_logistic_regressions) regressions.log(html=html)->Resultats + if(choix==.dico[["txt_regressions"]]) regressions(html=html)->Resultats + if(choix==.dico[["txt_mediation_effect"]]) ez.mediation(html=html)->Resultats + if(choix==.dico[["txt_logistic_regressions"]]) regressions.log(html=html)->Resultats return(Resultats) } diff --git a/R/comp.corr.R b/R/comp.corr.R index 1395004..d371e16 100644 --- a/R/comp.corr.R +++ b/R/comp.corr.R @@ -16,48 +16,48 @@ comp.corr <- all(c(n,n2)>0) & all(c(n,n2)%%1==0)) { paired.r(xy=xy, xz=xz, yz=yz, n=n, n2=n2,twotailed=twotailed)->r } else { - msgBox(desc_corr_values_must_be_between_min_1_and_1) + msgBox(.dico[["desc_corr_values_must_be_between_min_1_and_1"]]) } if(exists("r") && length(r$p)!=0 && !is.na(r$p)) { - Resultats[[txt_comparison_of_two_correlations]]<-r + Resultats[[.dico[["txt_comparison_of_two_correlations"]]]]<-r Resultats$call<-paste("comp.corr(xy=", xy, ",xz=", xz, ",yz=",yz, ",n=", n, ",n2=", n2, ",twotailed=",twotailed, ")") data1<-data.frame() - .add.history(data=data1, command=Resultats$call, nom=paste(txt_comparisons_XY, xy, txt_and_YZ, yz )) - .add.result(Resultats=Resultats, name =paste(txt_correlations_comparison, Sys.time() )) - Resultats[[txt_references]]<-ref1(packages) + .add.history(data=data1, command=Resultats$call, nom=paste(.dico[["txt_comparisons_XY"]], xy, .dico[["txt_and_YZ"]], yz )) + .add.result(Resultats=Resultats, name =paste(.dico[["txt_correlations_comparison"]], Sys.time() )) + Resultats[[.dico[["txt_references"]]]]<-ref1(packages) return(Resultats) } else{ - type<- dlgList(c(txt_apparied_correlations, txt_independant_correlations), preselect=FALSE, multiple = FALSE, title=txt_compare_two_correlations)$res + type<- dlgList(c(.dico[["txt_apparied_correlations"]], .dico[["txt_independant_correlations"]]), preselect=FALSE, multiple = FALSE, title=.dico[["txt_compare_two_correlations"]])$res if(length(type)==0) return(choix.corr()) - if(type==txt_independant_correlations) { - name <- c(txt_XY_correlation, txt_N_of_XY_corr, txt_XZ_correlation, txt_N_of_XZ_corr) + if(type==.dico[["txt_independant_correlations"]]) { + name <- c(.dico[["txt_XY_correlation"]], .dico[["txt_N_of_XY_corr"]], .dico[["txt_XZ_correlation"]], .dico[["txt_N_of_XZ_corr"]]) vals <- c(0, 100, 0, 100) Form <- setNames(as.list(vals), name) }else{ - name <- c(txt_XY_correlation, txt_XZ_correlation, txt_YZ_correlation, txt_sample_size) + name <- c(.dico[["txt_XY_correlation"]], .dico[["txt_XZ_correlation"]], .dico[["txt_YZ_correlation"]], .dico[["txt_sample_size"]]) vals <- c(0, 0, 0, 100) Form <- setNames(as.list(vals), name) } # For Unix users: The native form dialog box (dlgForm) is available only if you install 'yad' # moreover, dlgForm is working as a dialog box only on Linux (see ?dlgForm) - value<-dlgForm(Form, ask_enter_different_values)$res + value<-dlgForm(Form, .dico[["ask_enter_different_values"]])$res if(any(is.na(value))) { - msgBox(desc_some_values_are_not_numeric) + msgBox(.dico[["desc_some_values_are_not_numeric"]]) comp.corr(xy=NULL, xz=NULL, yz=NULL, n=NULL, n2=NULL,twotailed=TRUE)->Resultats return(Resultats) } - xy<-value[[txt_XY_correlation]] - xz<-value[[txt_XZ_correlation]] - yz<-value[[txt_YZ_correlation]] - if(type==txt_apparied_correlations) { - n<-value[[txt_sample_size]] + xy<-value[[.dico[["txt_XY_correlation"]]]] + xz<-value[[.dico[["txt_XZ_correlation"]]]] + yz<-value[[.dico[["txt_YZ_correlation"]]]] + if(type==.dico[["txt_apparied_correlations"]]) { + n<-value[[.dico[["txt_sample_size"]]]] } else { - n<-value[[txt_N_of_XY_corr]] - n2<-value[[txt_N_of_XZ_corr]] + n<-value[[.dico[["txt_N_of_XY_corr"]]]] + n2<-value[[.dico[["txt_N_of_XZ_corr"]]]] } comp.corr(xy=xy, xz=xz, yz=yz, n=n, n2=n2,twotailed=twotailed)->Resultats if(html) html<-FALSE diff --git a/R/contrastes.ez.R b/R/contrastes.ez.R index fc7266c..47527ad 100644 --- a/R/contrastes.ez.R +++ b/R/contrastes.ez.R @@ -1,14 +1,14 @@ contrastes.ez <- function(longdata, inter=NULL, intra=NULL){ Resultats<-list() - writeLines(desc_all_contrasts_description) - type.cont<- dlgList(c(txt_apriori, txt_comparison_two_by_two, txt_none), preselect=txt_apriori,multiple = FALSE, title=ask_which_contrasts)$res + writeLines(.dico[["desc_all_contrasts_description"]]) + type.cont<- dlgList(c(.dico[["txt_apriori"]], .dico[["txt_comparison_two_by_two"]], .dico[["txt_none"]]), preselect=.dico[["txt_apriori"]],multiple = FALSE, title=.dico[["ask_which_contrasts"]])$res if(length(type.cont)==0) return(NULL) Resultats$type.cont<-type.cont c(inter, unlist(intra))->interintra - if(type.cont==txt_apriori) { + if(type.cont==.dico[["txt_apriori"]]) { contrastes<-list() - writeLines(desc_you_can_choose_contrasts_you_want) + writeLines(.dico[["desc_you_can_choose_contrasts_you_want"]]) cont.exemple<-list() contr.helmert(3)->cont.exemple$Orthogonaux apply(contr.helmert(3), 2, rev)->cont.exemple$Orthogonaux.inverses @@ -18,33 +18,33 @@ contrastes.ez <- for (i in 1:length(interintra)){ if(i>1) { - type.cont2<- dlgList(c(txt_orthogonals, txt_orthogonals_inverse, txt_polynomials,txt_compare_to_baseline, ask_specify_contrasts), - preselect=c(txt_orthogonals), multiple = FALSE, title=paste(ask_which_contrasts_for_variable,names(longdata[interintra])[i],"?"))$res} else { - type.cont2<- dlgList(c(txt_orthogonals, txt_orthogonals_inverse, txt_polynomials,txt_compare_to_baseline, - ask_specify_contrasts),preselect=c(txt_orthogonals), multiple = FALSE, title=paste(ask_which_contrasts_for_variable,names(longdata[interintra])[i],"?"))$res + type.cont2<- dlgList(c(.dico[["txt_orthogonals"]], .dico[["txt_orthogonals_inverse"]], .dico[["txt_polynomials"]],.dico[["txt_compare_to_baseline"]], .dico[["ask_specify_contrasts"]]), + preselect=c(.dico[["txt_orthogonals"]]), multiple = FALSE, title=paste(.dico[["ask_which_contrasts_for_variable"]],names(longdata[interintra])[i],"?"))$res} else { + type.cont2<- dlgList(c(.dico[["txt_orthogonals"]], .dico[["txt_orthogonals_inverse"]], .dico[["txt_polynomials"]],.dico[["txt_compare_to_baseline"]], + .dico[["ask_specify_contrasts"]]),preselect=c(.dico[["txt_orthogonals"]]), multiple = FALSE, title=paste(.dico[["ask_which_contrasts_for_variable"]],names(longdata[interintra])[i],"?"))$res } if(length(type.cont2)==0) return(contrastes.ez()) - if(type.cont2==txt_orthogonals) contr.helmert(nlevels(longdata[,interintra[i]]))->contrastes[[i]] - if(type.cont2==txt_orthogonals_inverse) apply(contr.helmert(nlevels(longdata[,interintra[i]])), 2, rev)->contrastes[[i]] - if(type.cont2==txt_polynomials) contr.poly(nlevels(longdata[,interintra[i]]))->contrastes[[i]] - if(type.cont2==txt_compare_to_baseline) { + if(type.cont2==.dico[["txt_orthogonals"]]) contr.helmert(nlevels(longdata[,interintra[i]]))->contrastes[[i]] + if(type.cont2==.dico[["txt_orthogonals_inverse"]]) apply(contr.helmert(nlevels(longdata[,interintra[i]])), 2, rev)->contrastes[[i]] + if(type.cont2==.dico[["txt_polynomials"]]) contr.poly(nlevels(longdata[,interintra[i]]))->contrastes[[i]] + if(type.cont2==.dico[["txt_compare_to_baseline"]]) { base<- dlgList(levels(longdata[, interintra[i]]), preselect=levels(longdata[,interintra[i]])[1], - multiple = FALSE, title=ask_baseline)$res + multiple = FALSE, title=.dico[["ask_baseline"]])$res which(levels(longdata[, interintra[i]])==base)->base contr.treatment(levels(longdata[, interintra[i]]), base = base, contrasts = TRUE, sparse = FALSE)->contrastes[[i]] } - if(type.cont2==ask_specify_contrasts){ + if(type.cont2==.dico[["ask_specify_contrasts"]]){ ortho<-FALSE while(ortho!=TRUE){ matrix(rep(0,times=nlevels(longdata[,interintra[i]])*(nlevels(longdata[,interintra[i]])-1)), nrow=nlevels(longdata[,interintra[i]]))->contrastes3 dimnames(contrastes3)[[1]]<-levels(longdata[,interintra[i]]) - dimnames(contrastes3)[[2]]<-paste(txt_contrast, 1:(nlevels(longdata[,interintra[i]])-1), sep=".") + dimnames(contrastes3)[[2]]<-paste(.dico[["txt_contrast"]], 1:(nlevels(longdata[,interintra[i]])-1), sep=".") fix(contrastes3)->contrastes3 if(any(colSums(contrastes3)!=0)|(nlevels(longdata[,interintra[i]])>2 & max(rle(c(contrastes3))$lengths)>2*(nlevels(longdata[,interintra[i]])-2))) ortho<-FALSE else { test.out<-rep(1, length(contrastes3[,1])) for(j in 1:length(contrastes3[1,])) {contrastes3[,j]*test.out->test.out} if(sum(test.out)==0) ortho<-TRUE else ortho<-FALSE} - if(ortho==FALSE) {dlgMessage(ask_contrast_must_respect_ortho, "yesno")$res->cont + if(ortho==FALSE) {dlgMessage(.dico[["ask_contrast_must_respect_ortho"]], "yesno")$res->cont if(cont=="no") return(contrastes.ez(longdata=longdata, inter=inter, intra=intra )) } contrastes[[i]]<-contrastes3 @@ -52,16 +52,16 @@ contrastes.ez <- } - dimnames(contrastes[[i]])[[2]]<-paste(txt_contrast, 1:(nlevels(longdata[,interintra[i]])-1), sep=".") + dimnames(contrastes[[i]])[[2]]<-paste(.dico[["txt_contrast"]], 1:(nlevels(longdata[,interintra[i]])-1), sep=".") } names(contrastes)<-interintra Resultats$contrastes<-contrastes } - if(type.cont== txt_comparison_two_by_two){ + if(type.cont== .dico[["txt_comparison_two_by_two"]]){ list()->p.adjust - writeLines(ask_which_correction) - dlgList(c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none"), preselect="holm", multiple = FALSE, title=ask_correction_type)$res->p.adjust + writeLines(.dico[["ask_which_correction"]]) + dlgList(c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none"), preselect="holm", multiple = FALSE, title=.dico[["ask_correction_type"]])$res->p.adjust if(length(p.adjust)==0) return(contrastes.ez()) Resultats$p.adjust<-p.adjust } diff --git a/R/corr.complet.R b/R/corr.complet.R index b7d6042..0632057 100644 --- a/R/corr.complet.R +++ b/R/corr.complet.R @@ -1,5 +1,5 @@ corr.complet <- - function(X=NULL, Y=NULL, Z=NULL,data=NULL, group=NULL, param=c(txt_param_test, txt_non_param_test,txt_robusts_tests_with_bootstraps, txt_bayesian_factors), + function(X=NULL, Y=NULL, Z=NULL,data=NULL, group=NULL, param=c(.dico[["txt_param_test"]], .dico[["txt_non_param_test"]],.dico[["txt_robusts_tests_with_bootstraps"]], .dico[["txt_bayesian_factors"]]), save=F, outlier=c("complete", "id", "removed"), z=NULL, info=T, n.boot=NULL, rscale=0.353, html=T) {options (warn=-1) @@ -8,16 +8,16 @@ corr.complet <- Resultats<-list() if(!is.null(X) & !is.null(data) & !is.null(Y)) { dial<-F - if(is.null(Z)) choix<-txt_correlations - else choix<-txt_partial_and_semi_correlations + if(is.null(Z)) choix<-.dico[["txt_correlations"]] + else choix<-.dico[["txt_partial_and_semi_correlations"]] } else { dial<-T choix<-NULL } if(is.null(choix)){ - if(info) writeLines(ask_type_correlation) - choix<-dlgList(c(txt_correlations, txt_partial_and_semi_correlations), preselect=txt_correlations, multiple = FALSE, title=ask_simple_or_partial_corr)$res + if(info) writeLines(.dico[["ask_type_correlation"]]) + choix<-dlgList(c(.dico[["txt_correlations"]], .dico[["txt_partial_and_semi_correlations"]]), preselect=.dico[["txt_correlations"]], multiple = FALSE, title=.dico[["ask_simple_or_partial_corr"]])$res if(length(choix)==0) return(NULL) } data<-choix.data(data=data, info=info, nom=T) @@ -25,10 +25,10 @@ corr.complet <- nom<-data[[1]] data<-data[[2]] - msg3<-ask_chose_variable_x_axis - msg4<-ask_chose_variable_y_axis + msg3<-.dico[["ask_chose_variable_x_axis"]] + msg4<-.dico[["ask_chose_variable_y_axis"]] - X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=txt_x_axis_variables, out=NULL) + X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=.dico[["txt_x_axis_variables"]], out=NULL) if(is.null(X)) { corr.complet.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL,n.boot=NULL, rscale=0.707)->Resultats return(Resultats) @@ -36,16 +36,16 @@ corr.complet <- data<-X$data X1<-X$X - Y<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg4, multiple=T, title=txt_y_axis_variables, out=X1) + Y<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg4, multiple=T, title=.dico[["txt_y_axis_variables"]], out=X1) if(is.null(Y)) { corr.complet.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL,n.boot=NULL, rscale=0.707)->Resultats return(Resultats) } data<-Y$data Y<-Y$X - if(choix==txt_partial_and_semi_correlations) { - msg6<-ask_control_variables - Z<-.var.type(X=Z, info=info, data=data, type="numeric", check.prod=F, message=msg6, multiple=T, title=txt_control_variables, out=c(X1,Y)) + if(choix==.dico[["txt_partial_and_semi_correlations"]]) { + msg6<-.dico[["ask_control_variables"]] + Z<-.var.type(X=Z, info=info, data=data, type="numeric", check.prod=F, message=msg6, multiple=T, title=.dico[["txt_control_variables"]], out=c(X1,Y)) if(is.null(Z)) { corr.complet.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL,n.boot=NULL, rscale=0.707)->Resultats return(Resultats) @@ -55,16 +55,16 @@ corr.complet <- } if(dial) { - if(info==TRUE) writeLines(desc_corr_group_analysis_spec) - dlgList(c(txt_yes, txt_no), preselect=txt_no, multiple = FALSE, title=ask_analysis_by_group)$res->par.groupe + if(info==TRUE) writeLines(.dico[["desc_corr_group_analysis_spec"]]) + dlgList(c(.dico[["txt_yes"]], .dico[["txt_no"]]), preselect=.dico[["txt_no"]], multiple = FALSE, title=.dico[["ask_analysis_by_group"]])$res->par.groupe if(length(par.groupe)==0) { corr.complet.in(X=NULL, Y=NULL, data=NULL,param=NULL, outlier=NULL, save=NULL, info=T, group=NULL,n.boot=NULL, rscale=0.707)->Resultats return(Resultats) } - msg5<-ask_chose_ranking_categorial_factor - if(par.groupe==txt_yes) { - group<-.var.type(X=group, info=info, data=data, type="factor", check.prod=F, message=msg5, multiple=TRUE, title=txt_variables, out=c(X1,Y,Z)) + msg5<-.dico[["ask_chose_ranking_categorial_factor"]] + if(par.groupe==.dico[["txt_yes"]]) { + group<-.var.type(X=group, info=info, data=data, type="factor", check.prod=F, message=msg5, multiple=TRUE, title=.dico[["txt_variables"]], out=c(X1,Y,Z)) if(length(group)==0) { corr.complet.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL,n.boot=NULL, rscale=0.707)->Resultats return(Resultats) @@ -72,15 +72,15 @@ corr.complet <- data<-group$data group<-group$X if(any(ftable(data[,group])<3)) { - msgBox(desc_need_at_least_three_observation_by_combination) + msgBox(.dico[["desc_need_at_least_three_observation_by_combination"]]) corr.complet.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.707)->Resultats return(Resultats) } } } - msg.options1<-desc_param_is_BP - msg.options2<- desc_non_param_are_rho_and_tau + msg.options1<-.dico[["desc_param_is_BP"]] + msg.options2<- .dico[["desc_non_param_are_rho_and_tau"]] options<-.ez.options(options=c('choix',"outlier"), n.boot=n.boot,param=T, non.param=T, robust=T, Bayes=T, msg.options1=msg.options1, msg.options2=msg.options2, info=info, dial=dial, choix=param,sauvegarde=save, outlier=outlier, rscale=rscale) if(is.null(options)) { @@ -106,18 +106,18 @@ corr.complet <- boot_BPSP<-function(data,i)cor(data[ , X][i], data[ , Y1][i], use="complete.obs", method="pearson") boot_SpearmanSP<-function(data,i)cor(data[ ,X][i], data[ , Y1][i], use="complete.obs", method="spearman") list()->Resultats - Resultats[[txt_descriptive_statistics]]<-.stat.desc.out(X=c(X,Y,Z), groupes=NULL, data=data, tr=.1, type=3, plot=T) + Resultats[[.dico[["txt_descriptive_statistics"]]]]<-.stat.desc.out(X=c(X,Y,Z), groupes=NULL, data=data, tr=.1, type=3, plot=T) if(!is.null(group)) { - Resultats[[txt_descriptive_statistics_by_group]]<-.stat.desc.out(X=c(X,Y,Z), groupes=group, data=data, tr=.1, type=3, plot=T) + Resultats[[.dico[["txt_descriptive_statistics_by_group"]]]]<-.stat.desc.out(X=c(X,Y,Z), groupes=group, data=data, tr=.1, type=3, plot=T) } - if(choix== txt_correlations) { - title<-txt_BP_correlation - title2<-txt_rho + if(choix== .dico[["txt_correlations"]]) { + title<-.dico[["txt_BP_correlation"]] + title2<-.dico[["txt_rho"]] X1<-X Y1<-Y} else { - title<-txt_partial_corr_BP - title2<-txt_partial_rho + title<-.dico[["txt_partial_corr_BP"]] + title2<-.dico[["txt_partial_rho"]] modele1<-as.formula(paste0(X,"~",Z[1])) modele2<-as.formula(paste0(Y,"~", Z[1])) if(length(Z)>1) for(i in 2:length(Z)){ @@ -135,8 +135,8 @@ corr.complet <- lm.r<-lm(modele,na.action=na.exclude,data=data) resid(lm.r)->data$'residus' # recuperation du residu sur le modele lineaire - if(any(param=="Bayes") | any(param==txt_bayesian_factors) | any(param=="param") | any(param==txt_param_tests)) { - Resultats[[txt_normality_tests]]<-.normalite(data=data, X='residus', Y=NULL) + if(any(param=="Bayes") | any(param==.dico[["txt_bayesian_factors"]]) | any(param=="param") | any(param==.dico[["txt_param_tests"]])) { + Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=data, X='residus', Y=NULL) graphiques<-list() p<-ggplot(data) p<-p+ eval(parse(text=paste0("aes(x=", X,", y=", Y,")"))) + geom_point() @@ -159,26 +159,26 @@ corr.complet <- } graphiques[[2]]<-p1 } - Resultats[[txt_param_tests]][[txt_graphics]]<-graphiques + Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_graphics"]]]]<-graphiques } - if(any(param=="param") | any(param==txt_param_tests)){ - if(choix!=txt_correlations) { + if(any(param=="param") | any(param==.dico[["txt_param_tests"]])){ + if(choix!=.dico[["txt_correlations"]]) { cor.part<-rbind( pcor.test(data[,X], data[ ,Y], data[ , Z], method = "pearson")[1:3], spcor.test(data[,X], data[ ,Y], data[ ,Z], method = "pearson")[1:3]) - cor.part$estimate^2->cor.part[[txt_r_dot_square]] + cor.part$estimate^2->cor.part[[.dico[["txt_r_dot_square"]]]] round(cor.part, 4)->cor.part cor.part$ddl<-(pcor.test(data[,X], data[ ,Y], data[ , Z], method = "pearson")$n-2-length(Z)) - dimnames(cor.part)<-list(c(txt_partial_corr_BP,txt_semi_BP), c("Correlation", txt_p_dot_val, "test.t", txt_r_dot_square,txt_df)) - Resultats[[txt_partial_semi_BP]]<-cor.part + dimnames(cor.part)<-list(c(.dico[["txt_partial_corr_BP"]],.dico[["txt_semi_BP"]]), c("Correlation", .dico[["txt_p_dot_val"]], "test.t", .dico[["txt_r_dot_square"]],.dico[["txt_df"]])) + Resultats[[.dico[["txt_partial_semi_BP"]]]]<-cor.part } else { BP<-cor.test(data[, X1], data[ ,Y1], method = "pearson") - Resultats[[txt_param_tests]][[txt_BP_correlation]]<-round(data.frame("r"=BP$estimate,txt_r_dot_two=BP$estimate^2, txt_ci_inferior_limit=BP$conf.int[1],txt_ci_superior_limit=BP$conf.int[2], "t"=BP$statistic, txt_df=BP$parameter, txt_p_dot_val=BP$p.value),4) - names(Resultats[[txt_param_tests]][[txt_BP_correlation]])<-c("r",txt_r_dot_two, txt_ci_inferior_limit,txt_ci_superior_limit, "t", txt_df, txt_p_dot_val) + Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_BP_correlation"]]]]<-round(data.frame("r"=BP$estimate,txt_r_dot_two=BP$estimate^2, txt_ci_inferior_limit=BP$conf.int[1],txt_ci_superior_limit=BP$conf.int[2], "t"=BP$statistic, txt_df=BP$parameter, txt_p_dot_val=BP$p.value),4) + names(Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_BP_correlation"]]]])<-c("r",.dico[["txt_r_dot_two"]], .dico[["txt_ci_inferior_limit"]],.dico[["txt_ci_superior_limit"]], "t", .dico[["txt_df"]], .dico[["txt_p_dot_val"]]) } if(!is.null(group)){ - if(choix==txt_correlations) { + if(choix==.dico[["txt_correlations"]]) { corr.g<-function(X2){ return(data.frame(BP.r= cor.test(X2[, X1], X2[ ,Y1], method = "pearson")$estimate, BP.ddl= cor.test(X2[, X1], X2[ ,Y1], method = "pearson")$parameter, @@ -200,14 +200,14 @@ corr.complet <- dimnames(BPgroup)[[2]]<- c("BP.r", "BP.ddl", "BP.t", "BP.p") BPgroup<-data.frame(gr.l,BPgroup ) - if(choix!=txt_correlations) { - Resultats[[txt_param_tests]][[txt_partial_corr_BP_by_group]]<-BPgroup + if(choix!=.dico[["txt_correlations"]]) { + Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_partial_corr_BP_by_group"]]]]<-BPgroup } else { - Resultats[[txt_BP_correlation_by_group]]<-BPgroup + Resultats[[.dico[["txt_BP_correlation_by_group"]]]]<-BPgroup } } } - if(any(param=="non param")| any(param==txt_non_parametric_test)){ + if(any(param=="non param")| any(param==.dico[["txt_non_parametric_test"]])){ graphiques<-list() p<-ggplot(data) @@ -232,31 +232,31 @@ corr.complet <- } graphiques[[2]]<-p1 } - Resultats[[txt_non_parametric_test]][[txt_graphics]]<-graphiques + Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_graphics"]]]]<-graphiques - if(choix!=txt_correlations) { + if(choix!=.dico[["txt_correlations"]]) { spear<-rbind( pcor.test(data[,X], data[ ,Y], data[ , Z], method = "spearman")[1:3],spcor.test(data[,X], data[ ,Y], data[ ,Z], method = "spearman")[1:3]) tau<-rbind(pcor.test(data[,X], data[ ,Y], data[ , Z], method = "kendall")[1:3],spcor.test(data[,X], data[ ,Y], data[ , Z], method = "kendall")[1:3]) spear<-round(spear,4) tau<-round(tau,4) #spear$estimate^2->spear$r.carre - spear$estimate^2->spear[[txt_r_dot_square]] + spear$estimate^2->spear[[.dico[["txt_r_dot_square"]]]] round(spear, 4)->cor.part - dimnames(spear)<-list(c(txt_partial_rho,txt_semi_partial_rho), c("rho", txt_p_dot_val, "t", txt_r_dot_square)) - Resultats[[txt_non_parametric_test]][[txt_partial_semi_partial_rho]]<-spear + dimnames(spear)<-list(c(.dico[["txt_partial_rho"]],.dico[["txt_semi_partial_rho"]]), c("rho", .dico[["txt_p_dot_val"]], "t", .dico[["txt_r_dot_square"]])) + Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_partial_semi_partial_rho"]]]]<-spear tau<-round(tau,4) - dimnames(tau)<-list(c(txt_kendall_partial_tau,txt_kendall_semipartial_tau), c("tau", txt_p_dot_val, "z")) - Resultats[[txt_non_parametric_test]][[txt_kendall_partial_semipartial_tau]]<-tau + dimnames(tau)<-list(c(.dico[["txt_kendall_partial_tau"]],.dico[["txt_kendall_semipartial_tau"]]), c("tau", .dico[["txt_p_dot_val"]], "z")) + Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_kendall_partial_semipartial_tau"]]]]<-tau } else { Spear<-cor.test(data[,X1], data[ ,Y1], method = "spearman", exact=T, continuity=T) cor.test(data[,X1], data[ ,Y1], method = "kendall")->Kendall - Resultats[[txt_non_parametric_test]][[txt_rho]]<-round(data.frame("rho"=Spear$estimate,txt_rho_dot_square=Spear$estimate^2,"S"=Spear$statistic,txt_p_dot_val=Spear$p.value),4) - names(Resultats[[txt_non_parametric_test]][[txt_rho]])<-c("rho",txt_rho_dot_square,"S",txt_p_dot_val) - round(data.frame("tau"=Kendall$estimate,"z"=Kendall$statistic,txt_p_dot_val=Kendall$p.value),4)->Resultats[[txt_non_parametric_test]][[txt_kendall_tau]] - c("tau","z",txt_p_dot_val)->names(Resultats[[txt_non_parametric_test]][[txt_kendall_tau]]) + Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_rho"]]]]<-round(data.frame("rho"=Spear$estimate,txt_rho_dot_square=Spear$estimate^2,"S"=Spear$statistic,txt_p_dot_val=Spear$p.value),4) + names(Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_rho"]]]])<-c("rho",.dico[["txt_rho_dot_square"]],"S",.dico[["txt_p_dot_val"]]) + round(data.frame("tau"=Kendall$estimate,"z"=Kendall$statistic,txt_p_dot_val=Kendall$p.value),4)->Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_kendall_tau"]]]] + c("tau","z",.dico[["txt_p_dot_val"]])->names(Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_kendall_tau"]]]]) } if(!is.null(group)){ - if(choix==txt_correlations) {corr.g<-function(X2){ return(data.frame(Sp.r= cor.test(X2[, X1], X2[ ,Y1], method = "spearman")$estimate, + if(choix==.dico[["txt_correlations"]]) {corr.g<-function(X2){ return(data.frame(Sp.r= cor.test(X2[, X1], X2[ ,Y1], method = "spearman")$estimate, Sp.p= cor.test(X2[, X1], X2[ ,Y1], method = "spearman")$p.value, Kendall.r= cor.test(X2[, X1], X2[ ,Y1], method = "kendall")$estimate, Kendall.p= cor.test(X2[, X1], X2[ ,Y1], method = "kendall")$p.value))} @@ -278,70 +278,70 @@ corr.complet <- gr.l<-data.frame(gr.l) gr.l<-expand.grid(gr.l) } - if(choix!=txt_correlations){ - dimnames(BPgroup)[[2]]<- c("Spearman.rho", txt_spearman_df, "Spearman.t", "Spearman.p") + if(choix!=.dico[["txt_correlations"]]){ + dimnames(BPgroup)[[2]]<- c("Spearman.rho", .dico[["txt_spearman_df"]], "Spearman.t", "Spearman.p") BPgroup<-data.frame(gr.l,BPgroup ) - Resultats[[txt_non_parametric_test]][[txt_partial_spearman_by_group]]<-BPgroup + Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_partial_spearman_by_group"]]]]<-BPgroup } else { dimnames(BPgroup)[[2]]<- c( "Spearman.r", "Spearman.p", "Tau.Kendall.r", "Tau.Kendall.p") BPgroup<-data.frame(gr.l,BPgroup ) - Resultats[[txt_non_parametric_test]][[txt_spearman_kendall_corr_by_group]]<-BPgroup + Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_spearman_kendall_corr_by_group"]]]]<-BPgroup } } } - if(any(param=="robust"| any(param==txt_robusts_tests_with_bootstraps))) { + if(any(param=="robust"| any(param==.dico[["txt_robusts_tests_with_bootstraps"]]))) { boot_BP_results<-boot(data, boot_BP, n.boot) - if(!is.null(Resultats[[txt_param_tests]][[txt_BP_correlation]])) { - try(Resultats[[txt_param_tests]][[txt_BP_correlation]][[txt_bca_inferior_limit]]<-round( boot.ci(boot_BP_results)$bca[,4],4), silent=T) - try(Resultats[[txt_param_tests]][[txt_BP_correlation]][[txt_bca_superior_limit]]<-round( boot.ci(boot_BP_results)$bca[,5],4),silent=T) - } else if(!is.null(Resultats[[txt_param_tests]][[txt_partial_semi_BP]])) { + if(!is.null(Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_BP_correlation"]]]])) { + try(Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_BP_correlation"]]]][[.dico[["txt_bca_inferior_limit"]]]]<-round( boot.ci(boot_BP_results)$bca[,4],4), silent=T) + try(Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_BP_correlation"]]]][[.dico[["txt_bca_superior_limit"]]]]<-round( boot.ci(boot_BP_results)$bca[,5],4),silent=T) + } else if(!is.null(Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_partial_semi_BP"]]]])) { boot_BPSP_results<-boot(data, boot_BPSP, n.boot) - try(Resultats[[txt_param_tests]][[txt_partial_semi_BP]][[txt_bca_inferior_limit]]<-round( c(boot.ci(boot_BP_results)$bca[,4], boot.ci(boot_BPSP_results)$bca[,4]),4),silent=T) - try(Resultats[[txt_param_tests]][[txt_partial_semi_BP]][[txt_bca_superior_limit]]<-round( c(boot.ci(boot_BP_results)$bca[,5], boot.ci(boot_BPSP_results)$bca[,5]) ,4), silent=T) - #} else try(Resultats[[txt_robust_analysis]][[txt_bootstrap_on_BP]]<-round(data.frame(txt_bca_inferior_limit= boot.ci(boot_BP_results)$bca[,4], txt_bca_superior_limit=boot.ci(boot_BP_results)$bca[,5] ), 4),silent=T) + try(Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_partial_semi_BP"]]]][[.dico[["txt_bca_inferior_limit"]]]]<-round( c(boot.ci(boot_BP_results)$bca[,4], boot.ci(boot_BPSP_results)$bca[,4]),4),silent=T) + try(Resultats[[.dico[["txt_param_tests"]]]][[.dico[["txt_partial_semi_BP"]]]][[.dico[["txt_bca_superior_limit"]]]]<-round( c(boot.ci(boot_BP_results)$bca[,5], boot.ci(boot_BPSP_results)$bca[,5]) ,4), silent=T) + #} else try(Resultats[[.dico[["txt_robust_analysis"]]]][[.dico[["txt_bootstrap_on_BP"]]]]<-round(data.frame(txt_bca_inferior_limit= boot.ci(boot_BP_results)$bca[,4], txt_bca_superior_limit=boot.ci(boot_BP_results)$bca[,5] ), 4),silent=T) } else { - try(Resultats[[txt_robust_analysis]][[txt_bootstrap_on_BP]]<-round(data.frame(txt_bca_inferior_limit= boot.ci(boot_BP_results)$bca[,4], txt_bca_superior_limit=boot.ci(boot_BP_results)$bca[,5] ), 4),silent=T) - try(names(Resultats[[txt_robust_analysis]][[txt_bootstrap_on_BP]])<-c(txt_bca_inferior_limit, txt_bca_superior_limit)) + try(Resultats[[.dico[["txt_robust_analysis"]]]][[.dico[["txt_bootstrap_on_BP"]]]]<-round(data.frame(txt_bca_inferior_limit= boot.ci(boot_BP_results)$bca[,4], txt_bca_superior_limit=boot.ci(boot_BP_results)$bca[,5] ), 4),silent=T) + try(names(Resultats[[.dico[["txt_robust_analysis"]]]][[.dico[["txt_bootstrap_on_BP"]]]])<-c(.dico[["txt_bca_inferior_limit"]], .dico[["txt_bca_superior_limit"]])) } - if(any(param=="non param")| any(param==txt_non_parametric_test)) { + if(any(param=="non param")| any(param==.dico[["txt_non_parametric_test"]])) { boot_Spearman_results<-boot(data, boot_Spearman, n.boot) - if(!is.null(Resultats[[txt_non_parametric_test]][[txt_rho]])) { - try(Resultats[[txt_non_parametric_test]][[txt_rho]][[txt_bca_inferior_limit]]<-round( boot.ci(boot_Spearman_results)$bca[,4],4), silent=T) - try(Resultats[[txt_non_parametric_test]][[txt_rho]][[txt_bca_superior_limit]]<-round( boot.ci(boot_Spearman_results)$bca[,5],4), silent=T) + if(!is.null(Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_rho"]]]])) { + try(Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_rho"]]]][[.dico[["txt_bca_inferior_limit"]]]]<-round( boot.ci(boot_Spearman_results)$bca[,4],4), silent=T) + try(Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_rho"]]]][[.dico[["txt_bca_superior_limit"]]]]<-round( boot.ci(boot_Spearman_results)$bca[,5],4), silent=T) } else{ boot_SpearmanSP_results<-boot(data, boot_SpearmanSP, n.boot) - try(Resultats[[txt_non_parametric_test]][[txt_partial_semi_partial_rho]][[txt_bca_inferior_limit]]<-round(c( boot.ci(boot_Spearman_results)$bca[,4], boot.ci(boot_SpearmanSP_results)$bca[,4]),4), silent=T) - try(Resultats[[txt_non_parametric_test]][[txt_partial_semi_partial_rho]][[txt_bca_superior_limit]]<-round(c( boot.ci(boot_Spearman_results)$bca[,5], boot.ci(boot_SpearmanSP_results)$bca[,5]),4), silent=T) + try(Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_partial_semi_partial_rho"]]]][[.dico[["txt_bca_inferior_limit"]]]]<-round(c( boot.ci(boot_Spearman_results)$bca[,4], boot.ci(boot_SpearmanSP_results)$bca[,4]),4), silent=T) + try(Resultats[[.dico[["txt_non_parametric_test"]]]][[.dico[["txt_partial_semi_partial_rho"]]]][[.dico[["txt_bca_superior_limit"]]]]<-round(c( boot.ci(boot_Spearman_results)$bca[,5], boot.ci(boot_SpearmanSP_results)$bca[,5]),4), silent=T) } } } - if(any(param=="Bayes") | any(param==txt_bayesian_factors) ){ + if(any(param=="Bayes") | any(param==.dico[["txt_bayesian_factors"]]) ){ BF<-regressionBF(modele, data=data, rscaleCont=rscale ) sample<-posterior(BF, iterations = ifelse(is.null(n.boot), 1000, n.boot)) BF<-extractBF(BF, onlybf=F) BF<-data.frame(txt_bayesian_factor=c(ifelse(BF$bf>10000,">10000", round(BF$bf,5)), ifelse(1/BF$bf>10000, ">10000", round((1/BF$bf),5))), txt_error=round(c( BF$error, BF$error),5)) - names(BF)<-c(txt_bayesian_factor,txt_error) + names(BF)<-c(.dico[["txt_bayesian_factor"]],.dico[["txt_error"]]) - dimnames(BF)[[1]]<-c(txt_supports_alternative, txt_supports_null) + dimnames(BF)[[1]]<-c(.dico[["txt_supports_alternative"]], .dico[["txt_supports_null"]]) # what is the t-value for the data? r2Val <-cor.test(data[,X1],data[,Y1])$estimate BF$r<-r2Val r2Val<-r2Val^2 #BF$r.carre<-r2Val - BF[[txt_r_dot_square]]<-r2Val - Resultats[[txt_bayesian_factors]][[txt_bayesian_factors_for_BP]]<-BF + BF[[.dico[["txt_r_dot_square"]]]]<-r2Val + Resultats[[.dico[["txt_bayesian_factors"]]]][[.dico[["txt_bayesian_factors_for_BP"]]]]<-BF - if(any(param=="non param")| any(param==txt_non_parametric_test)) { + if(any(param=="non param")| any(param==.dico[["txt_non_parametric_test"]])) { data2<-sapply(data[,c(X,Y,Z)], rank, ties.method="average", na.last="keep") data2<-data.frame(data2) - if(choix!=txt_correlations){ + if(choix!=.dico[["txt_correlations"]]){ lm.r1<-lm(modele1, data2) lm.r2<-lm(modele2, data2) data2$'residus1'<-lm.r1$'residuals' @@ -353,9 +353,9 @@ corr.complet <- BFS<-data.frame(txt_bayesian_factor=c(ifelse(BFS$bf>10000,">10000", round(BFS$bf,5)), ifelse(1/BFS$bf>10000, ">10000", round((1/BFS$bf),5))), txt_error=round(c( BFS$error, BF$error),5)) - names(BFS)<-c(txt_bayesian_factor,txt_error) - dimnames(BFS)[[1]]<-c(txt_supports_alternative, txt_supports_null) - Resultats[[txt_bayesian_factors]][[txt_bayesian_factors_for_spearman]]<-BFS + names(BFS)<-c(.dico[["txt_bayesian_factor"]],.dico[["txt_error"]]) + dimnames(BFS)[[1]]<-c(.dico[["txt_supports_alternative"]], .dico[["txt_supports_null"]]) + Resultats[[.dico[["txt_bayesian_factors"]]]][[.dico[["txt_bayesian_factors_for_spearman"]]]]<-BFS } @@ -365,24 +365,24 @@ corr.complet <- BF<-extractBF(BF, onlybf=F) #return(data.frame(txt_bayesian_factor=round(BF$bf,5), txt_error=round(BF$error,5))) current_df <- data.frame(txt_bayesian_factor=round(BF$bf,5), txt_error=round(BF$error,5)) - names(current_df) <- c(txt_bayesian_factor,txt_error) + names(current_df) <- c(.dico[["txt_bayesian_factor"]],.dico[["txt_error"]]) return(current_df) } BPgroup<-by(data=data, INDICES=data[,group], FUN=corr.g) BPgroup<-round(matrix(unlist(BPgroup), ncol=2, byrow=T), 4) - dimnames(BPgroup)[[2]]<- c("FB", txt_error) + dimnames(BPgroup)[[2]]<- c("FB", .dico[["txt_error"]]) if(length(group)==1) {gr.l<-expand.grid(levels(data[,group])) names(gr.l)<-group}else gr.l<-expand.grid(sapply(data[,group],levels)) BPgroup<-data.frame(gr.l,BPgroup ) - if( any(param=="non param")| any(param==txt_non_parametric_test)){ + if( any(param=="non param")| any(param==.dico[["txt_non_parametric_test"]])){ BFgroupS<-by(data=data2, INDICES=data[,group], FUN=corr.g) BFgroupS<-matrix(unlist(BFgroupS), ncol=2, byrow=T) BPgroup<-cbind(BPgroup, BFgroupS) - names(BPgroup)<-c(group, "FB.BP",txt_error_BP, "FB.Spearman", txt_error_spearman) + names(BPgroup)<-c(group, "FB.BP",.dico[["txt_error_BP"]], "FB.Spearman", .dico[["txt_error_spearman"]]) } - BPgroup->Resultats[[txt_bayesian_factors]][[txt_bayesian_factor_by_group]] + BPgroup->Resultats[[.dico[["txt_bayesian_factors"]]]][[.dico[["txt_bayesian_factor_by_group"]]]] } plot(sample) @@ -395,9 +395,9 @@ corr.complet <- } SBF<-data.frame("n"=rep(5:length(data[,X]), each=3 ),"BF"= bfs, - "rscale"=as.factor(rep(c("moyen - 0.353", txt_large_half, txt_ultrawide_val), length.out= 3*(length(data[,X])-4) ))) + "rscale"=as.factor(rep(c("moyen - 0.353", .dico[["txt_large_half"]], .dico[["txt_ultrawide_val"]]), length.out= 3*(length(data[,X])-4) ))) SBF$rscale<-relevel(SBF$rscale, ref=2) - Resultats[[txt_bayesian_factors_sequential]]<-.plotSBF(SBF) + Resultats[[.dico[["txt_bayesian_factors_sequential"]]]]<-.plotSBF(SBF) ##### Debut du graphique Bayes Factor Robustness Check @@ -425,7 +425,7 @@ corr.complet <- format(axe2, scientific=T)->axe2b par(mar = c(4, 10, 0.5, 0.5), mgp = c(8, 1, 0)) plot(cauchyRates, bayesFactors, type = "l", lwd = 2, col = "gray48", ylim= c(min(bayesFactors), max(bayesFactors)), - yaxt = "n" , xaxt = "n", xlab = txt_cauchy_prior_width , ylab = txt_bayes_factor_10) + yaxt = "n" , xaxt = "n", xlab = .dico[["txt_cauchy_prior_width"]] , ylab = .dico[["txt_bayes_factor_10"]]) axis(2, labels=axe2b, at=axe2, las=2) abline(h = 0, lwd = 1) abline(h = 6, col = "black", lty = 2, lwd = 2) @@ -481,30 +481,30 @@ corr.complet <- Y1<-as.character(XY[i,2]) data1<-data[complete.cases(data[,c(Y1,X1,Z)]),] R1<-list() - if(any(outlier%in% c(txt_complete_dataset, "complete"))){ - R1[[txt_complete_dataset]]<-corr.complet.out(X=X1, Y=Y1,Z=Z, data=data1, choix=choix, group=group, param=param, n.boot=n.boot, rscale=rscale) + if(any(outlier%in% c(.dico[["txt_complete_dataset"]], "complete"))){ + R1[[.dico[["txt_complete_dataset"]]]]<-corr.complet.out(X=X1, Y=Y1,Z=Z, data=data1, choix=choix, group=group, param=param, n.boot=n.boot, rscale=rscale) } - if(any(outlier%in%c(txt_identifying_outliers,"id"))| - any(outlier%in%c(txt_without_outliers, "removed"))){ + if(any(outlier%in%c(.dico[["txt_identifying_outliers"]],"id"))| + any(outlier%in%c(.dico[["txt_without_outliers"]], "removed"))){ modele<-as.formula(paste0(X1,"~",Y1)) if(!is.null(Z)){for(i in 1:length(Z)) modele<-update(modele, as.formula(paste0(".~.+",Z[i])))} data1$'residu'<-resid(lm(modele, data=data1)) critere<-ifelse(is.null(z), "Grubbs", "z") valeurs.influentes(X='residu', critere=critere,z=z, data=data1)->influentes } - if(any(outlier%in% c("id",txt_identifying_outliers))){influentes->R1[[txt_outliers_values]]} - if(any(outlier%in%c("removed", txt_without_outliers))) { + if(any(outlier%in% c("id",.dico[["txt_identifying_outliers"]]))){influentes->R1[[.dico[["txt_outliers_values"]]]]} + if(any(outlier%in%c("removed", .dico[["txt_without_outliers"]]))) { #if(length(influentes$'observations influentes')!=0 | - #if(length(influentes[[txt_outliers]])!=0 | - #if(influentes[[txt_outliers_synthesis]]$Synthese[1]!=0 | - if(influentes[[txt_outliers_synthesis]][[txt_synthesis]][1]!=0 | - ! any(outlier %in% c(txt_complete_dataset,"complete"))){ + #if(length(influentes[[.dico[["txt_outliers"]]]])!=0 | + #if(influentes[[.dico[["txt_outliers_synthesis"]]]]$Synthese[1]!=0 | + if(influentes[[.dico[["txt_outliers_synthesis"]]]][[.dico[["txt_synthesis"]]]][1]!=0 | + ! any(outlier %in% c(.dico[["txt_complete_dataset"]],"complete"))){ get('nettoyees', envir=.GlobalEnv)->nettoyees - R1[[txt_without_outliers]]<-corr.complet.out(X=X1, Y=Y1,Z=Z, data=nettoyees, choix=choix, group=group, param=param, n.boot=n.boot, rscale=rscale) + R1[[.dico[["txt_without_outliers"]]]]<-corr.complet.out(X=X1, Y=Y1,Z=Z, data=nettoyees, choix=choix, group=group, param=param, n.boot=n.boot, rscale=rscale) } } Resultats[[i]]<-R1 - names(Resultats)[i]<-paste(txt_correlation_between_var_x, X1, desc_and_variabe, Y1) + names(Resultats)[i]<-paste(.dico[["txt_correlation_between_var_x"]], X1, .dico[["desc_and_variabe"]], Y1) } paste(X, collapse="','", sep="")->X @@ -528,7 +528,7 @@ corr.complet <- if(save){ try(ez.html(Resultats, html=F), silent=T) } - ref1(packages)->Resultats[[txt_references]] + ref1(packages)->Resultats[[.dico[["txt_references"]]]] if(html) try(ez.html(Resultats), silent=T) ### Obtenir les Resultats return(Resultats) diff --git a/R/corr.matrice.R b/R/corr.matrice.R index dc3b5a8..12ba12b 100644 --- a/R/corr.matrice.R +++ b/R/corr.matrice.R @@ -1,5 +1,5 @@ corr.matrice <- - function(X=NULL, Y=NULL, Z=NULL,data=NULL, group=NULL,method="pearson",param=c("H0","FB"), save=F, outlier=c(txt_complete_dataset),n.boot=1, rscale=0.354, info=T, + function(X=NULL, Y=NULL, Z=NULL,data=NULL, group=NULL,method="pearson",param=c("H0","FB"), save=F, outlier=c(.dico[["txt_complete_dataset"]]),n.boot=1, rscale=0.354, info=T, p.adjust="holm",out.m=2, na.rm=NULL, html=T) { # X : character or vector. First set of variables # Y : character or vector. Second set of variables Must be NULL if Z is not @@ -9,26 +9,26 @@ corr.matrice <- # method : one among c("pearson", "spearman", "kendall") # param : one or both among "H0" (null hypoethesis testing) et "FB"(bayesian factors) # save : logical. Must the analyses be saved ? - # outlier : One among c(txt_complete_dataset, txt_without_outliers) - # rscale : numeric. If not null, bayesian factors are computed. Can also be "moyen", txt_large, txt_ultrawide + # outlier : One among c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]) + # rscale : numeric. If not null, bayesian factors are computed. Can also be "moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]] # info : logical. Must information be displayed in dialog box interface. # correction : character. Probability adjustement. See p.adjust for list of possibilities # out.m : 1 for deleting one observation at the time in outlier detection. 2 for all at the same time. # na.rm : character. How to deal with missing values ? # html : Logical. Should output be a HTML page ? - corr.matrice.in<-function(X=NULL, Y=NULL, Z=NULL, group=NULL, data=NULL, p.adjust=NULL, rscale=0.354,save=F,outlier=txt_complete_dataset, info=T, method="pearson", param=c("H0","FB"), n.boot=NULL){ + corr.matrice.in<-function(X=NULL, Y=NULL, Z=NULL, group=NULL, data=NULL, p.adjust=NULL, rscale=0.354,save=F,outlier=.dico[["txt_complete_dataset"]], info=T, method="pearson", param=c("H0","FB"), n.boot=NULL){ Resultats<-list() if(!is.null(X) & !is.null(data) & (is.null(Y) | is.null(Z))) {dial<-F - if(is.null(Z)) choix<-txt_correlations else choix<-txt_partial_and_semi_correlations - #if(!is.null(Y)) carre<-"rectangulaire" else carre<-txt_square - if(!is.null(Y)) carre<-txt_rectangular else carre<-txt_square + if(is.null(Z)) choix<-.dico[["txt_correlations"]] else choix<-.dico[["txt_partial_and_semi_correlations"]] + #if(!is.null(Y)) carre<-"rectangulaire" else carre<-.dico[["txt_square"]] + if(!is.null(Y)) carre<-.dico[["txt_rectangular"]] else carre<-.dico[["txt_square"]] } else {dial<-T choix<-NULL} if(is.null(choix) ){ - if(info) writeLines(ask_type_correlation) - choix<-dlgList(c(txt_correlations, txt_partila_correlations), preselect=txt_correlations, multiple = FALSE, title=ask_corr_or_partial_correlations)$res + if(info) writeLines(.dico[["ask_type_correlation"]]) + choix<-dlgList(c(.dico[["txt_correlations"]], .dico[["txt_partila_correlations"]]), preselect=.dico[["txt_correlations"]], multiple = FALSE, title=.dico[["ask_corr_or_partial_correlations"]])$res if(length(choix)==0) return(NULL) } @@ -37,26 +37,26 @@ corr.matrice <- nom<-data[[1]] data<-data[[2]] - if(choix==txt_correlations & dial==T){ - writeLines(desc_square_matrix_rectangular_matrix) - carre<-dlgList(c(txt_square, txt_rectangular), multiple = FALSE, title=txt_matrix_type)$res + if(choix==.dico[["txt_correlations"]] & dial==T){ + writeLines(.dico[["desc_square_matrix_rectangular_matrix"]]) + carre<-dlgList(c(.dico[["txt_square"]], .dico[["txt_rectangular"]]), multiple = FALSE, title=.dico[["txt_matrix_type"]])$res if(length(carre)==0){Resultats<-corr.matrice.in() return(Resultats)} - } else carre<-txt_square + } else carre<-.dico[["txt_square"]] - msg3<-ask_first_variables_set + msg3<-.dico[["ask_first_variables_set"]] - X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=txt_variables, out=NULL) + X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=.dico[["txt_variables"]], out=NULL) if(is.null(X)) { corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats return(Resultats)} data<-X$data X1<-X$X - if(carre==txt_rectangular){ - msg4<-ask_second_variables_set - Y<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg4, multiple=T, title=txt_second_variables_set, out=X1) + if(carre==.dico[["txt_rectangular"]]){ + msg4<-.dico[["ask_second_variables_set"]] + Y<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg4, multiple=T, title=.dico[["txt_second_variables_set"]], out=X1) if(is.null(Y)) { corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats @@ -65,9 +65,9 @@ corr.matrice <- Y<-Y$X } - if(choix==txt_partila_correlations){ - msg6<-ask_control_variables - Z<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg6, multiple=T, title=txt_control_variables, out=c(X1,Y)) + if(choix==.dico[["txt_partila_correlations"]]){ + msg6<-.dico[["ask_control_variables"]] + Z<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg6, multiple=T, title=.dico[["txt_control_variables"]], out=c(X1,Y)) if(is.null(Z)) { corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats @@ -79,39 +79,39 @@ corr.matrice <- if(dial){ - if(info==TRUE) writeLines(desc_corr_group_analysis_spec) - dlgList(c(txt_yes, txt_no), preselect=txt_no, multiple = FALSE, title=ask_analysis_by_group)$res->par.groupe + if(info==TRUE) writeLines(.dico[["desc_corr_group_analysis_spec"]]) + dlgList(c(.dico[["txt_yes"]], .dico[["txt_no"]]), preselect=.dico[["txt_no"]], multiple = FALSE, title=.dico[["ask_analysis_by_group"]])$res->par.groupe if(length(par.groupe)==0) { corr.matrice.in(X=NULL, Y=NULL, data=NULL,method=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats return(Resultats) - } } else par.groupe<-txt_no - msg5<-ask_chose_ranking_categorial_factor - if(par.groupe==txt_yes || !is.null(group)){group<-.var.type(X=group, info=info, data=data, type="factor", check.prod=F, message=msg5, multiple=TRUE, title=txt_variables, out=c(X1,Y,Z)) + } } else par.groupe<-.dico[["txt_no"]] + msg5<-.dico[["ask_chose_ranking_categorial_factor"]] + if(par.groupe==.dico[["txt_yes"]] || !is.null(group)){group<-.var.type(X=group, info=info, data=data, type="factor", check.prod=F, message=msg5, multiple=TRUE, title=.dico[["txt_variables"]], out=c(X1,Y,Z)) if(length(group)==0) { corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats return(Resultats)} data<-group$data group<-group$X if(any(ftable(data[,group])<3)){ - msgBox(desc_need_at_least_three_observation_by_combination) + msgBox(.dico[["desc_need_at_least_three_observation_by_combination"]]) corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats return(Resultats) } } - if(dial || length(outlier)>1 || outlier %in% c(txt_complete_dataset, txt_without_outliers) ==FALSE){ - if(info) writeLines(ask_analysis_on_complete_data_or_remove_outliers) - outlier<- dlgList(c(txt_complete_dataset, txt_without_outliers), preselect=c(txt_complete_dataset), - multiple = FALSE, title=ask_results_desired)$res + if(dial || length(outlier)>1 || outlier %in% c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]) ==FALSE){ + if(info) writeLines(.dico[["ask_analysis_on_complete_data_or_remove_outliers"]]) + outlier<- dlgList(c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]), preselect=c(.dico[["txt_complete_dataset"]]), + multiple = FALSE, title=.dico[["ask_results_desired"]])$res if(length(outlier)==0) { Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353) return(Resultats)} } if(dial || length(method)>1 || method %in% c("pearson", "spearman","kendall") ==FALSE){ - if(info) writeLines(ask_correlations_type) - method<-dlgList(c("pearson", "spearman","kendall"), preselect="pearson", multiple = FALSE, title=ask_correlations_type)$res + if(info) writeLines(.dico[["ask_correlations_type"]]) + method<-dlgList(c("pearson", "spearman","kendall"), preselect="pearson", multiple = FALSE, title=.dico[["ask_correlations_type"]])$res if(length(method)==0) { Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353) return(Resultats)} @@ -121,13 +121,13 @@ corr.matrice <- if(is.null(Y) & is.null(Z)){ if(!is.null(n.boot) && ((class(n.boot)!="numeric" & class(n.boot)!="integer") || n.boot%%1!=0 || n.boot<1)){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } while(is.null(n.boot)){ - writeLines(ask_bootstrap_numbers_1_for_none) + writeLines(.dico[["ask_bootstrap_numbers_1_for_none"]]) - n.boot<-dlgInput(ask_bootstraps_number, 1)$res + n.boot<-dlgInput(.dico[["ask_bootstraps_number"]], 1)$res if(length(n.boot)==0) {Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, method=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353) return(Resultats)} @@ -135,44 +135,44 @@ corr.matrice <- tail(n.boot[[1]],n=1)->n.boot as.numeric(n.boot)->n.boot if(is.na(n.boot) || n.boot%%1!=0 || n.boot<1){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } } } - if((dial)|| !is.null(rscale) & ((is.numeric(rscale) & (rscale<0.1 | rscale>2)) || (!is.numeric(rscale) & rscale%in% c("moyen", txt_large, txt_ultrawide)==F))) { - if(info) writeLines(ask_null_hypothesis_tests_or_bayesian_factors) - param<-dlgList(c(txt_bayesian_factors,txt_null_hypothesis_tests), preselect=c(txt_bayesian_factors,txt_null_hypothesis_tests), multiple = T, title=ask_statistical_approach)$res + if((dial)|| !is.null(rscale) & ((is.numeric(rscale) & (rscale<0.1 | rscale>2)) || (!is.numeric(rscale) & rscale%in% c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]])==F))) { + if(info) writeLines(.dico[["ask_null_hypothesis_tests_or_bayesian_factors"]]) + param<-dlgList(c(.dico[["txt_bayesian_factors"]],.dico[["txt_null_hypothesis_tests"]]), preselect=c(.dico[["txt_bayesian_factors"]],.dico[["txt_null_hypothesis_tests"]]), multiple = T, title=.dico[["ask_statistical_approach"]])$res if(length(param)==0) { Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353) return(Resultats)} - if(any(param==txt_bayesian_factors) | any(param=="FB")){ - if(info) writeLines(ask_cauchy_apriori_distribution) + if(any(param==.dico[["txt_bayesian_factors"]]) | any(param=="FB")){ + if(info) writeLines(.dico[["ask_cauchy_apriori_distribution"]]) - rscale<-dlgList(c("moyen", txt_large, txt_ultrawide), preselect="moyen", multiple = F, title=ask_distribution_type)$res + rscale<-dlgList(c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]), preselect="moyen", multiple = F, title=.dico[["ask_distribution_type"]])$res if(length(rscale)==0) { Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353) return(Resultats) } - ifelse(rscale=="moyen", rscale<-2^0.5/4, ifelse(rscale==txt_large, rscale<-0.5, ifelse(rscale==txt_ultrawide, rscale<-2^0.5/2, rscale<-rscale)))} else rscale<-NULL + ifelse(rscale=="moyen", rscale<-2^0.5/4, ifelse(rscale==.dico[["txt_large"]], rscale<-0.5, ifelse(rscale==.dico[["txt_ultrawide"]], rscale<-2^0.5/2, rscale<-rscale)))} else rscale<-NULL } - if(any(param==txt_null_hypothesis_tests) |any(param=="H0")){ + if(any(param==.dico[["txt_null_hypothesis_tests"]]) |any(param=="H0")){ if(dial | length(p.adjust)!=1 || p.adjust %in% c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none")==FALSE){ - writeLines(ask_correction_desired) - dlgList(c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none"), preselect=NULL, multiple = FALSE, title=ask_correction_type)$res->p.adjust + writeLines(.dico[["ask_correction_desired"]]) + dlgList(c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none"), preselect=NULL, multiple = FALSE, title=.dico[["ask_correction_type"]])$res->p.adjust if(length(p.adjust)==0) {Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats return(Resultats)} } } else p.adjust<-"none" if(dial | length(save)!=1 || !is.logical(save)){ - writeLines(ask_save_results) - save<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = TRUE, title=ask_save_results)$res + writeLines(.dico[["ask_save_results"]]) + save<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = TRUE, title=.dico[["ask_save_results"]])$res if(length(save)==0) {Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353)->Resultats return(Resultats)} @@ -180,9 +180,9 @@ corr.matrice <- } if(any(is.na(data[,c(X1,Y,Z)]))){ - msgBox(ask_how_to_treat_missing_values) - imp<- dlgList(c(txt_do_nothing_keep_all_obs, txt_delete_observations_with_missing_values, txt_replace_by_mean, - txt_replace_by_median,txt_multiple_imputation_amelia), preselect=FALSE, multiple = TRUE, title=txt_missing_values_treatment)$res + msgBox(.dico[["ask_how_to_treat_missing_values"]]) + imp<- dlgList(c(.dico[["txt_do_nothing_keep_all_obs"]], .dico[["txt_delete_observations_with_missing_values"]], .dico[["txt_replace_by_mean"]], + .dico[["txt_replace_by_median"]],.dico[["txt_multiple_imputation_amelia"]]), preselect=FALSE, multiple = TRUE, title=.dico[["txt_missing_values_treatment"]])$res if(length(imp)==0){ Resultats<-corr.matrice.in(X=NULL, Y=NULL, data=NULL, param=NULL, outlier=NULL, save=NULL, info=T, group=NULL, n.boot=NULL, rscale=0.353) @@ -213,8 +213,8 @@ corr.matrice <- corr.matrice.out<-function(data, X, Y, Z, p.adjust, method,save, rscale, n.boot, param){ Resultats<-list() - Resultats[[txt_descriptive_statistics]]<-.stat.desc.out(X=c(X,Y,Z), groupes=NULL, data=data, tr=.1, type=3, plot=F) - Resultats[[txt_multivariate_normality]]<-.normalite(data, c(X,Y,Z)) + Resultats[[.dico[["txt_descriptive_statistics"]]]]<-.stat.desc.out(X=c(X,Y,Z), groupes=NULL, data=data, tr=.1, type=3, plot=F) + Resultats[[.dico[["txt_multivariate_normality"]]]]<-.normalite(data, c(X,Y,Z)) if(is.null(Z)){ if(is.null(Y)) { Y1<-NULL @@ -224,10 +224,10 @@ corr.matrice <- } X1<-as.data.frame(data[,X]) names(X1)<-X - corr.test(x=X1, y=Y1, use = txt_pairwise,method=method,adjust=p.adjust, alpha=.05,ci=TRUE)->matrice + corr.test(x=X1, y=Y1, use = .dico[["txt_pairwise"]],method=method,adjust=p.adjust, alpha=.05,ci=TRUE)->matrice r1<-round(matrice$r,3) if(is.null(Y)) r1[which(lower.tri(r1, diag = T))]<-"-" - Resultats[[txt_correlations_matrix]]<-as.data.frame(r1) + Resultats[[.dico[["txt_correlations_matrix"]]]]<-as.data.frame(r1) } else{ data[,c(X,Z)]->d2 @@ -237,28 +237,28 @@ corr.matrice <- r1<-round(matrice$r, 3) class(r1)<-"matrix" r1[which(lower.tri(r1, diag = T))]<-"-" - Resultats[[txt_partial_correlations_matrix]] <-as.data.frame(r1) + Resultats[[.dico[["txt_partial_correlations_matrix"]]]] <-as.data.frame(r1) } class(r1)<-"matrix" dimnames(r1)[[1]]<-paste(dimnames(r1)[[1]], "r") - matrice$n->Resultats[[txt_sample_size]] + matrice$n->Resultats[[.dico[["txt_sample_size"]]]] - if(any(param=="H0")|any(param==txt_null_hypothesis_tests)) { - paste(desc_applied_correction_is,p.adjust)->Resultats$Correction[1] - if(is.null(Y)) {Resultats$Correction[2]<-desc_only_values_above_diagonal_are_adjusted_for_multiple_comp + if(any(param=="H0")|any(param==.dico[["txt_null_hypothesis_tests"]])) { + paste(.dico[["desc_applied_correction_is"]],p.adjust)->Resultats$Correction[1] + if(is.null(Y)) {Resultats$Correction[2]<-.dico[["desc_only_values_above_diagonal_are_adjusted_for_multiple_comp"]] round(matrice$p,3)->r2}else{ r2<-round(matrice$p.adj,3) } class(r2)<-c("matrix", "p.value") - Resultats[[txt_probability_matrix]]<-r2 + Resultats[[.dico[["txt_probability_matrix"]]]]<-r2 dimnames(r2)[[1]]<-paste0(dimnames(r2)[[1]], ".p") if(is.null(Y)) r2[which(lower.tri(r2, diag = T))]<-NA r1<-rbind(r1,r2) } if(method=="kendall") { r2<-round(sin(0.5*pi*matrice$r)^2,3) # from David A. Walker 2003 JMASM9: Converting Kendall's Tau For Correlational Or Meta-Analytic Analyses - Resultats[[txt_information]]<-desc_effect_size_by_walker + Resultats[[.dico[["txt_information"]]]]<-.dico[["desc_effect_size_by_walker"]] } else r2<-round(matrice$r^2,3) @@ -271,12 +271,12 @@ corr.matrice <- r3<-format(r3, scientific=T) if(is.null(Y)) r3[which(lower.tri(r3, diag = T))]<-"-" dimnames(r3)[[1]]<-paste0(dimnames(r3)[[1]], ".FB") - Resultats[[txt_bayesian_factors]]<-as.data.frame(r3) + Resultats[[.dico[["txt_bayesian_factors"]]]]<-as.data.frame(r3) r1<-rbind(r1, r3) } class(r2)<-"matrix" if(is.null(Y)) r2[which(lower.tri(r2, diag = T))]<-"-" - Resultats[[txt_r_squared_matrix]] <-as.data.frame(r2) + Resultats[[.dico[["txt_r_squared_matrix"]]]] <-as.data.frame(r2) dimnames(r2)[[1]]<-paste(dimnames(r2)[[1]], "r^2") r1<-rbind(r1, r2) r1<-data.frame(r1) @@ -287,12 +287,12 @@ corr.matrice <- r1[is.na(r1)]<-"-" } nice.mat<-list() - nice.mat[[txt_correlations_matrix]]<-(r1) + nice.mat[[.dico[["txt_correlations_matrix"]]]]<-(r1) if(html) try(ez.html(nice.mat), silent =T) - if(is.null(Y) & is.null(Z) & (!is.null(n.boot) && n.boot > 100)) round(cor.ci(data[,X], n.iter=n.boot, plot=FALSE)$ci,4)->Resultats[[txt_confidence_interval_estimated_by_bootstrap]] else round(matrice$ci,4)->Resultats[[txt_confidence_interval]] - names(Resultats[[length(Resultats)]])<-c(txt_inferior_limit,"r",txt_ci_superior_limit,txt_p_dot_val) + if(is.null(Y) & is.null(Z) & (!is.null(n.boot) && n.boot > 100)) round(cor.ci(data[,X], n.iter=n.boot, plot=FALSE)$ci,4)->Resultats[[.dico[["txt_confidence_interval_estimated_by_bootstrap"]]]] else round(matrice$ci,4)->Resultats[[.dico[["txt_confidence_interval"]]]] + names(Resultats[[length(Resultats)]])<-c(.dico[["txt_inferior_limit"]],"r",.dico[["txt_ci_superior_limit"]],.dico[["txt_p_dot_val"]]) return(Resultats) @@ -326,18 +326,18 @@ corr.matrice <- p.adjust<-corr.options$p.adjust n.boot<-corr.options$n.boot - if(outlier==txt_without_outliers){ + if(outlier==.dico[["txt_without_outliers"]]){ # When matrix correlations > partial > without outliers => missing Y value causes crash (VI.multiples no Y argumlent) #print(corr.options) #print(X) #print(Y) # <- missing #print(Z) inf<-VI.multiples(data, X=c(X,Y,Z)) - Resultats[[txt_labeled_outliers]]<-inf[[txt_labeled_outliers]] + Resultats[[.dico[["txt_labeled_outliers"]]]]<-inf[[.dico[["txt_labeled_outliers"]]]] data<-inf$data } - Resultats[[txt_correlations_matrix]]<-corr.matrice.out(data=data, X=X, Y=Y, Z=Z, p.adjust=p.adjust, method=method,save=save, + Resultats[[.dico[["txt_correlations_matrix"]]]]<-corr.matrice.out(data=data, X=X, Y=Y, Z=Z, p.adjust=p.adjust, method=method,save=save, rscale=rscale, n.boot=n.boot, param=param) @@ -373,8 +373,8 @@ corr.matrice <- - if(save) save(Resultats=Resultats, choix=paste(txt_correlation_is, method), env=.e) - ref1(packages)->Resultats[[txt_references]] + if(save) save(Resultats=Resultats, choix=paste(.dico[["txt_correlation_is"]], method), env=.e) + ref1(packages)->Resultats[[.dico[["txt_references"]]]] if(html) try(ez.html(Resultats), silent=T) return(Resultats) } diff --git a/R/donnees.R b/R/donnees.R index 1e8ffdd..d8ccd74 100644 --- a/R/donnees.R +++ b/R/donnees.R @@ -1,18 +1,18 @@ donnees <- function(){options (warn=-1) require(svDialogs) - choix<- c(txt_import_data, txt_view_data, txt_import_results,txt_export_data, txt_compile_report) - if( 'RGtk2Extras' %in% installed.packages()) choix<-c(txt_new_data_set, choix) - title<-ask_what_to_do + choix<- c(.dico[["txt_import_data"]], .dico[["txt_view_data"]], .dico[["txt_import_results"]],.dico[["txt_export_data"]], .dico[["txt_compile_report"]]) + if( 'RGtk2Extras' %in% installed.packages()) choix<-c(.dico[["txt_new_data_set"]], choix) + title<-.dico[["ask_what_to_do"]] dlgList(choix, preselect=NULL, multiple = FALSE, title=title)$res->choix if(length(choix)==0) return(easieR()) - if(choix %in% c(txt_new_data_set, txt_new_data_set)) blank.data()->Resultats - if(choix %in% c(txt_view_data, txt_view_data)) voir()->Resultats - if(choix %in% c(txt_import_results, txt_import_results)) import.results()->Resultats - if(choix %in% c(txt_import_data, txt_import_data) ) import()->Resultats - if(choix %in% c(txt_export_data, txt_export_data)) exporterD()->Resultats - if(choix %in% c(txt_compile_report, txt_compile_report)) { + if(choix %in% c(.dico[["txt_new_data_set"]], .dico[["txt_new_data_set"]])) blank.data()->Resultats + if(choix %in% c(.dico[["txt_view_data"]], .dico[["txt_view_data"]])) voir()->Resultats + if(choix %in% c(.dico[["txt_import_results"]], .dico[["txt_import_results"]])) import.results()->Resultats + if(choix %in% c(.dico[["txt_import_data"]], .dico[["txt_import_data"]]) ) import()->Resultats + if(choix %in% c(.dico[["txt_export_data"]], .dico[["txt_export_data"]])) exporterD()->Resultats + if(choix %in% c(.dico[["txt_compile_report"]], .dico[["txt_compile_report"]])) { ez.report() Resultats<-NULL } diff --git a/R/easieR.R b/R/easieR.R index 3c4f007..cbeb777 100644 --- a/R/easieR.R +++ b/R/easieR.R @@ -1,6 +1,6 @@ easieR <- function(info=TRUE, html=T, lang=NULL){ - + # 1. l'argument info permettra a terme de choisir les informations qui s'affichent dans la console ou non options (warn=1) options(scipen=999) @@ -16,12 +16,12 @@ easieR <- choix <- dlgList(easieR.msg("2"), preselect=NULL, multiple = FALSE, title=easieR.msg("3"))$res if(length(choix)==0) return(writeLines(easieR.msg("4"))) else { - if(choix %in%c(.dico$txt_data_import_export_save, .dico$txt_data_import_export_save)) Resultats <- donnees() - if(choix %in% c(.dico$txt_hypothesis_analysis, .dico$txt_hypothesis_analysis)) Resultats <-analyse(html=html) - if(choix%in%c(.dico$txt_interface_objects_in_memory, .dico$txt_interface_objects_in_memory)) Resultats <- interfaceR() - if(choix%in% c(.dico$txt_preprocess_sort_select_operations, .dico$txt_preprocess_sort_select_operations)) Resultats<-preprocess() - if(choix%in% c(.dico$txt_teaching_material, .dico$txt_teaching_material)) return(teaching()) - if(choix%in%c(.dico$txt_graphics, .dico$txt_graphics)) return(graphiques()) + if(choix %in%c(.dico[["txt_data_import_export_save"]], .dico[["txt_data_import_export_save"]])) Resultats <- donnees() + if(choix %in% c(.dico[["txt_hypothesis_analysis"]], .dico[["txt_hypothesis_analysis"]])) Resultats <-analyse(html=html) + if(choix%in%c(.dico[["txt_interface_objects_in_memory"]], .dico[["txt_interface_objects_in_memory"]])) Resultats <- interfaceR() + if(choix%in% c(.dico[["txt_preprocess_sort_select_operations"]], .dico[["txt_preprocess_sort_select_operations"]])) Resultats<-preprocess() + if(choix%in% c(.dico[["txt_teaching_material"]], .dico[["txt_teaching_material"]])) return(teaching()) + if(choix%in%c(.dico[["txt_graphics"]], .dico[["txt_graphics"]])) return(graphiques()) return(Resultats) } } @@ -29,35 +29,35 @@ easieR <- easieR.msg<-function(msg="1"){ #if(grepl("French",Sys.setlocale()) | grepl("fr",Sys.setlocale())) { # msg<-switch(msg, - # "1"=c(.dico$desc_for_easier_to_work) , - # "2"=c(.dico$txt_data_import_export_save, - # .dico$txt_preprocess_sort_select_operations, - # .dico$txt_hypothesis_analysis, .dico$txt_graphics, - # .dico$txt_interface_objects_in_memory, - # .dico$txt_teaching_material), - # "3"=.dico$ask_what_do_you_want, - # "4"=.dico$txt_user_exited_easieR) + # "1"=c(.dico[["desc_for_easier_to_work"]]) , + # "2"=c(.dico[["txt_data_import_export_save"]], + # .dico[["txt_preprocess_sort_select_operations"]], + # .dico[["txt_hypothesis_analysis"]], .dico[["txt_graphics"]], + # .dico[["txt_interface_objects_in_memory"]], + # .dico[["txt_teaching_material"]]), + # "3"=.dico[["ask_what_do_you_want"]], + # "4"=.dico[["txt_user_exited_easieR"]]) # }else { # msg<-switch(msg, "1"="In order to ensure that easieR is properly installed, please install Pandoc at the following url : # https://github.com/jgm/pandoc/releases", - # "2"=c("Data - (Import, export, save)", .dico$txt_preprocess_sort_select_operations, + # "2"=c("Data - (Import, export, save)", .dico[["txt_preprocess_sort_select_operations"]], # "Analyses - Hypothesis tests", "Graphics", - # .dico$txt_interface_objects_in_memory, + # .dico[["txt_interface_objects_in_memory"]], # "Teaching material"), # "3"="What do you want to do?", # "4"="User has terminated easieR") # # } - if (msg=="1") c(.dico$desc_for_easier_to_work) -> msg - else if (msg=="2") c(.dico$txt_data_import_export_save, - .dico$txt_preprocess_sort_select_operations, - .dico$txt_hypothesis_analysis, - .dico$txt_graphics, - .dico$txt_interface_objects_in_memory, - .dico$txt_teaching_material) -> msg - else if (msg=="3") .dico$ask_what_do_you_want -> msg - else if (msg=="4") .dico$txt_user_exited_easieR -> msg + if (msg=="1") c(.dico[["desc_for_easier_to_work"]]) -> msg + else if (msg=="2") c(.dico[["txt_data_import_export_save"]], + .dico[["txt_preprocess_sort_select_operations"]], + .dico[["txt_hypothesis_analysis"]], + .dico[["txt_graphics"]], + .dico[["txt_interface_objects_in_memory"]], + .dico[["txt_teaching_material"]]) -> msg + else if (msg=="3") .dico[["ask_what_do_you_want"]] -> msg + else if (msg=="4") .dico[["txt_user_exited_easieR"]] -> msg return(msg) } @@ -73,7 +73,7 @@ easieR.msg<-function(msg="1"){ -#### statistiques .dico$descriptives #### +#### statistiques descriptives #### #### permet d'identifier et enlever les valeurs influentes #### @@ -90,7 +90,7 @@ VI.multiples<-function(data, X){ nvar<-length(X) try(psych::outlier(data[,X], bad=T, na.rm=T,plot=T),silent=T)->essai if(class(essai)=='try-error'){ - msgBox(.dico$desc_singular_matrix_mahalanobis_on_max_info) + msgBox(.dico[["desc_singular_matrix_mahalanobis_on_max_info"]]) data2<-data rankifremoved <- sapply(1:ncol(data2), function (x) qr(data2[,-x])$rank) which(rankifremoved == max(rankifremoved))->rangs @@ -109,13 +109,13 @@ VI.multiples<-function(data, X){ if(class(essai)=='try-error') { corr.test(data2[,X])$r->matrice if(any(abs(matrice)==1)) { - msgBox(.dico$desc_perfectly_correlated_variables_in_matrix_trying_to_solve) + msgBox(.dico[["desc_perfectly_correlated_variables_in_matrix_trying_to_solve"]]) which(abs(matrice)==1, arr.ind=TRUE)->un un<-un[-which(un[,1]==un[,2]),] data2[,-un[,2]]->data2 try(psych::outlier(data2), silent=T)->essai if(class(essai)=='try-error') { - writeLines(.dico$desc_cannot_compute_mahalanobis) + writeLines(.dico[["desc_cannot_compute_mahalanobis"]]) 0->data$D.Mahalanobis } }else{essai-> data$D.Mahalanobis} } else{ essai-> data$D.Mahalanobis @@ -128,30 +128,31 @@ VI.multiples<-function(data, X){ data[which(data$D.Mahalanobis>seuil),]->outliers length(outliers[,1])/length(data[,1])*100->pourcent - msgBox(paste(round(pourcent,2), .dico$desc_percentage_outliers)) + + msgBox(paste(round(pourcent,2), .dico[["desc_percentage_outliers"]])) if(pourcent!=0){ - writeLines(.dico$desc_outliers_removal_implications) + writeLines(.dico[["desc_outliers_removal_implications"]]) - suppr<- dlgList(c(.dico$txt_suppress_all_outliers, .dico$txt_suppress_outliers_manually), - preselect=c(.dico$txt_suppress_all_outliers), multiple = FALSE, title=.dico$ask_how_to_remove)$res + suppr<- dlgList(c(.dico[["txt_suppress_all_outliers"]], .dico[["txt_suppress_outliers_manually"]]), + preselect=c(.dico[["txt_suppress_all_outliers"]]), multiple = FALSE, title=.dico[["ask_how_to_remove"]])$res if(length(suppr)==0) return(NULL) - if(suppr==.dico$txt_suppress_all_outliers) {data[which(data$D.Mahalanobisdata - outliers->Resultats[[.dico$txt_labeled_outliers]]}else{ + if(suppr==.dico[["txt_suppress_all_outliers"]]) {data[which(data$D.Mahalanobisdata + outliers->Resultats[[.dico[["txt_labeled_outliers"]]]]}else{ suppression<-"yes" outliers<-data.frame() while(suppression=="yes"){ print(data[which.max(data$D.Mahalanobis),]) - cat (.dico$ask_press_enter_to_continue) + cat (.dico[["ask_press_enter_to_continue"]]) line <- readline() - dlgMessage(.dico$ask_suppress_this_obs, "yesno")$res->suppression + dlgMessage(.dico[["ask_suppress_this_obs"]], "yesno")$res->suppression if(suppression=="yes") {rbind(outliers, data[which.max(data$D.Mahalanobis),])->outliers data[-which.max(data$D.Mahalanobis),]->data } } - Resultats[[.dico$txt_labeled_outliers]]<-outliers + Resultats[[.dico[["txt_labeled_outliers"]]]]<-outliers } } Resultats$data<-data @@ -217,11 +218,11 @@ VI.multiples<-function(data, X){ etend.y<-max.y-min.y y_breaks<-c(min.y, min.y+1/4*etend.y ,min.y+1/2*etend.y ,min.y+3/4*etend.y , max.y ) y_labs<-as.character(round(exp(y_breaks),2)) - reorder( c("moyen", .dico$txt_large, .dico$txt_ultrawide),levels(SBF$rscale))->levels(SBF$rscale) - p1 <- ggplot(SBF, aes(x = n, y = log(BF), group=rscale)) + ylab(.dico$txt_bayesian_factors_10) + - # p1 <- ggplot(SBF, aes(x = as.factor(n), y = log(BF), group=rscale)) + ylab(.dico$txt_bayesian_factors_10) + + reorder( c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]),levels(SBF$rscale))->levels(SBF$rscale) + p1 <- ggplot(SBF, aes(x = n, y = log(BF), group=rscale)) + ylab(.dico[["txt_bayesian_factors_10"]]) + + # p1 <- ggplot(SBF, aes(x = as.factor(n), y = log(BF), group=rscale)) + ylab(.dico[["txt_bayesian_factors_10"]]) + xlab("n")+ geom_line(aes(linetype=rscale))+ geom_point() - p1<-p1+theme(plot.title = element_text(size = 12))+ggtitle(.dico$txt_sequential_bayesian_factors_robustness_analysis) + p1<-p1+theme(plot.title = element_text(size = 12))+ggtitle(.dico[["txt_sequential_bayesian_factors_robustness_analysis"]]) p1<-p1+scale_y_continuous(breaks = y_breaks, labels =y_labs ) return(p1) } @@ -248,7 +249,7 @@ VI.multiples<-function(data, X){ -.var.type<-function(X=NULL, info=T, data=NULL, type=NULL, check.prod=T, message=NULL, multiple=F, title=.dico$txt_variable, out=NULL){ +.var.type<-function(X=NULL, info=T, data=NULL, type=NULL, check.prod=T, message=NULL, multiple=F, title=.dico[["txt_variable"]], out=NULL){ # permet de selectionner des variables # verifie les conditions pour les variables qui doivent respecter certaines conditions # data : data.frame name which allow to check whether the variable is the data.frame @@ -279,20 +280,20 @@ VI.multiples<-function(data, X){ if(!is.null(type) && type=="factor"){ if(all(sapply(data[,X], class)%in% c("factor", "character"))!=T ) { - res<-okCancelBox(.dico$ask_transform_numerical_to_categorial_variables) + res<-okCancelBox(.dico[["ask_transform_numerical_to_categorial_variables"]]) if(res==F) {X<-NULL .var.type(X=NULL, info=info, data=data, type=type,message=message, multiple=multiple, title=title, out=out)->Resultats return(Resultats)} } if(length(X)==1) factor(data[,X])->data[,X] else lapply(data[, X], factor)->data[, X] if((length(X)==1 && nlevels(data[,X])<2) | (length(X)>1 && any(sapply(data[, X], nlevels)<2))) { - okCancelBox(.dico$ask_choose_a_variable_with_at_least_two_modalities) + okCancelBox(.dico[["ask_choose_a_variable_with_at_least_two_modalities"]]) .var.type(X=NULL, info=info, data=data, type=type,message=message, multiple=multiple, title=title,out=out)->Resultats return(Resultats) } if(check.prod){ if(length(X)>1 && prod(sapply(data[,X],nlevels))>length(data[,1])) { - msgBox(.dico$ask_redefine_analysis_because_modalities_product_is_superior_to_obs) + msgBox(.dico[["ask_redefine_analysis_because_modalities_product_is_superior_to_obs"]]) .var.type(X=NULL, info=info, data=data, type=type,message=message, multiple=multiple, title=title,out=out)->Resultats return(Resultats) } @@ -303,7 +304,7 @@ VI.multiples<-function(data, X){ } if(!is.null(type) && type=="integer"){ if((any(data[,X]%%1==0) %in% c(FALSE, NA)) || min(data[,X])<0) { - okCancelBox(.dico$desc_variable_must_be_positive_int) + okCancelBox(.dico[["desc_variable_must_be_positive_int"]]) X<-NULL .var.type(X=NULL, info=info, data=data, type=type,message=message, multiple=multiple, title=title, out=out)->Resultats return(Resultats) @@ -312,7 +313,7 @@ VI.multiples<-function(data, X){ if(!is.null(type) && type=="numeric"){ if(length(X)==1) moy<-is.na(mean(data[,X],na.rm=T)) else moy<-any(is.na(sapply(data[,X], mean, na.rm=T))) if(any(moy!=0) || any(var(data[,X],na.rm=T)==0)){ - okCancelBox(.dico$desc_variable_must_be_numeric_and_of_non_null_variance) + okCancelBox(.dico[["desc_variable_must_be_numeric_and_of_non_null_variance"]]) X<-NULL .var.type(X=NULL, info=info, data=data, type=type,message=message, multiple=multiple, title=title, out=out)->Resultats return(Resultats) @@ -346,35 +347,35 @@ VI.multiples<-function(data, X){ choix<-c() if(param==T){ if(info) writeLines(msg.options1) - choix<-c(choix, .dico$txt_param_tests) + choix<-c(choix, .dico[["txt_param_tests"]]) } if(non.param==T) { if(info) writeLines(msg.options2) - choix<-c(choix, .dico$txt_non_parametric_test) + choix<-c(choix, .dico[["txt_non_parametric_test"]]) } if(robust==T) { - if(info) writeLines(.dico$desc_robust_statistics_are_alternative_to_the_principal_but_slower) - choix<-c(choix, .dico$txt_robusts_tests_with_bootstraps) + if(info) writeLines(.dico[["desc_robust_statistics_are_alternative_to_the_principal_but_slower"]]) + choix<-c(choix, .dico[["txt_robusts_tests_with_bootstraps"]]) } if(Bayes==T) { - if(info) writeLines(.dico$txt_bayesian_factors_compute_null_with_bayesian_approach) - choix<-c(choix, .dico$txt_bayesian_factors) + if(info) writeLines(.dico[["txt_bayesian_factors_compute_null_with_bayesian_approach"]]) + choix<-c(choix, .dico[["txt_bayesian_factors"]]) } - choix<- dlgList(choix, preselect=choix, multiple = TRUE, title=.dico$ask_which_analysis)$res + choix<- dlgList(choix, preselect=choix, multiple = TRUE, title=.dico[["ask_which_analysis"]])$res if(length(choix)==0) return(NULL) } Resultats$choix<-choix - if(exists('choix') && any(choix== .dico$txt_robusts_tests_with_bootstraps) || !is.null(n.boot)){{ + if(exists('choix') && any(choix== .dico[["txt_robusts_tests_with_bootstraps"]]) || !is.null(n.boot)){{ if(!is.null(n.boot) && ((class(n.boot)!="numeric" & class(n.boot)!="integer") || n.boot%%1!=0 || n.boot<1)){ - msgBox(.dico$desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } while(is.null(n.boot)){ - writeLines(.dico$ask_bootstrap_numbers_1_for_none) + writeLines(.dico[["ask_bootstrap_numbers_1_for_none"]]) - n.boot<-dlgInput(.dico$ask_bootstraps_number, 1000)$res + n.boot<-dlgInput(.dico[["ask_bootstraps_number"]], 1000)$res if(length(n.boot)==0) {.ez.options(options=options, n.boot=NULL,param=param, non.param=non.param, robust=robust, Bayes=Bayes, msg.options1=msg.options1, msg.options2=msg.options2, info=T, dial=T, choix=choix,sauvegarde=F, outlier=NULL,rscale=rscale)->Resultats @@ -383,7 +384,7 @@ VI.multiples<-function(data, X){ tail(n.boot[[1]],n=1)->n.boot as.numeric(n.boot)->n.boot if(is.na(n.boot) || n.boot%%1!=0 || n.boot<1){ - msgBox(.dico$desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } } @@ -391,9 +392,9 @@ VI.multiples<-function(data, X){ Resultats$n.boot<-n.boot } if(!is.null(rscale)){ - if(dial & any(choix==.dico$txt_bayesian_factors)|| (is.numeric(rscale) & (rscale<0.1 | rscale>2)) || (!is.numeric(rscale) & rscale%in% c("moyen", .dico$txt_large, .dico$txt_ultrawide)==F)) { - if(info) writeLines(.dico$ask_cauchy_apriori_distribution) - rscale<-dlgList(c("moyen", .dico$txt_large, .dico$txt_ultrawide), preselect="moyen", multiple = F, title=.dico$ask_distribution_type)$res + if(dial & any(choix==.dico[["txt_bayesian_factors"]])|| (is.numeric(rscale) & (rscale<0.1 | rscale>2)) || (!is.numeric(rscale) & rscale%in% c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]])==F)) { + if(info) writeLines(.dico[["ask_cauchy_apriori_distribution"]]) + rscale<-dlgList(c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]), preselect="moyen", multiple = F, title=.dico[["ask_distribution_type"]])$res if(length(rscale)==0) { .ez.options(options=options, n.boot=NULL,param=param, non.param=non.param, robust=robust, Bayes=Bayes, msg.options1=msg.options1, msg.options2=msg.options2, info=T, dial=T, @@ -401,7 +402,7 @@ VI.multiples<-function(data, X){ } } if(is.character(rscale)) { - ifelse(rscale=="moyen", rscale<-2^0.5/2, ifelse(rscale==.dico$txt_large, rscale<-1, ifelse(rscale==.dico$txt_ultrawide, rscale<-2^0.5, rscale<-rscale))) + ifelse(rscale=="moyen", rscale<-2^0.5/2, ifelse(rscale==.dico[["txt_large"]], rscale<-1, ifelse(rscale==.dico[["txt_ultrawide"]], rscale<-2^0.5, rscale<-rscale))) Resultats$rscalei<-T } else Resultats$rscalei<-F @@ -411,12 +412,12 @@ VI.multiples<-function(data, X){ if(any(options=="outlier")){ if(dial || is.null(outlier)|| - (dial==F & any(outlier %in%c(.dico$txt_complete_dataset, .dico$txt_identifying_outliers,.dico$txt_without_outliers, + (dial==F & any(outlier %in%c(.dico[["txt_complete_dataset"]], .dico[["txt_identifying_outliers"]],.dico[["txt_without_outliers"]], "complete", "id", "removed"))==F)) { - if(info==TRUE) writeLines(.dico$desc_complete_dataset_vs_identification_outliers_vs_without_outliers) - Resultats$desires<- dlgList(c(.dico$txt_complete_dataset, .dico$txt_identifying_outliers,.dico$txt_without_outliers), - preselect=c(.dico$txt_complete_dataset,.dico$txt_identifying_outliers, .dico$txt_without_outliers), - multiple = TRUE, title=.dico$ask_which_analysis)$res + if(info==TRUE) writeLines(.dico[["desc_complete_dataset_vs_identification_outliers_vs_without_outliers"]]) + Resultats$desires<- dlgList(c(.dico[["txt_complete_dataset"]], .dico[["txt_identifying_outliers"]],.dico[["txt_without_outliers"]]), + preselect=c(.dico[["txt_complete_dataset"]],.dico[["txt_identifying_outliers"]], .dico[["txt_without_outliers"]]), + multiple = TRUE, title=.dico[["ask_which_analysis"]])$res if(length(Resultats$desires)==0) {.ez.options(options=options, n.boot=NULL,param=param, non.param=non.param, robust=robust, Bayes=Bayes, msg.options1=msg.options1, msg.options2=msg.options2, info=T, dial=T, choix=choix,sauvegarde=F, outlier=NULL,rscale=rscale)->Resultats @@ -424,7 +425,7 @@ VI.multiples<-function(data, X){ } else Resultats$desires<-outlier } - if( dial==T) {Resultats$sauvegarde<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=.dico$ask_save_results)$res + if( dial==T) {Resultats$sauvegarde<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=.dico[["ask_save_results"]])$res if(length(Resultats$sauvegarde)==0) {.ez.options(options=options, n.boot=NULL,param=param, non.param=non.param, robust=robust, Bayes=Bayes, msg.options1=msg.options1, msg.options2=msg.options2, info=T, dial=T, choix=choix,sauvegarde=F, outlier=NULL,rscale=rscale)->Resultats @@ -441,7 +442,7 @@ VI.multiples<-function(data, X){ try(get("ez.history", envir=.GlobalEnv),silent=T)->ez.history if(class(ez.history)=='try-error') {ez.history<-list() ez.history$Analyse[[1]]<-data - names(ez.history)[length(ez.history)]<-paste(.dico$txt_analysis_on,nom) + names(ez.history)[length(ez.history)]<-paste(.dico[["txt_analysis_on"]],nom) names(ez.history[[length(ez.history)]])[1]<-nom ez.history[[length(ez.history)]]$historique<-command }else{ @@ -450,7 +451,7 @@ VI.multiples<-function(data, X){ ez.history[[length(ez.history)]]$historique<-rbind(ez.history[[length(ez.history)]]$historique,command) }else { ez.history$Analyse[[1]]<-data - names(ez.history)[length(ez.history)]<-paste(.dico$txt_analysis_on,nom) + names(ez.history)[length(ez.history)]<-paste(.dico[["txt_analysis_on"]],nom) names(ez.history[[length(ez.history)]])[1]<-nom ez.history[[length(ez.history)]]$historique<-command } @@ -498,10 +499,10 @@ VI.multiples<-function(data, X){ shapiro.test(data[,"res"])->Shapiro_Wilk # realise le Shapiro-Wilk lillie.test(data[,"res"])->Lilliefors # realise le Lilliefors round(data.frame(Shapiro_Wilk$statistic,Shapiro_Wilk$p.value, Lilliefors$statistic, Lilliefors$p.value),4)->normalite - names(normalite)<-c(.dico$txt_shapiro_wilk, .dico$txt_p_dot_val_sw, .dico$txt_lilliefors_d, .dico$txt_p_dot_val_lilliefors) + names(normalite)<-c(.dico[["txt_shapiro_wilk"]], .dico[["txt_p_dot_val_sw"]], .dico[["txt_lilliefors_d"]], .dico[["txt_p_dot_val_lilliefors"]]) dimnames(normalite)[1]<-" " # format(normalite, width = max(sapply(names(normalite), nchar)), justify = "centre")->normalite - n2[[.dico$txt_normality_tests]]<-normalite} + n2[[.dico[["txt_normality_tests"]]]]<-normalite} #p1<-ggplot(data, aes(x=res))+geom_histogram(aes(y=..density..)) @@ -509,9 +510,9 @@ VI.multiples<-function(data, X){ p1<-p1+ stat_function(fun = dnorm, colour = "red", args = list(mean = mean(data[,"res"], na.rm = TRUE), sd = sd(data[,"res"], na.rm = TRUE))) - p1<-p1+theme(plot.title = element_text(size = 12))+labs(x = .dico$txt_residual_distribution) + p1<-p1+theme(plot.title = element_text(size = 12))+labs(x = .dico[["txt_residual_distribution"]]) #print(p1) - n2[[.dico$txt_residuals_distribution]]<-p1 + n2[[.dico[["txt_residuals_distribution"]]]]<-p1 p2<-ggplot(data, aes(sample=res))+stat_qq() p2<-p2+theme(plot.title = element_text(size = 12))+ggtitle("QQplot") n2$"QQplot"<-p2 @@ -525,8 +526,8 @@ VI.multiples<-function(data, X){ "skew"=mardia.results$skew,"p.skew"=mardia.results$p.skew,"small.skew"= mardia.results$small.skew,"p.small"= mardia.results$p.small, "kurtosis"=mardia.results$kurtosis,"p.kurtosis"=mardia.results$p.kurt )->n2 } else { - msgBox(.dico$desc_matrix_is_singular_mardia_cannot_be_performed) - n2<-data.frame(.dico$txt_shapiro_wilk=NULL, .dico$txt_p_dot_val_sw=NULL, .dico$txt_lilliefors_d=NULL, .dico$txt_p_dot_val_lilliefors=NULL) + msgBox(.dico[["desc_matrix_is_singular_mardia_cannot_be_performed"]]) + n2<-data.frame(txt_shapiro_wilk=NULL, txt_p_dot_val_sw=NULL, txt_lilliefors_d=NULL, txt_p_dot_val_lilliefors=NULL) for(i in 1:length(X)){ X[i]->Z .normalite(data=data, X=Z,Y=Y)->nor1 @@ -541,7 +542,7 @@ VI.multiples<-function(data, X){ # cree la liste avec tous les resultats -.stat..dico$desc.out<-function(X=NULL, groupes=NULL, data=NULL, tr=.1, type=3, plot=T){ +.stat.desc.out<-function(X=NULL, groupes=NULL, data=NULL, tr=.1, type=3, plot=T){ packages<-c('psych', 'ggplot2') test2<-try(lapply(packages, library, character.only=T), silent=T) data_summary <- function(x) { @@ -559,16 +560,16 @@ VI.multiples<-function(data, X){ if(length(X)!=0){ if(is.null(groupes)) NULL->groupes2 else data.frame(data[,groupes])->groupes2 - try( psych::.dico$describeBy(data[,X], group=groupes2,mat=(!is.null(groupes)),type=type,digits=4, check=FALSE,skew = TRUE, - ranges = TRUE,trim=tr, fast=FALSE), silent=T)->psych..dico$desc - if(any(class(psych..dico$desc)=='try-error')) { - psych::.dico$describeBy(data[,X], group=groupes2,mat=F,type=type,digits=15, check=FALSE,skew = TRUE, - ranges = TRUE,trim=tr)->psych..dico$desc + try( psych::describeBy(data[,X], group=groupes2,mat=(!is.null(groupes)),type=type,digits=4, check=FALSE,skew = TRUE, + ranges = TRUE,trim=tr, fast=FALSE), silent=T)->psych.desc + if(any(class(psych.desc)=='try-error')) { + psych::describeBy(data[,X], group=groupes2,mat=F,type=type,digits=15, check=FALSE,skew = TRUE, + ranges = TRUE,trim=tr)->psych.desc expand.grid(sapply(groupes2, levels))->modalites for(i in 1:length(modalites[,1])) { - if(is.null(psych..dico$desc[[i]])) paste(.dico$desc_no_obs_for_combination, paste(unlist(modalites[i,]), collapse=" & "))->Resultats[[i]] else psych..dico$desc[[i]]->Resultats[[i]] + if(is.null(psych.desc[[i]])) paste(.dico[["desc_no_obs_for_combination"]], paste(unlist(modalites[i,]), collapse=" & "))->Resultats[[i]] else psych.desc[[i]]->Resultats[[i]] paste(unlist(modalites[i,]), collapse=" & ")->names(Resultats)[i]} - } else psych..dico$desc-> Resultats[[.dico$txt_numeric_variables]] + } else psych.desc-> Resultats[[.dico[["txt_numeric_variables"]]]] @@ -614,8 +615,8 @@ VI.multiples<-function(data, X){ }) Resultats$Graphiques<-graphiques - Resultats[[.dico$txt_graphics_informations]][[1]]<-.dico$desc_graph_thickness_gives_density - Resultats[[.dico$txt_graphics_informations]][[2]]<-.dico$desc_red_dot_is_mean_error_is_sd + Resultats[[.dico[["txt_graphics_informations"]]]][[1]]<-.dico[["desc_graph_thickness_gives_density"]] + Resultats[[.dico[["txt_graphics_informations"]]]][[2]]<-.dico[["desc_red_dot_is_mean_error_is_sd"]] } } @@ -636,14 +637,14 @@ ref1 <- require('bibtex') c('base', packages, 'bibtex')->packages if(Sys.info()[[1]]=="Windows"){ - file.name.dico$txt<-paste0(tempdir(), "\\references.bib") + file.nametxt<-paste0(tempdir(), "\\references.bib") } else { - file.name.dico$txt<-paste0(tempdir(), "/references.bib") + file.nametxt<-paste0(tempdir(), "/references.bib") } - write.bib(packages, file=file.name.dico$txt) - bibtex::read.bib(file.name.dico$txt)->Resultats - file.remove(file.name.dico$txt) + write.bib(packages, file=file.nametxt) + bibtex::read.bib(file.nametxt)->Resultats + file.remove(file.nametxt) return(Resultats) } @@ -652,14 +653,14 @@ ref1 <- .onAttach <- function(libname, pkgname) { load_language(lang='auto') textVersion = - paste(.dico$desc_how_to_cite_easier, - .dico$desc_easier_metapackage, + paste(.dico[["desc_how_to_cite_easier"]], + .dico[["desc_easier_metapackage"]], sep = "") packageStartupMessage("##############") - packageStartupMessage(.dico$desc_welcome_in_easieR) - packageStartupMessage(.dico$desc_first_time_easier) - packageStartupMessage(.dico$desc_special_characters_have_been_removed) + packageStartupMessage(.dico[["desc_welcome_in_easieR"]]) + packageStartupMessage(.dico[["desc_first_time_easier"]]) + packageStartupMessage(.dico[["desc_special_characters_have_been_removed"]]) packageStartupMessage(textVersion) packageStartupMessage("Last update 06/24/2024") packageStartupMessage("##############") diff --git a/R/exporterD.R b/R/exporterD.R index 97f428d..e150219 100644 --- a/R/exporterD.R +++ b/R/exporterD.R @@ -5,14 +5,14 @@ exporterD <- require(packages)} list()->Resultats data <- dlgList(Filter( function(x) 'data.frame' %in% class( get(x) ), ls(envir=.GlobalEnv)), multiple = FALSE, - title=ask_which_data_to_export)$res + title=.dico[["ask_which_data_to_export"]])$res if(length(data)==0) return(donnees()) data<-get(data) - nom <- dlgInput(ask_exportation_filename, "Nouveau.fichier")$res + nom <- dlgInput(.dico[["ask_exportation_filename"]], "Nouveau.fichier")$res if(length(nom)==0) nom<-"Nouveau.fichier" strsplit(nom, ":")->nom tail(nom[[1]],n=1)->nom write.csv(data, file=paste(nom, ".csv")) - paste(desc_file_is_saved_in, getwd())->Resultats + paste(.dico[["desc_file_is_saved_in"]], getwd())->Resultats return(Resultats) } diff --git a/R/ez.anova.R b/R/ez.anova.R index 5560183..e07e5f0 100644 --- a/R/ez.anova.R +++ b/R/ez.anova.R @@ -57,25 +57,25 @@ ez.anova<-function(data=NULL, DV=NULL, between=NULL, within=NULL,id=NULL, cov=NU complet[["aov.plus.in"]]<-NULL - if(any(outlier %in% c("complete", txt_complete_dataset,txt_complete_dataset))){ + if(any(outlier %in% c("complete", .dico[["txt_complete_dataset"]],.dico[["txt_complete_dataset"]]))){ Resultats[[.ez.anova.msg("title", 12)]]<-complet - aov.plus.in->aov.plus.list[[txt_complete_dataset]]} + aov.plus.in->aov.plus.list[[.dico[["txt_complete_dataset"]]]]} - if(any(outlier %in% c("id", "removed" , txt_identifying_outliers, txt_without_outliers, - txt_identifying_outliers, txt_without_outliers))) { + if(any(outlier %in% c("id", "removed" , .dico[["txt_identifying_outliers"]], .dico[["txt_without_outliers"]], + .dico[["txt_identifying_outliers"]], .dico[["txt_without_outliers"]]))) { if(is.null(data$'residu')) { Resultats[[.ez.anova.msg("title", 55)]]<-.ez.anova.msg("msg", 34) return(Resultats)} valeurs.influentes(X='residu', critere="Grubbs",z=3.26, data=data)->influentes - if(any(outlier %in% c(txt_identifying_outliers,"id",txt_identifying_outliers ))) Resultats[[.ez.anova.msg("title", 13)]]<-influentes - if(any(outlier %in% c( txt_without_outliers, txt_without_outliers,"removed" ))){ + if(any(outlier %in% c(.dico[["txt_identifying_outliers"]],"id",.dico[["txt_identifying_outliers"]] ))) Resultats[[.ez.anova.msg("title", 13)]]<-influentes + if(any(outlier %in% c( .dico[["txt_without_outliers"]], .dico[["txt_without_outliers"]],"removed" ))){ - #if(!is.null(influentes[[txt_outliers]][,id])){ - # setdiff(data[,as.character(id)],influentes[[txt_outliers]][,as.character(id)])->diffs - #if(influentes[[txt_outliers_synthesis]]$Synthese[1]!=0){ - if(influentes[[txt_outliers_synthesis]][[txt_synthesis]][1]!=0){ - setdiff(data[,as.character(id)],influentes[[txt_outliers]][,as.character(id)])->diffs + #if(!is.null(influentes[[.dico[["txt_outliers"]]]][,id])){ + # setdiff(data[,as.character(id)],influentes[[.dico[["txt_outliers"]]]][,as.character(id)])->diffs + #if(influentes[[.dico[["txt_outliers_synthesis"]]]]$Synthese[1]!=0){ + if(influentes[[.dico[["txt_outliers_synthesis"]]]][[.dico[["txt_synthesis"]]]][1]!=0){ + setdiff(data[,as.character(id)],influentes[[.dico[["txt_outliers"]]]][,as.character(id)])->diffs data[which(data[,id] %in% diffs), ]->nettoyees factor(nettoyees[,id])->nettoyees[,id] nett<-.ez.anova.out(data=nettoyees, DV=DV, between=between, within=within,id=id, cov=cov, @@ -84,10 +84,10 @@ ez.anova<-function(data=NULL, DV=NULL, between=NULL, within=NULL,id=NULL, cov=NU nett[["data"]]<-NULL nett[["aov.plus.in"]]<-NULL Resultats[[.ez.anova.msg("title", 14)]]<-nett - aov.plus.in->aov.plus.list[[txt_without_outliers]] + aov.plus.in->aov.plus.list[[.dico[["txt_without_outliers"]]]] } - print(!all(outlier %in% c("complete", txt_complete_dataset,txt_complete_dataset))) - if(!any(outlier %in% c("complete", txt_complete_dataset,txt_complete_dataset))) Resultats[[.ez.anova.msg("title", 14)]]<-complet + print(!all(outlier %in% c("complete", .dico[["txt_complete_dataset"]],.dico[["txt_complete_dataset"]]))) + if(!any(outlier %in% c("complete", .dico[["txt_complete_dataset"]],.dico[["txt_complete_dataset"]]))) Resultats[[.ez.anova.msg("title", 14)]]<-complet } @@ -104,7 +104,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist if(!is.null(cov)) cov<-paste(unique(cov), collapse="','", sep="") param<-paste(unique(param), collapse="','", sep="") outlier<-paste(unique(outlier), collapse="','", sep="") - if(!any(contrasts%in%c("none", txt_none, txt_pairwise, txt_comparison_two_by_two))){ + if(!any(contrasts%in%c("none", .dico[["txt_none"]], .dico[["txt_pairwise"]], .dico[["txt_comparison_two_by_two"]]))){ cont.call<-"list(" for(i in 1:length(contrasts)){ if(i>1) cont.call<-paste0(cont.call, ",") @@ -121,9 +121,9 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist ", p.adjust = '", p.adjust, "', n.boot = ", n.boot, ",rscaleFixed = ", rscaleFixed, ", rscaleRandom = ", rscaleRandom, ")") Resultats$call<-call - .add.history(data=data, command=Resultats$Call, nom=txt_anova) - .add.result(Resultats=Resultats, name =paste(txt_anova, Sys.time() )) - if(save==T) save(Resultats=Resultats ,choix =paste(txt_anova_on, nom), env=.e) + .add.history(data=data, command=Resultats$Call, nom=.dico[["txt_anova"]]) + .add.result(Resultats=Resultats, name =paste(.dico[["txt_anova"]], Sys.time() )) + if(save==T) save(Resultats=Resultats ,choix =paste(.dico[["txt_anova_on"]], nom), env=.e) ref1(packages)->Resultats[[.ez.anova.msg("title", 56)]] if(html) try(ez.html(Resultats), silent=T) return(Resultats) @@ -137,59 +137,59 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist .ez.anova.msg<-function(type, number){ # type : either "msg" or "title" # number : number of message - msg<-c(ask_variables_type_for_anova, - desc_at_least_independant_variables_or_repeated_measures, - ask_select_variables_or_modalities_of_repeated_measure_variable, - ask_which_variable_identifies_participants, - desc_each_participant_must_appear_only_once_, - desc_two_cols_are_needed, - desc_large_format_must_be_numeric_or_integer, - ask_chose_independant_group_variables, - ask_you_did_not_chose_a_variable_continue_or_abort, - ask_chose_dependant_variable, - desc_some_participants_have_missing_values_on_repeated_measures, - ask_chose_covariables, - ask_not_enough_obs_verify_dataset, - desc_all_tests_description, - desc_complete_dataset_vs_identification_outliers_vs_without_outliers, - desc_cannot_have_both_within_RML_arguments, - desc_most_common_effect_size, - desc_multiple_ways_to_compute_squares_sum, - ask_save_results, - ask_dependant_variable_with_less_than_three_val_verify_dataset, - desc_all_contrasts_description, - desc_you_can_chose_predefined_or_manual_contrasts, - ask_contrast_must_respect_ortho, - desc_contrasts_must_be_coeff_matrices_in_list, - desc_manual_contrast_need_coeff_matrice, - ask_which_correction, - desc_authorized_values_for_contrasts, - desc_at_least_on_contrast_matrix_incorrect, - desc_biased_results_risk_because_of_low_number_of_obs_or_zero_variance, - desc_bayesian_factors_could_not_be_computed, - desc_we_could_not_compute_anova_on_medians, - desc_proba_and_IC_estimated_on_bootstrap, - desc_we_could_not_compute_robust_anova, desc_analysis_aborted + msg<-c(.dico[["ask_variables_type_for_anova"]], + .dico[["desc_at_least_independant_variables_or_repeated_measures"]], + .dico[["ask_select_variables_or_modalities_of_repeated_measure_variable"]], + .dico[["ask_which_variable_identifies_participants"]], + .dico[["desc_each_participant_must_appear_only_once_"]], + .dico[["desc_two_cols_are_needed"]], + .dico[["desc_large_format_must_be_numeric_or_integer"]], + .dico[["ask_chose_independant_group_variables"]], + .dico[["ask_you_did_not_chose_a_variable_continue_or_abort"]], + .dico[["ask_chose_dependant_variable"]], + .dico[["desc_some_participants_have_missing_values_on_repeated_measures"]], + .dico[["ask_chose_covariables"]], + .dico[["ask_not_enough_obs_verify_dataset"]], + .dico[["desc_all_tests_description"]], + .dico[["desc_complete_dataset_vs_identification_outliers_vs_without_outliers"]], + .dico[["desc_cannot_have_both_within_RML_arguments"]], + .dico[["desc_most_common_effect_size"]], + .dico[["desc_multiple_ways_to_compute_squares_sum"]], + .dico[["ask_save_results"]], + .dico[["ask_dependant_variable_with_less_than_three_val_verify_dataset"]], + .dico[["desc_all_contrasts_description"]], + .dico[["desc_you_can_chose_predefined_or_manual_contrasts"]], + .dico[["ask_contrast_must_respect_ortho"]], + .dico[["desc_contrasts_must_be_coeff_matrices_in_list"]], + .dico[["desc_manual_contrast_need_coeff_matrice"]], + .dico[["ask_which_correction"]], + .dico[["desc_authorized_values_for_contrasts"]], + .dico[["desc_at_least_on_contrast_matrix_incorrect"]], + .dico[["desc_biased_results_risk_because_of_low_number_of_obs_or_zero_variance"]], + .dico[["desc_bayesian_factors_could_not_be_computed"]], + .dico[["desc_we_could_not_compute_anova_on_medians"]], + .dico[["desc_proba_and_IC_estimated_on_bootstrap"]], + .dico[["desc_we_could_not_compute_robust_anova"]], .dico[["desc_analysis_aborted"]] ) - title<-c(ask_variables_type, txt_repeated_measures,txt_participants_id,txt_independant_group_variables, - txt_dependant_variable, ask_covariables, - txt_param_model, txt_non_param_model,txt_bayesian_factors, txt_robust_statistics, - ask_which_analysis,txt_complete_dataset,txt_identifying_outliers, txt_without_outliers, - ask_results_desired, ask_which_size_effect,ask_which_squared_sum, - ask_save, txt_apriori, txt_comparison_two_by_two, txt_none,ask_which_contrasts, - txt_contrasts_for, txt_specify_contrasts,ask_which_baseline, txt_descriptive_statistics,txt_test_model, - txt_variable_descriptive_statistics,txt_descriptive_statistics_of_interaction_between_x,txt_warning, - txt_normality_tests,txt_ancova_application_conditions,txt_absence_of_difference_between_groups_test_on, - txt_slopes_homogeneity_between_groups_on_dependant_variable, - txt_levene_test_verifying_homogeneity_variances,txt_mauchly_test_sphericity_covariance_matrix, - txt_principal_analysis,txt_anova_with_welch_correction, txt_pairwise_comparisons,txt_contrasts, - txt_variables_coeff_matrix,txt_contrasts_table,txt_contrasts_table_imitating_commercial_softwares,txt_bayesian_factors, - txt_non_param_analysis,txt_kruskal_wallis_test,txt_friedman_anova,txt_friedman_anova_pairwise_comparison, - txt_kruskal_wallis_pairwise ,txt_anova_on_medians,txt_principal_analysis,txt_anova_on_truncated_means, - txt_anova_on_m_estimator, txt_anova_on_modified_huber_estimator,txt_analysis_premature_abortion, - desc_references + title<-c(.dico[["ask_variables_type"]], .dico[["txt_repeated_measures"]],.dico[["txt_participants_id"]],.dico[["txt_independant_group_variables"]], + .dico[["txt_dependant_variable"]], .dico[["ask_covariables"]], + .dico[["txt_param_model"]], .dico[["txt_non_param_model"]],.dico[["txt_bayesian_factors"]], .dico[["txt_robust_statistics"]], + .dico[["ask_which_analysis"]],.dico[["txt_complete_dataset"]],.dico[["txt_identifying_outliers"]], .dico[["txt_without_outliers"]], + .dico[["ask_results_desired"]], .dico[["ask_which_size_effect"]],.dico[["ask_which_squared_sum"]], + .dico[["ask_save"]], .dico[["txt_apriori"]], .dico[["txt_comparison_two_by_two"]], .dico[["txt_none"]],.dico[["ask_which_contrasts"]], + .dico[["txt_contrasts_for"]], .dico[["txt_specify_contrasts"]],.dico[["ask_which_baseline"]], .dico[["txt_descriptive_statistics"]],.dico[["txt_test_model"]], + .dico[["txt_variable_descriptive_statistics"]],.dico[["txt_descriptive_statistics_of_interaction_between_x"]],.dico[["txt_warning"]], + .dico[["txt_normality_tests"]],.dico[["txt_ancova_application_conditions"]],.dico[["txt_absence_of_difference_between_groups_test_on"]], + .dico[["txt_slopes_homogeneity_between_groups_on_dependant_variable"]], + .dico[["txt_levene_test_verifying_homogeneity_variances"]],.dico[["txt_mauchly_test_sphericity_covariance_matrix"]], + .dico[["txt_principal_analysis"]],.dico[["txt_anova_with_welch_correction"]], .dico[["txt_pairwise_comparisons"]],.dico[["txt_contrasts"]], + .dico[["txt_variables_coeff_matrix"]],.dico[["txt_contrasts_table"]],.dico[["txt_contrasts_table_imitating_commercial_softwares"]],.dico[["txt_bayesian_factors"]], + .dico[["txt_non_param_analysis"]],.dico[["txt_kruskal_wallis_test"]],.dico[["txt_friedman_anova"]],.dico[["txt_friedman_anova_pairwise_comparison"]], + .dico[["txt_kruskal_wallis_pairwise"]] ,.dico[["txt_anova_on_medians"]],.dico[["txt_principal_analysis"]],.dico[["txt_anova_on_truncated_means"]], + .dico[["txt_anova_on_m_estimator"]], .dico[["txt_anova_on_modified_huber_estimator"]],.dico[["txt_analysis_premature_abortion"]], + .dico[["desc_references"]] ) @@ -204,7 +204,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist # "Please choose between participant variables.", # "You have not chosen any variable. Do you want to continue (ok) or to leave (cancel) this analysis ?", # "Please choose the dependant variable", - # desc_some_participants_have_missing_values_on_repeated_measures, + # .dico[["desc_some_participants_have_missing_values_on_repeated_measures"]], # "Please choose the covariables", # "The number of observations is not enough given the number of levels for each variable. \nPlease ensure that there are at least 3 observations for each combination of levels", # "The parametruc model is the usual anova presented in statistical packages. The non parametric test is \nthe Kruskal Wallis test for one way anova and Friedman test for repeated measure anova.\n @@ -215,12 +215,12 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist # "There are several ways to estimate the Sum of Squares. Default value for comercial softwares is Type 3,\nwhich prioritizes interaction instead of main effects", # "Do you want to save the results of the analysis?", # "The dependant variable has less than 3 unique different values. Check your data or the analysis that you try to make is not relevant.", - # desc_all_contrasts_description, + # .dico[["desc_all_contrasts_description"]], # "You may use one of predefined contrast matrix or state the contrast by yourself. In the latter case, you must choose state the contrasts.", - # ask_contrasts_must_be_ortho, - # desc_contrasts_must_be_coeff_matrices_in_list, + # .dico[["ask_contrasts_must_be_ortho"]], + # .dico[["desc_contrasts_must_be_coeff_matrices_in_list"]], # "If you choose the coefficients yourself, all the variables in the analysis must have their coefficient matrix", - # ask_probability_correction, + # .dico[["ask_probability_correction"]], # "Allowed values for contrasts are +none+, +pairwise+ or a list with the coefficients of contrasts.", # "At least one of your contrast matrix is not correct.", # "There are less than 3 observations for at least one group or the variance for one groupe is 0. Results are probably biased", @@ -233,21 +233,21 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist # # # - # title<-c("Which kind of variable ?", txt_repeated_measures, "Id of individuals", "Between participant variables", - # "Dependant variable",ask_covariables, - # "Parametric", "Non parametric",txt_bayesian_factors, "Robust statistics - might take some time", + # title<-c("Which kind of variable ?", .dico[["txt_repeated_measures"]], "Id of individuals", "Between participant variables", + # "Dependant variable",.dico[["ask_covariables"]], + # "Parametric", "Non parametric",.dico[["txt_bayesian_factors"]], "Robust statistics - might take some time", # "Which analysis do you want ?", "Complete dataset", "Identification of outliers", "Dataset with outliers removed", # "Which results do you want ?", "Which effect size do you want?", "Which sum of squares do you want ?","Do you want to save?", - # txt_apriori, txt_pairwise, "none", "Please choose the type of contrast", "Contrasts for", "Choose your own contrasts", - # "Which level is the baseline?","Descriptive statistics", txt_test_model, "Descriptive for", - # txt_descriptive_statistics_of_interaction_between_x,"Warning","Normal distribution test", - # "Assumptions of ancova",txt_absence_of_difference_between_groups_test_on, + # .dico[["txt_apriori"]], .dico[["txt_pairwise"]], "none", "Please choose the type of contrast", "Contrasts for", "Choose your own contrasts", + # "Which level is the baseline?","Descriptive statistics", .dico[["txt_test_model"]], "Descriptive for", + # .dico[["txt_descriptive_statistics_of_interaction_between_x"]],"Warning","Normal distribution test", + # "Assumptions of ancova",.dico[["txt_absence_of_difference_between_groups_test_on"]], # "Homogeneity of slopes between groups on the dependant variable", - # "Levene's test testing homogeneity of variances",txt_mauchly_test_sphericity_covariance_matrix,"Main analysis", - # "Welch's ANOVA for heterogeneous variances",txt_pairwise_comparisons,"contrasts","Matrix of coefficients", - # "Table of contrasts","Contrasts that mimics commercial software",txt_bayesian_factors, txt_non_param_analysis, txt_kruskal_wallis_test, txt_friedman_anova, - # "Friedman pairwise comparison",txt_kruskal_wallis_pairwise, txt_anova_on_medians,"Main analysis", - # "Anova on trimmed mean", txt_anova_on_m_estimator,"Anova on modified Huber estimator", "A problem occurred. The analysis has stopped", + # "Levene's test testing homogeneity of variances",.dico[["txt_mauchly_test_sphericity_covariance_matrix"]],"Main analysis", + # "Welch's ANOVA for heterogeneous variances",.dico[["txt_pairwise_comparisons"]],"contrasts","Matrix of coefficients", + # "Table of contrasts","Contrasts that mimics commercial software",.dico[["txt_bayesian_factors"]], .dico[["txt_non_param_analysis"]], .dico[["txt_kruskal_wallis_test"]], .dico[["txt_friedman_anova"]], + # "Friedman pairwise comparison",.dico[["txt_kruskal_wallis_pairwise"]], .dico[["txt_anova_on_medians"]],"Main analysis", + # "Anova on trimmed mean", .dico[["txt_anova_on_m_estimator"]],"Anova on modified Huber estimator", "A problem occurred. The analysis has stopped", # "References of packages used for this analysis") # #} @@ -259,7 +259,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist .contrastes.ez<-function(data, between=NULL, within=NULL, contrasts="none", p.adjust="none", dial=T){ options (warn=1) c(between, unlist(within))->betweenwithin - if(any(!contrasts%in%c("none",txt_pairwise,txt_none,txt_pairwise_comparison)) & class(contrasts)!="list") { + if(any(!contrasts%in%c("none",.dico[["txt_pairwise"]],.dico[["txt_none"]],.dico[["txt_pairwise_comparison"]])) & class(contrasts)!="list") { okCancelBox( .ez.anova.msg("msg", 27)) return(.contrastes.ez(data=data, between=between, within=within, contrasts="none", p.adjust="none", dial=T)) } @@ -297,7 +297,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist if(length(type.cont)==0) return(NULL) Resultats$type.cont<-type.cont - if(type.cont==txt_apriori) { + if(type.cont==.dico[["txt_apriori"]]) { writeLines(.ez.anova.msg("msg", 21)) cont.exemple<-list() @@ -332,13 +332,13 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist if (type.cont2=="consec") { emmeans:::consec.emmc(1:nlevels(data[,betweenwithin[i]])) -> contrastes[[i]]} if (type.cont2=="mean.change") { emmeans:::mean_chg.emmc(1:nlevels(data[,betweenwithin[i]])) -> contrastes[[i]]} - if(type.cont2 %in% c(txt_specify_contrasts, txt_specify_contrasts)){ + if(type.cont2 %in% c(.dico[["txt_specify_contrasts"]], .dico[["txt_specify_contrasts"]])){ ortho<-FALSE while(ortho!=TRUE){ own.cont<-matrix(rep(0,times=(nlevels(data[,betweenwithin[i]])*(nlevels(data[,betweenwithin[i]])-1))), nrow=nlevels(data[,betweenwithin[i]])) dimnames( own.cont)[[1]]<-levels(data[,betweenwithin[i]]) - dimnames( own.cont)[[2]]<-paste(txt_contrast, 1:(nlevels(data[,betweenwithin[i]])-1), sep=".") + dimnames( own.cont)[[2]]<-paste(.dico[["txt_contrast"]], 1:(nlevels(data[,betweenwithin[i]])-1), sep=".") own.cont<-fix( own.cont) if(any(colSums( own.cont)!=0)|(nlevels(data[,betweenwithin[i]])>2 & max(rle(c( own.cont))$lengths)>2*(nlevels(data[,betweenwithin[i]])-2))) ortho<-FALSE else { @@ -357,20 +357,20 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist } - dimnames(contrastes[[i]])[[2]]<-paste(txt_contrast, 1:(ncol(contrastes[[i]])), sep=".") + dimnames(contrastes[[i]])[[2]]<-paste(.dico[["txt_contrast"]], 1:(ncol(contrastes[[i]])), sep=".") dimnames(contrastes[[i]])[[1]]<-levels(data[,betweenwithin[i]]) } names(contrastes)<-betweenwithin Resultats$contrastes<-contrastes }else{ - if(type.cont %in% c(txt_comparison_two_by_two,txt_pairwise, txt_pairwise, "none", txt_none)) { Resultats$contrastes<-type.cont + if(type.cont %in% c(.dico[["txt_comparison_two_by_two"]],.dico[["txt_pairwise"]], .dico[["txt_pairwise"]], "none", .dico[["txt_none"]])) { Resultats$contrastes<-type.cont contrastes<-type.cont} } }else{ contrastes<-list() - if(any(contrasts %in% c(txt_comparison_two_by_two,txt_pairwise, txt_pairwise, "none", txt_none))) { + if(any(contrasts %in% c(.dico[["txt_comparison_two_by_two"]],.dico[["txt_pairwise"]], .dico[["txt_pairwise"]], "none", .dico[["txt_none"]]))) { Resultats$contrastes<-contrasts contrastes<-contrasts }else{ @@ -380,7 +380,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist j<-which(names(data)==names(contrasts)[[i]]) noms<-list() noms[[1]]<-levels(data[,j]) - noms[[2]]<-paste(txt_contrast, 1:(ncol(cont2)), sep=".") + noms[[2]]<-paste(.dico[["txt_contrast"]], 1:(ncol(cont2)), sep=".") dimnames(cont2)<-noms contrastes[[i]]<-cont2 } @@ -388,13 +388,13 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist Resultats$contrastes<-contrastes } } - if((dial & all(contrastes %in% c(txt_comparison_two_by_two,txt_pairwise, txt_pairwise))) || + if((dial & all(contrastes %in% c(.dico[["txt_comparison_two_by_two"]],.dico[["txt_pairwise"]], .dico[["txt_pairwise"]]))) || (!p.adjust %in% c("holm", "hochberg", "hommel", "bonferroni", "fdr","tukey","scheffe", "sidak","dunnettx","mvt" ,"none" ))){ list()->p.adjust writeLines(.ez.anova.msg("msg", 26) ) dlgList(c("holm", "hochberg", "hommel", "bonferroni", "fdr","tukey","scheffe", - "sidak","dunnettx","mvt" ,"none"), preselect="holm", multiple = FALSE, title=ask_correction_anova_contrasts)$res->p.adjust + "sidak","dunnettx","mvt" ,"none"), preselect="holm", multiple = FALSE, title=.dico[["ask_correction_anova_contrasts"]])$res->p.adjust if(length(p.adjust)==0) return(contrastes.ez()) @@ -409,9 +409,9 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist list()->Resultats if(dial || !any(param %in% c("param", "non param", "bayes", "robust", - txt_param_model, txt_non_param_model,txt_bayesian_factors, txt_robust_statistics, - txt_param_model, txt_non_param_model,txt_bayesian_factors, txt_robust_statistics))){ - #"Parametric", "Non parametric",txt_bayesian_factors, "Robust statistics - might take some time"))){ + .dico[["txt_param_model"]], .dico[["txt_non_param_model"]],.dico[["txt_bayesian_factors"]], .dico[["txt_robust_statistics"]], + .dico[["txt_param_model"]], .dico[["txt_non_param_model"]],.dico[["txt_bayesian_factors"]], .dico[["txt_robust_statistics"]]))){ + #"Parametric", "Non parametric",.dico[["txt_bayesian_factors"]], "Robust statistics - might take some time"))){ writeLines(.ez.anova.msg("msg",14)) msg<-c(.ez.anova.msg("title",7),.ez.anova.msg("title",9)) if(is.null(cov)) { @@ -426,15 +426,15 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist - if(dial | !any(outlier%in% c("complete","id", "removed",txt_complete_dataset,txt_identifying_outliers, txt_without_outliers, - txt_complete_dataset,txt_identifying_outliers, txt_without_outliers)) ){ + if(dial | !any(outlier%in% c("complete","id", "removed",.dico[["txt_complete_dataset"]],.dico[["txt_identifying_outliers"]], .dico[["txt_without_outliers"]], + .dico[["txt_complete_dataset"]],.dico[["txt_identifying_outliers"]], .dico[["txt_without_outliers"]])) ){ outlier<-c(.ez.anova.msg("title",12),.ez.anova.msg("title",13),.ez.anova.msg("title",14)) outlier<- dlgList(outlier, preselect=outlier,multiple = TRUE, title=.ez.anova.msg("title",15))$res if(length(outlier)==0) return(.options.aov(between=between, within=within, cov=cov)) } - if(any(param %in% c(txt_param_model, "Parametric", "param"))){ + if(any(param %in% c(.dico[["txt_param_model"]], "Parametric", "param"))){ if(!ES %in% c("ges", "pes") | dial){ writeLines(.ez.anova.msg("msg",17)) ES<- dlgList(c("ges", "pes"), preselect=c("ges"),multiple = FALSE, title=.ez.anova.msg("title",16))$res @@ -480,19 +480,19 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist if(is.null(c(between,within, RML))) { index<-1 # From when language was hard coded in easieR. TODO - type.v<-matrix(c(txt_independant_groups, txt_repeated_measures, txt_covariables, - txt_independant_groups, txt_repeated_measures, txt_covariables), ncol=2) + type.v<-matrix(c(.dico[["txt_independant_groups"]], .dico[["txt_repeated_measures"]], .dico[["txt_covariables"]], + .dico[["txt_independant_groups"]], .dico[["txt_repeated_measures"]], .dico[["txt_covariables"]]), ncol=2) writeLines(.ez.anova.msg("msg", 1)) type.v2<-dlgList(type.v[,index], multiple = TRUE, title=.ez.anova.msg("title", 1))$res if(length(type.v2)==0) return(.ez.anova.in()) type.v<-type.v[which(type.v[, index]%in%type.v2),1] - if(!any(type.v %in% c(txt_independant_groups, txt_repeated_measures))) { + if(!any(type.v %in% c(.dico[["txt_independant_groups"]], .dico[["txt_repeated_measures"]]))) { writeLines(.ez.anova.msg("msg",2)) return(.ez.anova.in()) } } - if(any(type.v==txt_repeated_measures) | !is.null(within) | !is.null(RML)) { + if(any(type.v==.dico[["txt_repeated_measures"]]) | !is.null(within) | !is.null(RML)) { if(!is.null(RML)) within<-RML within<-.var.type(X=within, info=T, data=data, type=NULL, check.prod=F, message=.ez.anova.msg("msg",3), multiple=TRUE, title=.ez.anova.msg("title",2), out=NULL) @@ -541,8 +541,8 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist IV.names=IV.names, IV.levels=RML.factor) nom<-paste0(nom, ".long") reshape.data<-TRUE - DV<-txt_value - within<-setdiff(names(data), c(idvar, txt_value,"IDeasy")) + DV<-.dico[["txt_value"]] + within<-setdiff(names(data), c(idvar, .dico[["txt_value"]],"IDeasy")) if(length(within)>1) { data[,within]<-lapply(data[, within], factor) within<-within[-which(within=="time")] @@ -569,7 +569,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist - if(any(type.v==txt_independant_groups) | !is.null(between)){ + if(any(type.v==.dico[["txt_independant_groups"]]) | !is.null(between)){ between<-.var.type(X=between, info=T, data=data, type="factor", check.prod=F, message=.ez.anova.msg("msg",8), multiple=TRUE, title=.ez.anova.msg("title",4), out=diffs) if(is.null(between)) { @@ -609,7 +609,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist - if(any(type.v==txt_covariables)) { + if(any(type.v==.dico[["txt_covariables"]])) { cov<-.var.type(X=cov, info=T, data=data, type="numeric", check.prod=F, message=.ez.anova.msg("msg",12), multiple=TRUE, title=.ez.anova.msg("title",6), out=diffs) @@ -645,8 +645,8 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist msgBox(.ez.anova.msg("msg",13)) return(NULL) } -#if(any(param %in% c(txt_param_model, "Parametric", "param"))) - if(any(options.out$param %in% c("param", txt_param_model, "Parametric", "param"))){ +#if(any(param %in% c(.dico[["txt_param_model"]], "Parametric", "param"))) + if(any(options.out$param %in% c("param", .dico[["txt_param_model"]], "Parametric", "param"))){ contrasts<-.contrastes.ez(data=data, between=between, within=within, contrasts=contrasts, dial=dial, p.adjust=p.adjust) if(is.null(contrasts)) return(.ez.anova.in()) } else contrasts<-NULL @@ -725,7 +725,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist return(Resultats) } - if(any(param %in% c(txt_param_model,"param", "Parametric"))){ + if(any(param %in% c(.dico[["txt_param_model"]],"param", "Parametric"))){ if(any(Resultats[[2]][[1]]$sd==0)) Resultats[[.ez.anova.msg("title",30)]]<-.ez.anova.msg("msg",29) options(contrasts=c("contr.sum","contr.poly")) if(!is.null(cov)) factorize<-FALSE else factorize<-TRUE @@ -754,14 +754,14 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist for(i in 1:length(cov)){ aov(as.formula(paste0(cov[i], "~",pred.ind)), data=data)->aov.cov Anova(aov.cov, type="III")->aov.cov - names(aov.cov)<-c("SC", txt_df, "F", txt_p_dot_val) + names(aov.cov)<-c("SC", .dico[["txt_df"]], "F", .dico[["txt_p_dot_val"]]) Resultats[[.ez.anova.msg("title",32)]][[paste0(.ez.anova.msg("title",33), cov[i])]]<-aov.cov if(i==1) {paste(cov[1],"*")->cov2} else {paste0(cov2, cov[i],"*")->cov2} } aov(as.formula(paste0(DV, "~", cov2,pred.ind)), data=data)->aov.cov Anova(aov.cov, type="III")->aov.cov - names(aov.cov)<-c("SC", txt_df, "F", txt_p_dot_val) + names(aov.cov)<-c("SC", .dico[["txt_df"]], "F", .dico[["txt_p_dot_val"]]) Resultats[[.ez.anova.msg("title",32)]][[.ez.anova.msg("title",34)]]<-aov.cov } @@ -770,7 +770,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist paste0(DV, "~",pred.ind)->modele2 Levene<-leveneTest(as.formula(modele2),data=data) # test de Levene pour homogeneite des variances Levene<-round(unlist(Levene)[c(1,2,3,5)],3) - names(Levene)<-c(txt_df1,txt_df2,"F",txt_p_dot_val) + names(Levene)<-c(.dico[["txt_df1"]],.dico[["txt_df2"]],"F",.dico[["txt_p_dot_val"]]) Resultats[[.ez.anova.msg("title",35)]]<- Levene } @@ -791,21 +791,21 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist if(!is.null(within) && any( sapply(data[,c(unlist(within))],nlevels)>2)) { aov.out2b<-round(aov.out2$sphericity.test,5) aov.out2b<-matrix(aov.out2b, ncol=2) - dimnames(aov.out2b)<-list(dimnames(aov.out2$sphericity.test)[[1]], c("Stat", txt_p_dot_val)) + dimnames(aov.out2b)<-list(dimnames(aov.out2$sphericity.test)[[1]], c("Stat", .dico[["txt_p_dot_val"]])) Resultats[[.ez.anova.msg("title",36)]]<-aov.out2b } aov.out3<-aov.out[[1]] aov.out3<-data.frame(aov.out3) - names(aov.out3)<-c(txt_df_num, txt_df_denom, "CME", "F", ES, txt_p_dot_val ) + names(aov.out3)<-c(.dico[["txt_df_num"]], .dico[["txt_df_denom"]], "CME", "F", ES, .dico[["txt_p_dot_val"]] ) omega.out<-effectsize::omega_squared(aov.out$Anova) aov.out3<-cbind(aov.out3, omega.2=omega.out[match(rownames(aov.out3), omega.out$Parameter),2]) Resultats[[.ez.anova.msg("title",37)]]<- aov.out3 if(!is.null(within) && any( sapply(data[,c(unlist(within))],nlevels)>2)) { GG.HF<-data.frame(round(aov.out2$pval.adjustments,5)) - names(GG.HF)<-c("GG.eps", txt_gg_p_value,"HF.eps", txt_hf_p_value) - Resultats[[txt_greenhouse_geisser_huynn_feldt_correction]]<-GG.HF} + names(GG.HF)<-c("GG.eps", .dico[["txt_gg_p_value"]],"HF.eps", .dico[["txt_hf_p_value"]]) + Resultats[[.dico[["txt_greenhouse_geisser_huynn_feldt_correction"]]]]<-GG.HF} if(length(between)==1 & is.null(within) & is.null(cov)) { Welch<-oneway.test(as.formula(paste(DV,"~", between)),data=data) Welch<-round(data.frame("F"=Welch$statistic,"num"=Welch$parameter[1],"denom"=Welch$parameter[2],"p"=Welch$p.value),4) @@ -817,10 +817,10 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist aov.plus.in$em.out<-em.out - if(!is.list(contrasts) && any(contrasts %in% c(txt_pairwise, txt_comparison_two_by_two ))){ + if(!is.list(contrasts) && any(contrasts %in% c(.dico[["txt_pairwise"]], .dico[["txt_comparison_two_by_two"]] ))){ pair<-pairs(em.out, adjust=p.adjust) pair<-summary(pair) - names(pair)[which(names(pair)=="p.value")]<-txt_p_dot_val + names(pair)[which(names(pair)=="p.value")]<-.dico[["txt_p_dot_val"]] Resultats[[.ez.anova.msg("title",39)]]<-pair } @@ -879,7 +879,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist round( ifelse(table.cont$d.Cohen==T, (2*table.cont$t.ratio)/(nlevels(data[,id])^0.5), table.cont$t.ratio/(nlevels(data[,id])^0.5)),4)->table.cont$d.Cohen}else{ round(table.cont$t.ratio/((nlevels(data[,id]))^0.5),4)->table.cont$d.Cohen} - names(table.cont)<-c(txt_contrast,txt_estimation, txt_error_dot_standard_short, txt_df,"t", txt_p_dot_val, txt_r_square, "d Cohen") + names(table.cont)<-c(.dico[["txt_contrast"]],.dico[["txt_estimation"]], .dico[["txt_error_dot_standard_short"]], .dico[["txt_df"]],"t", .dico[["txt_p_dot_val"]], .dico[["txt_r_square"]], "d Cohen") @@ -931,7 +931,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist round(Table.contrasts,4)->Table.contrasts data.frame(Table.contrasts)->Table.contrasts - names(Table.contrasts)<-c(txt_estimator, txt_df,"t", txt_p_dot_val) + names(Table.contrasts)<-c(.dico[["txt_estimator"]], .dico[["txt_df"]],"t", .dico[["txt_p_dot_val"]]) Table.contrasts$t^2/(Table.contrasts$t^2+Table.contrasts$ddl)->Table.contrasts$R.2 round(Table.contrasts$t/(nlevels(data[,id]))^0.5,4)->Table.contrasts$D.Cohen dimnames(Table.contrasts)[[1]]<-table.cont[,1] @@ -941,7 +941,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist } ##### Bayes - if(any(param %in% c("bayes",txt_bayesian_factors, txt_bayesian_factors)) ) { + if(any(param %in% c("bayes",.dico[["txt_bayesian_factors"]], .dico[["txt_bayesian_factors"]])) ) { modeleBF<-paste0(DV,"~") if(!is.null(cov)){ for(i in 1 : length(cov)){ @@ -970,12 +970,12 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist - if(any(param %in% c( "non param", txt_non_param_model, "Non parametric"))){ + if(any(param %in% c( "non param", .dico[["txt_non_param_model"]], "Non parametric"))){ if(!is.null(between)){ kruskal.test(as.formula( paste0(DV, "~",between[1])), data = data)->KW round(data.frame(KW$statistic,KW$parameter,KW$p.value),4)->KW - names(KW)<-c("H",txt_df,txt_p_dot_val) + names(KW)<-c("H",.dico[["txt_df"]],.dico[["txt_p_dot_val"]]) round((KW$H-nlevels(data[,between])+1)/(length(data[,1])-nlevels(data[,between])),4)->eta if(eta<0.0001) "<0.001"->KW$eta.2.H else KW$eta.2.H @@ -993,7 +993,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist friedman<-friedman.test(as.formula(paste0(DV,"~", within[[1]], "|", id )),data=data) friedman<-round(data.frame(friedman$statistic,friedman$parameter,friedman$p.value),4) friedman$W.de.Kendall<-round(friedman[,1]/(nrow(data)*(nlevels(data[,unlist(within)])-1)),4) - names(friedman)<-c(txt_chi_dot_squared,txt_df,txt_p_dot_val, txt_kendall_w) + names(friedman)<-c(.dico[["txt_chi_dot_squared"]],.dico[["txt_df"]],.dico[["txt_p_dot_val"]], .dico[["txt_kendall_w"]]) Resultats[[.ez.anova.msg("title",45)]][[.ez.anova.msg("title",47)]]<-friedman ans<-frdAllPairsExactTest(y=data[,DV],groups=data[,within], blocks=data[,id], p.adjust = p.adjust) comp<-expand.grid(dimnames(ans$p.value)) @@ -1007,20 +1007,20 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist } - if(any(param %in% c(txt_robust_statistics, "robust", "Robust statistics - might take some time"))){ + if(any(param %in% c(.dico[["txt_robust_statistics"]], "robust", "Robust statistics - might take some time"))){ if(length(between)==1 & is.null(within)){ split(data[,DV], data[,between])->robuste try(unlist(WRS::med1way(robuste,iter = n.boot)), silent=T)->mediane if(class(mediane)!='try-error'){ - names(mediane)<-c("Test", txt_critical_dot_val,txt_p_dot_val) + names(mediane)<-c("Test", .dico[["txt_critical_dot_val"]],.dico[["txt_p_dot_val"]]) Resultats[[.ez.anova.msg("title",50)]][[.ez.anova.msg("title",51)]]<-round(mediane,4) # revoir if(is.list(contrasts)){ contrasts<-contrasts[[1]] cont<-WRS::medpb(robuste,alpha=.05,nboot=n.boot,con=contrasts,bhop=FALSE) - dimnames(cont$output)[[2]]<-c("Num.cont",txt_contrast_dot_val, - txt_p_dot_val,txt_critical_p_corrected,txt_ci_inferior_limit_dot,txt_ci_superior_limit_dot, - txt_adjusted_p_dot_value) + dimnames(cont$output)[[2]]<-c("Num.cont",.dico[["txt_contrast_dot_val"]], + .dico[["txt_p_dot_val"]],.dico[["txt_critical_p_corrected"]],.dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_ci_superior_limit_dot"]], + .dico[["txt_adjusted_p_dot_value"]]) Resultats[[.ez.anova.msg("title",50)]][[.ez.anova.msg("title",42)]]<-cont$output } @@ -1037,7 +1037,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist WRS2::t1way(as.formula(paste0(DV, "~",between)), tr=.2,data=data)->AR1 WRS2::t1waybt(as.formula(paste0(DV, "~",between)), tr=.2, nboot=n.boot,data=data)->AR2 data.frame(AR1[[2]],AR1[[3]],AR1[[1]],AR2[[2]],AR2[[3]],AR2[[4]], AR2[[5]])->AR1 - names(AR1)<-c(txt_df_num,txt_df_denom,"Stat",txt_p_dot_val,txt_var_explained_dot,txt_effect_size_dot,txt_bootstrap_dot_number ) + names(AR1)<-c(.dico[["txt_df_num"]],.dico[["txt_df_denom"]],"Stat",.dico[["txt_p_dot_val"]],.dico[["txt_var_explained_dot"]],.dico[["txt_effect_size_dot"]],.dico[["txt_bootstrap_dot_number"]] ) Resultats[[.ez.anova.msg("title",52)]][[.ez.anova.msg("title",51)]]<-AR1 @@ -1047,7 +1047,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist try(WRS::mcppb20(robuste, tr=.2, nboot=n.boot, con=contrasts),silent=T)->cont2 if(class(cont)!= 'try-error') { cont<-data.frame(cont$psihat[,2],cont$test[,4],cont$test[,5],cont$test[,2],cont$test[,3],cont2$psihat[,4],cont2$psihat[,5],cont2$psihat[,6]) - names(cont)<-c(txt_contrast_dot_val,txt_error_dot_standard,txt_df,"test",txt_critical_dot_threshold,txt_ci_inferior_limit_dot,txt_ci_superior_limit_dot,txt_p_dot_val) + names(cont)<-c(.dico[["txt_contrast_dot_val"]],.dico[["txt_error_dot_standard"]],.dico[["txt_df"]],"test",.dico[["txt_critical_dot_threshold"]],.dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_ci_superior_limit_dot"]],.dico[["txt_p_dot_val"]]) Resultats[[.ez.anova.msg("title",52)]][[.ez.anova.msg("title",42)]] <-cont } @@ -1063,7 +1063,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist try( WRS2::t2way(as.formula(paste0(DV, "~",between[1],"*",between[2])), data=data, tr = 0.2), silent=T)->T2 if(class(T2)!='try-error'){ T2<-matrix(unlist(T2[c(1:6)]), ncol=2, byrow=T) - dimnames(T2)[[2]]<-c(txt_value, txt_p_dot_val) + dimnames(T2)[[2]]<-c(.dico[["txt_value"]], .dico[["txt_p_dot_val"]]) c(names(data[,between]), paste(names(data[,between])[1],":",names(data[,between])[2]))->dimnames(T2)[[1]] Resultats[[.ez.anova.msg("title",52)]][[.ez.anova.msg("title",51)]]<-T2 } @@ -1071,7 +1071,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist WRS2::pbad2way(as.formula(paste0(DV, "~",between[1],"*",between[2])),data=data, est = "mom", nboot = n.boot),silent=T) if(class(mom)!='try-error') { mom<-matrix(unlist(mom[c(2,4,6)]), ncol=1) - dimnames(mom)<-list(c(between, paste0(between[1], ":",between[2])),c(txt_p_dot_val)) + dimnames(mom)<-list(c(between, paste0(between[1], ":",between[2])),c(.dico[["txt_p_dot_val"]])) Resultats[[.ez.anova.msg("title",53)]][[.ez.anova.msg("title",51)]]<-mom } @@ -1079,7 +1079,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist WRS2::pbad2way(as.formula(paste0(DV, "~",between[1],"*",between[2])),data=data, est = "median", nboot = n.boot),silent=T) if(class(mom)!='try-error') { mom<-matrix(unlist(mom[c(2,4,6)]), ncol=1) - dimnames(mom)<-list(c(between, paste0(between[1], ":",between[2])),c(txt_p_dot_val)) + dimnames(mom)<-list(c(between, paste0(between[1], ":",between[2])),c(.dico[["txt_p_dot_val"]])) Resultats[[.ez.anova.msg("title",50)]][[.ez.anova.msg("title",51)]]<-mom } @@ -1088,7 +1088,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist comp<-data.frame(psihat=c(mediane[[1]][[1]][[1]], mediane[[1]][[2]][[1]] , mediane[[1]][[3]][[1]]), valeur.p=c(mediane[[1]][[1]][[3]], mediane[[1]][[2]][[3]] , mediane[[1]][[3]][[3]])) med<-cbind(comp, matrix(c(mediane[[1]][[1]][[2]], mediane[[1]][[2]][[2]] , mediane[[1]][[3]][[2]]), ncol=2, byrow=T)) - names(med)<-c("psihat", txt_p_dot_val, txt_inferior_limit,txt_ci_superior_limit) + names(med)<-c("psihat", .dico[["txt_p_dot_val"]], .dico[["txt_inferior_limit"]],.dico[["txt_ci_superior_limit"]]) dimnames(med)[[1]]<-names(mediane[[2]]) Resultats[[.ez.anova.msg("title",53)]][[.ez.anova.msg("title",42)]]<-med } @@ -1098,7 +1098,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist comp<-data.frame(psihat=c(mediane[[1]][[1]][[1]], mediane[[1]][[2]][[1]] , mediane[[1]][[3]][[1]]), valeur.p=c(mediane[[1]][[1]][[3]], mediane[[1]][[2]][[3]] , mediane[[1]][[3]][[3]])) med<-cbind(comp, matrix(c(mediane[[1]][[1]][[2]], mediane[[1]][[2]][[2]] , mediane[[1]][[3]][[2]]), ncol=2, byrow=T)) - names(med)<-c("psihat", txt_p_dot_val, txt_inferior_limit,txt_ci_superior_limit) + names(med)<-c("psihat", .dico[["txt_p_dot_val"]], .dico[["txt_inferior_limit"]],.dico[["txt_ci_superior_limit"]]) dimnames(med)[[1]]<-names(mediane[[2]]) Resultats[[.ez.anova.msg("title",50)]][[.ez.anova.msg("title",42)]]<-med } @@ -1118,6 +1118,7 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist if(class(ANOVA.tr)!='try-error'){ ANOVA.tr<-round(data.frame(txt_test_dot_val= ANOVA.tr$test,txt_df1=ANOVA.tr$df1, txt_df2=ANOVA.tr$df2,txt_p_dot_val=ANOVA.tr$p.value),4) + names(ANOVA.tr)<-c(.dico[["txt_test_dot_val"]], .dico[["txt_df1"]], .dico[["txt_df2"]], .dico[["txt_p_dot_val"]]) Resultats[[.ez.anova.msg("title",52)]][[.ez.anova.msg("title",51)]]<-ANOVA.tr if((nlevels(data[,within[[1]]]))>2) { @@ -1136,13 +1137,13 @@ if(reshape.data) Resultats$call.reshape<-as.character(ez.history[[length(ez.hist tronquees2<-matrix(unlist(unlist(tronquees)[c(1:12)]),ncol=4, byrow=T) tronquees2<-data.frame(tronquees2) rownames(tronquees2)<-c(tronquees$varnames[2] , tronquees$varnames[3], paste0(tronquees$varnames[2],":",tronquees$varnames[3])) - names(tronquees2)<-c("F", txt_p_dot_val, txt_df1, txt_df2) + names(tronquees2)<-c("F", .dico[["txt_p_dot_val"]], .dico[["txt_df1"]], .dico[["txt_df2"]]) Resultats[[.ez.anova.msg("title",52)]][[.ez.anova.msg("title",51)]] <-tronquees2 WRS2::sppba(modeleR, data[,id], data=data, est = "mom", avg = TRUE, nboot = n.boot, MDIS = FALSE)->MoMa WRS2::sppbb(modeleR, data[,id], data=data, est = "mom", nboot = n.boot)->MoMb WRS2::sppbi(modeleR, data[,id], data=data, est = "mom", nboot = n.boot)->MoMi MoM<-data.frame("effet"= c(between,within[[1]],"interaction"), txt_p_dot_val=c(MoMa$p.value,MoMb$p.value, MoMi$p.value) ) - names(MoM) <- c(txt_effect,txt_p_dot_val) + names(MoM) <- c(.dico[["txt_effect"]],.dico[["txt_p_dot_val"]]) Resultats[[.ez.anova.msg("title",54)]][[.ez.anova.msg("title",51)]] <-MoM }else Resultats[[.ez.anova.msg("title",10)]]<-.ez.anova.msg("msg",33) } diff --git a/R/ez.cfa.R b/R/ez.cfa.R index e5ff1f1..95f50b8 100644 --- a/R/ez.cfa.R +++ b/R/ez.cfa.R @@ -1,14 +1,14 @@ ez.cfa <- - function(modele=NULL, X=NULL, data=NULL,ord=NULL, outlier=txt_complete_dataset,imp="rm", output="default", info=T, sauvegarde=F, mimic=NULL, fixed.x="default", missing="default",information="default", zero.keep.margins="default",zero.add=c(0.5,0), + function(modele=NULL, X=NULL, data=NULL,ord=NULL, outlier=.dico[["txt_complete_dataset"]],imp="rm", output="default", info=T, sauvegarde=F, mimic=NULL, fixed.x="default", missing="default",information="default", zero.keep.margins="default",zero.add=c(0.5,0), estimator="ML",group=NULL, test="standard",se="standard",std.ov=T, orthogonal=F, likelihood="default", link="probit",int.ov.free=FALSE, int.lv.free=FALSE, std.lv=FALSE, n.boot=1000, group.w.free=F, - group.equal=c("loadings", "intercepts", "means", "thresholds", txt_regressions, "residuals", "residual.covariances", "lv.variances" , "lv.covariances")){ + group.equal=c("loadings", "intercepts", "means", "thresholds", .dico[["txt_regressions"]], "residuals", "residual.covariances", "lv.variances" , "lv.covariances")){ # modele : lavaan modele if X is null # data : dataframe # X : character. names of the variables if modele is null # LV : Vector. names of LV=atent Variables # ord: Character. Vector of ordered variables among X - # outlier : should outliers be detected and removed on Mahalanobis distance ? (txt_without_outliers) or not (txt_complete_dataset) + # outlier : should outliers be detected and removed on Mahalanobis distance ? (.dico[["txt_without_outliers"]]) or not (.dico[["txt_complete_dataset"]]) # imp : How must missing data be dealt :"rm"= remove, "mean" = impute mean, "median"=impute median, "amelia"=use amelia algorithm for imputation. # output : character vector. List of output that has to be shown. # info : logical. Should information be printed in the console ? @@ -24,29 +24,29 @@ ez.cfa <- if(!is.null(modele)){ semPlot.modele<-try(semPlotModel_lavaanModel(modele)) if(class(semPlot.modele)=='try-error'){ - msgBox(desc_model_seems_incorrect_could_not_be_created) + msgBox(.dico[["desc_model_seems_incorrect_could_not_be_created"]]) return(NULL) } semPaths(semPlot.modele, edge.label.cex = 0.65,edge.color="black", exoVar = FALSE,exoCov =T, cex=0.5) - cat (ask_press_enter_to_continue) + cat (.dico[["ask_press_enter_to_continue"]]) line <- readline() - dlgMessage(ask_is_model_correct, "yesno")$res->suppression + dlgMessage(.dico[["ask_is_model_correct"]], "yesno")$res->suppression if(suppression=="no") return( Lav.modele(X=X, modele=NULL, LV=NULL, info=T)) return(modele) } if(is.null(LV) && length(X)>3) { - if(info) writeLines(ask_latent_variables_number) + if(info) writeLines(.dico[["ask_latent_variables_number"]]) nF<-NA while(!is.numeric(nF)) { - if(info) writeLines(ask_latent_variables_number) - nF <- dlgInput(ask_factors_number, 2)$res + if(info) writeLines(.dico[["ask_latent_variables_number"]]) + nF <- dlgInput(.dico[["ask_factors_number"]], 2)$res if(length(nF)==0) return(NULL) strsplit(nF, ":")->nF tail(nF[[1]],n=1)->nF as.numeric(nF)->nF if(any((nF%%1==0)%in% c(FALSE, NA))|| nF<0 || nF>length(X) ){ - msgBox(desc_facotrs_must_be_positive_int_inferior_to_variables_num) + msgBox(.dico[["desc_facotrs_must_be_positive_int_inferior_to_variables_num"]]) nF<-NA } }} else if(!is.null(LV)) nF<-length(LV) else nF<-1 @@ -54,11 +54,11 @@ ez.cfa <- X->reste list()->modele2 for(i in 1:nF){ - if(is.null(LV[i])) {dlgInput(paste(txt_latent_variable_name,i, "?"), paste(txt_factor,i, sep="."))$res->noms + if(is.null(LV[i])) {dlgInput(paste(.dico[["txt_latent_variable_name"]],i, "?"), paste(.dico[["txt_factor"]],i, sep="."))$res->noms if(length(noms)==0) return(Lav.modele(X=X, LV=NULL)) strsplit(noms, ":")->noms tail(noms[[1]],n=1)->noms} else noms<-LV[i] - title<-paste(desc_manifest_variables_of, noms) + title<-paste(.dico[["desc_manifest_variables_of"]], noms) if(i==nF) O1<-reste else O1<- dlgList(reste, preselect=NULL, multiple = TRUE, title=title)$res O2<-c(O2,O1) setdiff(reste,O2)->reste @@ -73,76 +73,76 @@ ez.cfa <- semPaths(semPlot.modele, edge.label.cex = 0.65,edge.color="black", exoVar = FALSE,exoCov =T, cex=0.5) } - cat (ask_press_enter_to_continue) + cat (.dico[["ask_press_enter_to_continue"]]) line <- readline() - dlgMessage(ask_is_model_correct, "yesno")$res->suppression + dlgMessage(.dico[["ask_is_model_correct"]], "yesno")$res->suppression if(suppression=="no") return( Lav.modele(X=X, modele=NULL, LV=NULL, info=T)) return(modele) } .ez.lavaan.options<-function(modele=NULL, data=NULL, X=NULL, info=TRUE, opt.list=NULL, dial=T, imp=NULL, outlier=NULL,output=NULL){ if(dial || is.null(opt.list$mimic) || !opt.list$mimic%in% c("default", "Mplus", "EQS")){dial<-T - if(info) writeLines(ask_specify_all_parameters_or_imitate_specific_software) - opt.list$mimic<-dlgList(c("default", "Mplus", "EQS"), preselect="default", multiple = FALSE, title=ask_imitate)$res + if(info) writeLines(.dico[["ask_specify_all_parameters_or_imitate_specific_software"]]) + opt.list$mimic<-dlgList(c("default", "Mplus", "EQS"), preselect="default", multiple = FALSE, title=.dico[["ask_imitate"]])$res if(length(opt.list$mimic)==0) return(NULL) } if(dial){ if(opt.list$mimic=="default"){ - options2<-c(txt_exogenous_fixed_variables, txt_cfa_information_default, txt_cfa_continuity_correction_zero_keep_margins_default, - txt_likelihood_only_for_estimator) + options2<-c(.dico[["txt_exogenous_fixed_variables"]], .dico[["txt_cfa_information_default"]], .dico[["txt_cfa_continuity_correction_zero_keep_margins_default"]], + .dico[["txt_likelihood_only_for_estimator"]]) } else options2<-c() - options<-c(txt_cfa_estimator_ml_default, txt_cfa_groups_null_default, txt_cfa_test_standard_default, txt_cfa_standard_error_default, txt_cfa_observed_variabes_standardization_true_default, - txt_factors_ortho, txt_link_only_for_estimator, - txt_observed_variables_intercept, txt_latent_variables_intercept, txt_exogenous_fixed_variables, - txt_cfa_latent_variables_indicators_estimates_true_default, options2) + options<-c(.dico[["txt_cfa_estimator_ml_default"]], .dico[["txt_cfa_groups_null_default"]], .dico[["txt_cfa_test_standard_default"]], .dico[["txt_cfa_standard_error_default"]], .dico[["txt_cfa_observed_variabes_standardization_true_default"]], + .dico[["txt_factors_ortho"]], .dico[["txt_link_only_for_estimator"]], + .dico[["txt_observed_variables_intercept"]], .dico[["txt_latent_variables_intercept"]], .dico[["txt_exogenous_fixed_variables"]], + .dico[["txt_cfa_latent_variables_indicators_estimates_true_default"]], options2) - if(info) writeLines(ask_which_options_to_specify) - options<-dlgList(c(txt_keep_default_values, options), preselect=c(txt_cfa_estimator_ml_default,txt_cfa_test_standard_default, txt_cfa_standard_error_default), multiple = TRUE, title=ask_which_options)$res + if(info) writeLines(.dico[["ask_which_options_to_specify"]]) + options<-dlgList(c(.dico[["txt_keep_default_values"]], options), preselect=c(.dico[["txt_cfa_estimator_ml_default"]],.dico[["txt_cfa_test_standard_default"]], .dico[["txt_cfa_standard_error_default"]]), multiple = TRUE, title=.dico[["ask_which_options"]])$res if(length(options)==0) return(NULL) - if(options==txt_keep_default_values) return(list(mimic="default", fixed.x="default", missing="default",information="default", zero.keep.margins="default",zero.add=c(0.5,0), + if(options==.dico[["txt_keep_default_values"]]) return(list(mimic="default", fixed.x="default", missing="default",information="default", zero.keep.margins="default",zero.add=c(0.5,0), estimator="ml",group=NULL, test="standard",se="standard",std.ov=T, orthogonal=F, likelihood="default", link="probit",int.ov.free=FALSE, int.lv.free=FALSE,fixed.x="default", std.lv=FALSE, n.boot=1000, group.w.free=F, - group.equal=c("loadings", "intercepts", "means", "thresholds", txt_regressions, "residuals", "residual.covariances", + group.equal=c("loadings", "intercepts", "means", "thresholds", .dico[["txt_regressions"]], "residuals", "residual.covariances", "lv.variances" , "lv.covariances"))) } else options<-NULL - if(any(options==txt_cfa_estimator_ml_default)|is.null(opt.list$estimator) || length(opt.list$estimator)!=1|| + if(any(options==.dico[["txt_cfa_estimator_ml_default"]])|is.null(opt.list$estimator) || length(opt.list$estimator)!=1|| try(opt.list$estimator %in%c("ML","GLS", "WLS", "ULS", "DWLS", "MLM","MLMV","MLMVS","MLF", "MLR", "WLSM","WLSMV", "ULSM", "ULSMV" ),silent=T)!=T){ - if(info){ writeLines(desc_wls_corresponds_to_adf_plus_explaination_other_estimators) - abb<-data.frame(abb=c("ML","GLS", "WLS", "ULS", "DWLS"), nom=c(txt_max_likelihood,txt_less_square_generalized,txt_less_square_pondered,txt_less_square_not_pondered,txt_less_square_diagonally_pondered)) + if(info){ writeLines(.dico[["desc_wls_corresponds_to_adf_plus_explaination_other_estimators"]]) + abb<-data.frame(abb=c("ML","GLS", "WLS", "ULS", "DWLS"), nom=c(.dico[["txt_max_likelihood"]],.dico[["txt_less_square_generalized"]],.dico[["txt_less_square_pondered"]],.dico[["txt_less_square_not_pondered"]],.dico[["txt_less_square_diagonally_pondered"]])) print(abb) } - opt.list$estimator<-dlgList(c("ML","GLS", "WLS", "ULS", "DWLS", "MLM","MLMV","MLMVS","MLF", "MLR", "WLSM","WLSMV", "ULSM", "ULSMV" ), multiple = FALSE, title=ask_which_estimator)$res + opt.list$estimator<-dlgList(c("ML","GLS", "WLS", "ULS", "DWLS", "MLM","MLMV","MLMVS","MLF", "MLR", "WLSM","WLSMV", "ULSM", "ULSMV" ), multiple = FALSE, title=.dico[["ask_which_estimator"]])$res if(length(opt.list$estimator)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_cfa_test_standard_default) || length(opt.list$test)!=1 || !opt.list$test%in% c("standard", "Satorra.Bentler", "Yuan.Bentler", "mean.var.adjusted", + if(any(options==.dico[["txt_cfa_test_standard_default"]]) || length(opt.list$test)!=1 || !opt.list$test%in% c("standard", "Satorra.Bentler", "Yuan.Bentler", "mean.var.adjusted", "scaled.shifted", "bootstrap","Bollen.Stine")){ - if(info) writeLines(ask_which_test) - opt.list$test<-dlgList(c("standard", "Satorra.Bentler", "Yuan.Bentler", "mean.var.adjusted","scaled.shifted", "bootstrap","Bollen.Stine"), multiple = FALSE, title=ask_which_estimator)$res + if(info) writeLines(.dico[["ask_which_test"]]) + opt.list$test<-dlgList(c("standard", "Satorra.Bentler", "Yuan.Bentler", "mean.var.adjusted","scaled.shifted", "bootstrap","Bollen.Stine"), multiple = FALSE, title=.dico[["ask_which_estimator"]])$res if(length(opt.list$test)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } if(opt.list$test%in%c("boot","bootstrap","Bollen.Stine") &&!is.null(opt.list$n.boot) && ((class(opt.list$n.boot)!="numeric" & class(opt.list$n.boot)!="integer") || opt.list$n.boot%%1!=0 || opt.list$n.boot<1)){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) opt.list$n.boot<-NULL } if(dial & opt.list$test%in%c("boot","bootstrap","Bollen.Stine") || is.null(opt.list$n.boot) & opt.list$test%in%c("boot","bootstrap","Bollen.Stine")) { while(is.null(opt.list$n.boot)){ - writeLines(ask_bootstrap_numbers_1_for_none) - n.boot<-dlgInput(ask_bootstraps_number, 1)$res + writeLines(.dico[["ask_bootstrap_numbers_1_for_none"]]) + n.boot<-dlgInput(.dico[["ask_bootstraps_number"]], 1)$res if(length(n.boot)==0) {Resultats<-Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} strsplit(n.boot, ":")->n.boot tail(n.boot[[1]],n=1)->n.boot as.numeric(n.boot)->opt.list$n.boot if(is.na(opt.list$n.boot) || opt.list$n.boot%%1!=0 || opt.list$n.boot<1){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) opt.list$n.boot<-NULL } } @@ -152,41 +152,41 @@ ez.cfa <- if(opt.list$test%in%c("boot","bootstrap","Bollen.Stine")) se1<-c("standard","first.order", "robust", "bootstrap","none" ) else se1<-c("standard","first.order", "robust", "none" ) #if(any(options=="erreur standard [se]") || is.null(opt.list$se) || !opt.list$se%in%se1) { # problem here with original version? erreur standard [se] is not defined elsewhere - if(any(options==txt_cfa_standard_error_default) || is.null(opt.list$se) || !opt.list$se%in%se1) { - if(info) writeLines(ask_how_standard_error_must_be_estimated) - opt.list$se<-dlgList(se1, multiple = FALSE, title=ask_standard_error)$res + if(any(options==.dico[["txt_cfa_standard_error_default"]]) || is.null(opt.list$se) || !opt.list$se%in%se1) { + if(info) writeLines(.dico[["ask_how_standard_error_must_be_estimated"]]) + opt.list$se<-dlgList(se1, multiple = FALSE, title=.dico[["ask_standard_error"]])$res if(length(opt.list$se)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_cfa_groups_null_default) || !is.null(opt.list$group)){ - msg2<-ask_chose_defining_groups - .var.type(X=opt.list$group, info=T, data=data, type="factor", message=msg2,multiple=T, title=ask_group_variable, out=X)->group + if(any(options==.dico[["txt_cfa_groups_null_default"]]) || !is.null(opt.list$group)){ + msg2<-.dico[["ask_chose_defining_groups"]] + .var.type(X=opt.list$group, info=T, data=data, type="factor", message=msg2,multiple=T, title=.dico[["ask_group_variable"]], out=X)->group if(is.null(group)){ Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats) } group$data->data group$X->opt.list$group - if(dial|| any(opt.list$group.equal %in% c("loadings", "intercepts","means","thresholds",txt_regressions,"residuals","residual.covariances","lv.variances", "lv.covariances"))==FALSE){ - if(info) writeLines(ask_which_constant_parameters) - opt.list$group.equal<-dlgList(c("loadings", "intercepts","means","thresholds",txt_regressions,"residuals","residual.covariances","lv.variances", "lv.covariances"), multiple = T, - preselect=c("loadings", "intercepts","means","thresholds",txt_regressions,"residuals","residual.covariances","lv.variances", "lv.covariances"), title=ask_constant_parameters)$res + if(dial|| any(opt.list$group.equal %in% c("loadings", "intercepts","means","thresholds",.dico[["txt_regressions"]],"residuals","residual.covariances","lv.variances", "lv.covariances"))==FALSE){ + if(info) writeLines(.dico[["ask_which_constant_parameters"]]) + opt.list$group.equal<-dlgList(c("loadings", "intercepts","means","thresholds",.dico[["txt_regressions"]],"residuals","residual.covariances","lv.variances", "lv.covariances"), multiple = T, + preselect=c("loadings", "intercepts","means","thresholds",.dico[["txt_regressions"]],"residuals","residual.covariances","lv.variances", "lv.covariances"), title=.dico[["ask_constant_parameters"]])$res if(length(opt.list$group.equal)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats) }} # ecrase group equal puisque aa libere les group sur cette contraintes ==> utilite ? - #group.partial<-dlgList(c("loadings", "intercepts","means","thresholds",txt_regressions,"residuals","residual.covariances","lv.variances", "lv.covariances")) - if(info) writeLines(ask_are_frequences_free_parameters) - opt.list$group.w.free<-dlgList(c(TRUE, FALSE), multiple=F, preselect=FALSE, title=ask_freq_constance)$res + #group.partial<-dlgList(c("loadings", "intercepts","means","thresholds",.dico[["txt_regressions"]],"residuals","residual.covariances","lv.variances", "lv.covariances")) + if(info) writeLines(.dico[["ask_are_frequences_free_parameters"]]) + opt.list$group.w.free<-dlgList(c(TRUE, FALSE), multiple=F, preselect=FALSE, title=.dico[["ask_freq_constance"]])$res if(length(opt.list$group.w.free)==0) {Resultats<.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats) } } ### zero.keep.margins - if(any(options==txt_cfa_continuity_correction_zero_keep_margins_default) || is.null(opt.list$zero.keep.margins)||(!is.logical(opt.list$zero.keep.margins) & opt.list$zero.keep.margins!="default")){ - if(info) writeLines(ask_add_a_value_to_empty_cells) - opt.list$zero.keep.margins<-dlgList(c(TRUE, FALSE,"default"), preselect="default", multiple = FALSE, title=ask_empty_cells)$res + if(any(options==.dico[["txt_cfa_continuity_correction_zero_keep_margins_default"]]) || is.null(opt.list$zero.keep.margins)||(!is.logical(opt.list$zero.keep.margins) & opt.list$zero.keep.margins!="default")){ + if(info) writeLines(.dico[["ask_add_a_value_to_empty_cells"]]) + opt.list$zero.keep.margins<-dlgList(c(TRUE, FALSE,"default"), preselect="default", multiple = FALSE, title=.dico[["ask_empty_cells"]])$res if(length(opt.list$zero.keep.margins)==0) { Resultats<-Resultats<.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats) @@ -195,30 +195,30 @@ ez.cfa <- if( opt.list$zero.keep.margins==TRUE){ if(!is.null(opt.list$zero.add) && ((class(opt.list$zero.add)!="numeric" ) || any( opt.list$zero.add<0) || any(opt.list$zero.add>1))){ - msgBox(txt_correction_for_polyc_corr_must_be_between_zero_and_one) + msgBox(.dico[["txt_correction_for_polyc_corr_must_be_between_zero_and_one"]]) opt.list$zero.add<-NULL } while(is.null(opt.list$zero.add)){ - writeLines(ask_2x2_table_value) - zero.add1<-dlgInput(ask_2x2_table, 0.5)$res + writeLines(.dico[["ask_2x2_table_value"]]) + zero.add1<-dlgInput(.dico[["ask_2x2_table"]], 0.5)$res if(length(zero.add1)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} strsplit(zero.add1, ":")->zero.add1 tail(zero.add1[[1]],n=1)->zero.add1 as.numeric(zero.add1)->zero.add1 if(is.na(zero.add1) || zero.add1<0 || zero.add1>1){ - msgBox(desc_value_must_be_between_zero_and_one) + msgBox(.dico[["desc_value_must_be_between_zero_and_one"]]) opt.list$zero.add<-NA} else{ - writeLines(ask_bigger_tables_value) + writeLines(.dico[["ask_bigger_tables_value"]]) #zero.add2<-dlgInput("tableau > 2x2 ?", 0)$res - zero.add2<-dlgInput(ask_2x2_table, 0)$res + zero.add2<-dlgInput(.dico[["ask_2x2_table"]], 0)$res if(length(zero.add2)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} strsplit(zero.add2, ":")->zero.add2 tail(zero.add2[[1]],n=1)->zero.add2 as.numeric(zero.add2)->zero.add2 if(is.na(zero.add2) || zero.add2<0 || zero.add2>1){ - msgBox(desc_value_must_be_between_zero_and_one) + msgBox(.dico[["desc_value_must_be_between_zero_and_one"]]) opt.list$zero.add<-NA} } opt.list$zero.add<-c(zero.add1,zero.add2) @@ -228,70 +228,70 @@ ez.cfa <- ### fin zero.keep.margins - if(any(options==txt_likelihood_only_for_estimator) & opt.list$mimic=="default" & opt.list$estimator=="ML" ||is.null(opt.list$likelihood) || length(opt.list$likelihood)!=1 || try(opt.list$likelihood%in%c("wishart","normal", "default" ),silent=T)!=T) { - if(info) writeLines(ask_specify_likelihood) - opt.list$likelihood<-dlgList(c("wishart","normal", "default" ), multiple=F, preselect="default", title=ask_likelihood)$res # depend de mimic + if(any(options==.dico[["txt_likelihood_only_for_estimator"]]) & opt.list$mimic=="default" & opt.list$estimator=="ML" ||is.null(opt.list$likelihood) || length(opt.list$likelihood)!=1 || try(opt.list$likelihood%in%c("wishart","normal", "default" ),silent=T)!=T) { + if(info) writeLines(.dico[["ask_specify_likelihood"]]) + opt.list$likelihood<-dlgList(c("wishart","normal", "default" ), multiple=F, preselect="default", title=.dico[["ask_likelihood"]])$res # depend de mimic if(length(opt.list$likelihood)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_link_only_for_estimator) & opt.list$estimator=="MML" ||length(opt.list$link)!=1 || try(opt.list$link%in%c("logit","probit" ),silent=T)!=T ){ - if(info) writeLines(ask_family) - opt.list$link<-dlgList(c("logit","probit" ), multiple=F, preselect=FALSE, title=ask_distribution)$res + if(any(options==.dico[["txt_link_only_for_estimator"]]) & opt.list$estimator=="MML" ||length(opt.list$link)!=1 || try(opt.list$link%in%c("logit","probit" ),silent=T)!=T ){ + if(info) writeLines(.dico[["ask_family"]]) + opt.list$link<-dlgList(c("logit","probit" ), multiple=F, preselect=FALSE, title=.dico[["ask_distribution"]])$res if(length(opt.list$link)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_cfa_information_default) ||is.null(opt.list$information) || try(opt.list$information%in%c("expected","observed", "default" ),silent=T)!=T ){ - if(info) writeLines(ask_which_information_matrix_for_standard_error_estimation) - opt.list$information<-dlgList(c("expected","observed", "default" ), multiple=F, preselect=FALSE, title=ask_information_matrix)$res + if(any(options==.dico[["txt_cfa_information_default"]]) ||is.null(opt.list$information) || try(opt.list$information%in%c("expected","observed", "default" ),silent=T)!=T ){ + if(info) writeLines(.dico[["ask_which_information_matrix_for_standard_error_estimation"]]) + opt.list$information<-dlgList(c("expected","observed", "default" ), multiple=F, preselect=FALSE, title=.dico[["ask_information_matrix"]])$res if(length(opt.list$information)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_exogenous_fixed_variables) ||length(opt.list$fixed.x)!=1 || (!is.logical(opt.list$fixed.x) & opt.list$fixed.x!="default") ){ - if(info) writeLines(desc_if_true_covariates_as_fixed) - opt.list$fixed.x<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=ask_fixed_covariables)$res + if(any(options==.dico[["txt_exogenous_fixed_variables"]]) ||length(opt.list$fixed.x)!=1 || (!is.logical(opt.list$fixed.x) & opt.list$fixed.x!="default") ){ + if(info) writeLines(.dico[["desc_if_true_covariates_as_fixed"]]) + opt.list$fixed.x<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=.dico[["ask_fixed_covariables"]])$res if(length(opt.list$fixed.x)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_factors_ortho) ||length(opt.list$orthogonal)!=1 || !is.logical(opt.list$orthogonal) ){ - if(info) writeLines(ask_correlated_or_orthogonal_factors) - opt.list$orthogonal<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=ask_factors_ortho)$res + if(any(options==.dico[["txt_factors_ortho"]]) ||length(opt.list$orthogonal)!=1 || !is.logical(opt.list$orthogonal) ){ + if(info) writeLines(.dico[["ask_correlated_or_orthogonal_factors"]]) + opt.list$orthogonal<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=.dico[["ask_factors_ortho"]])$res if(length(opt.list$orthogonal)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_cfa_observed_variabes_standardization_true_default) ||length(opt.list$std.ov)!=1 || !is.logical(opt.list$std.ov) ){ - if(info) writeLines(ask_standardize_obs_variables_before) - opt.list$std.ov<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=ask_standardization)$res + if(any(options==.dico[["txt_cfa_observed_variabes_standardization_true_default"]]) ||length(opt.list$std.ov)!=1 || !is.logical(opt.list$std.ov) ){ + if(info) writeLines(.dico[["ask_standardize_obs_variables_before"]]) + opt.list$std.ov<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=.dico[["ask_standardization"]])$res if(length(opt.list$std.ov)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } ##### - if(any(options==txt_observed_variables_intercept) ||length(opt.list$int.ov.free)!=1 || !is.logical(opt.list$int.ov.free) ){ - if(info) writeLines(ask_should_intercept_of_obs_variables_be_fixed_to_zero) - opt.list$int.ov.free<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=ask_observed_variables_intercept_zero)$res + if(any(options==.dico[["txt_observed_variables_intercept"]]) ||length(opt.list$int.ov.free)!=1 || !is.logical(opt.list$int.ov.free) ){ + if(info) writeLines(.dico[["ask_should_intercept_of_obs_variables_be_fixed_to_zero"]]) + opt.list$int.ov.free<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=.dico[["ask_observed_variables_intercept_zero"]])$res if(length(opt.list$int.ov.free)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_latent_variables_intercept) ||length(opt.list$int.lv.free)!=1 || !is.logical(opt.list$int.lv.free) ){ - if(info) writeLines(ask_should_intercept_of_latent_variable_be_fixed_to_zero) - opt.list$int.lv.free<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=ask_latent_variables_intercept_zero)$res + if(any(options==.dico[["txt_latent_variables_intercept"]]) ||length(opt.list$int.lv.free)!=1 || !is.logical(opt.list$int.lv.free) ){ + if(info) writeLines(.dico[["ask_should_intercept_of_latent_variable_be_fixed_to_zero"]]) + opt.list$int.lv.free<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=.dico[["ask_latent_variables_intercept_zero"]])$res if(length(opt.list$int.lv.free)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } - if(any(options==txt_cfa_latent_variables_indicators_estimates_true_default) ||length(opt.list$std.lv)!=1 || !is.logical(opt.list$std.lv) ){ - if(info) writeLines(desc_if_true_latent_residuals_one) - opt.list$std.lv<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=ask_standardization_vl)$res + if(any(options==.dico[["txt_cfa_latent_variables_indicators_estimates_true_default"]]) ||length(opt.list$std.lv)!=1 || !is.logical(opt.list$std.lv) ){ + if(info) writeLines(.dico[["desc_if_true_latent_residuals_one"]]) + opt.list$std.lv<-dlgList(c(TRUE, FALSE ), multiple=F, preselect=FALSE, title=.dico[["ask_standardization_vl"]])$res if(length(opt.list$std.lv)==0) {Resultats<-.ez.lavaan.options(X=X, data=data, opt.list=opt.list) return(Resultats)} } @@ -317,43 +317,43 @@ ez.cfa <- if(is.null(modele)){ - msg3<-ask_chose_manifest_variables_at_least_three + msg3<-.dico[["ask_chose_manifest_variables_at_least_three"]] - X<-.var.type(X=X, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=T, title=txt_variables, out=NULL) + X<-.var.type(X=X, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=T, title=.dico[["txt_variables"]], out=NULL) data<-X$data X<-X$X if(is.null(X) || length(X)<3) return(NULL) - if(dial || length(outlier)>1 || outlier %in% c(txt_complete_dataset, txt_without_outliers) ==FALSE){ - if(info) writeLines(ask_analysis_on_complete_data_or_remove_outliers) - if(info) writeLines(desc_outliers_identified_on_mahalanobis) - outlier<- dlgList(c(txt_complete_dataset, txt_without_outliers), preselect=txt_complete_dataset,multiple = FALSE, title=ask_results_desired)$res + if(dial || length(outlier)>1 || outlier %in% c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]) ==FALSE){ + if(info) writeLines(.dico[["ask_analysis_on_complete_data_or_remove_outliers"]]) + if(info) writeLines(.dico[["desc_outliers_identified_on_mahalanobis"]]) + outlier<- dlgList(c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]), preselect=.dico[["txt_complete_dataset"]],multiple = FALSE, title=.dico[["ask_results_desired"]])$res if(length(outlier)==0) { Resultats<-cfa.in() return(Resultats)} } - if(outlier==txt_without_outliers){ + if(outlier==.dico[["txt_without_outliers"]]){ inf<-VI.multiples(data,X) - Resultats[[txt_labeled_outliers]]<-inf[[txt_labeled_outliers]] + Resultats[[.dico[["txt_labeled_outliers"]]]]<-inf[[.dico[["txt_labeled_outliers"]]]] data<-inf$data } if(dial){ - if(info) writeLines(ask_variables_type_correlations) - if(length(unique(unlist(data[,X])))<9) {type<-dlgList(c(txt_dichotomic_ordinal,txt_continuous, "mixte"), preselect=NULL, multiple = FALSE, title=ask_variables_type)$res}else { - type<-dlgList(c(txt_continuous, "mixte"), preselect=NULL, multiple = FALSE, title=ask_variables_type)$res + if(info) writeLines(.dico[["ask_variables_type_correlations"]]) + if(length(unique(unlist(data[,X])))<9) {type<-dlgList(c(.dico[["txt_dichotomic_ordinal"]],.dico[["txt_continuous"]], "mixte"), preselect=NULL, multiple = FALSE, title=.dico[["ask_variables_type"]])$res}else { + type<-dlgList(c(.dico[["txt_continuous"]], "mixte"), preselect=NULL, multiple = FALSE, title=.dico[["ask_variables_type"]])$res } if(length(type)==0) {Resultats<-cfa.in() return(Resultats)} - } else{if(is.null(ord)) type<-txt_continuous else type<-txt_dichotomic_ordinal + } else{if(is.null(ord)) type<-.dico[["txt_continuous"]] else type<-.dico[["txt_dichotomic_ordinal"]] } - if(type!=txt_continuous){ + if(type!=.dico[["txt_continuous"]]){ if(type=="mixte") { - if(info) writeLines(ask_ordinal_variables) - ord<-dlgList(X, multiple = TRUE, title=ask_ordinal_variables)$res + if(info) writeLines(.dico[["ask_ordinal_variables"]]) + ord<-dlgList(X, multiple = TRUE, title=.dico[["ask_ordinal_variables"]])$res if(length(ord)==0) {Resultats<-cfa.in() return(Resultats)} }else ord<-X @@ -371,9 +371,9 @@ ez.cfa <- } } if(any(is.na(data[,X]))) { - if(is.null(imp)) {msgBox(ask_missing_values_detected_what_to_do) - imp<- dlgList(c(txt_do_nothing_keep_all_obs, txt_delete_observations_with_missing_values,txt_replace_by_median,txt_multiple_imputation_amelia), - preselect=FALSE, multiple = TRUE, title=ask_missing_values_treatment)$res} + if(is.null(imp)) {msgBox(.dico[["ask_missing_values_detected_what_to_do"]]) + imp<- dlgList(c(.dico[["txt_do_nothing_keep_all_obs"]], .dico[["txt_delete_observations_with_missing_values"]],.dico[["txt_replace_by_median"]],.dico[["txt_multiple_imputation_amelia"]]), + preselect=FALSE, multiple = TRUE, title=.dico[["ask_missing_values_treatment"]])$res} if(length(imp)==0){ Resultats<-cfa.in() return(Resultats) @@ -392,16 +392,16 @@ ez.cfa <- } - if(dial || class(output)!="character"|| any(!output%in% c("default", txt_default_outputs, "parEst", txt_estimated_parameters, "parSt", txt_standardized_parameters,txt_covariance_matrix_adjusted, "fitted.cov", - txt_residue_standardized, "res.St","res.Unst",txt_non_standardized_residuals,"vcov",txt_covariance_matrix_estimated, - "AIC", "BIC", txt_adequation_measures,"fitM", txt_inspect_initial_values, "start", txt_inspect_model_matrices, - "modmat", txt_inspect_model_representation, "modrep"))==TRUE){ - if(info) writeLines(ask_which_results_warning_on_default_output) - output<-c( txt_default_outputs, txt_estimated_parameters, txt_standardized_parameters,txt_covariance_matrix_adjusted, - txt_residue_standardized, txt_non_standardized_residuals,txt_covariance_matrix_estimated,"AIC", "BIC", txt_adequation_measures, - txt_inspect_initial_values, txt_inspect_model_matrices, txt_inspect_model_representation) - if(info) writeLines(ask_which_output_results) - output<- dlgList(output, preselect=txt_default_outputs, multiple = TRUE, title=ask_results_output)$res + if(dial || class(output)!="character"|| any(!output%in% c("default", .dico[["txt_default_outputs"]], "parEst", .dico[["txt_estimated_parameters"]], "parSt", .dico[["txt_standardized_parameters"]],.dico[["txt_covariance_matrix_adjusted"]], "fitted.cov", + .dico[["txt_residue_standardized"]], "res.St","res.Unst",.dico[["txt_non_standardized_residuals"]],"vcov",.dico[["txt_covariance_matrix_estimated"]], + "AIC", "BIC", .dico[["txt_adequation_measures"]],"fitM", .dico[["txt_inspect_initial_values"]], "start", .dico[["txt_inspect_model_matrices"]], + "modmat", .dico[["txt_inspect_model_representation"]], "modrep"))==TRUE){ + if(info) writeLines(.dico[["ask_which_results_warning_on_default_output"]]) + output<-c( .dico[["txt_default_outputs"]], .dico[["txt_estimated_parameters"]], .dico[["txt_standardized_parameters"]],.dico[["txt_covariance_matrix_adjusted"]], + .dico[["txt_residue_standardized"]], .dico[["txt_non_standardized_residuals"]],.dico[["txt_covariance_matrix_estimated"]],"AIC", "BIC", .dico[["txt_adequation_measures"]], + .dico[["txt_inspect_initial_values"]], .dico[["txt_inspect_model_matrices"]], .dico[["txt_inspect_model_representation"]]) + if(info) writeLines(.dico[["ask_which_output_results"]]) + output<- dlgList(output, preselect=.dico[["txt_default_outputs"]], multiple = TRUE, title=.dico[["ask_results_output"]])$res if(is.null( Resultats$opt.list)) { Resultats<-cfa.in() return(Resultats) @@ -410,7 +410,7 @@ ez.cfa <- if(dial || length(sauvegarde)!=1 || !is.logical(sauvegarde)){ - sauvegarde<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=ask_save_results)$res + sauvegarde<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=.dico[["ask_save_results"]])$res if(length(sauvegarde)==0) { Resultats<-cfa.in() return(Resultats)} @@ -472,27 +472,27 @@ ez.cfa <- group.w.free= group.w.free,fixed.x=fixed.x,information=information,se=se,std.ov=as.logical(std.ov), orthogonal=as.logical(orthogonal),likelihood=likelihood, link=link, int.ov.free=as.logical(int.ov.free), int.lv.free=as.logical(int.lv.free),std.lv=as.logical(std.lv),zero.add=zero.add, zero.keep.margins=zero.keep.margins), silent=T) - if(class(fit)=='try-error') {msgBox(ask_could_not_finish_analysis_respecify_parameters) + if(class(fit)=='try-error') {msgBox(.dico[["ask_could_not_finish_analysis_respecify_parameters"]]) return(ez.cfa())} - if(any(output== "default") | any(output== txt_default_outputs)) { + if(any(output== "default") | any(output== .dico[["txt_default_outputs"]])) { print(summary(fit, fit.measures = TRUE, standardized=T)) - Resultats<-desc_to_display_results_use_summary + Resultats<-.dico[["desc_to_display_results_use_summary"]] summary(fit)->>fit if(length(output)==1) fit->>modele.cfa } - if(any(output== "parEst") | any(output==txt_estimated_parameters)) parameterEstimates(fit)->Resultats[[txt_estimated_parameters_not_standardized]] - if(any(output== "parSt") | any(output==txt_standardized_parameters)) standardizedSolution(fit)->Resultats[[txt_estimated_parameters_standardized]] - if(any(output== txt_covariance_matrix_adjusted) | any(output=="fitted.cov")) fitted(fit)->Resultats[[txt_covariance_matrix_adjusted]] - if(any(output== txt_residue_standardized) | any(output=="res.St")) resid(fit, type="standardized")->Resultats[[txt_residue_standardized]] - if(any(output== txt_non_standardized_residuals) | any(output=="res.Unst")) resid(fit)->Resultats[[txt_non_standardized_residuals]] - if(any(output== "vcov") | any(output==txt_covariance_matrix_estimated)) vcov(fit)->Resultat[[txt_covariance_matrix_estimated]] + if(any(output== "parEst") | any(output==.dico[["txt_estimated_parameters"]])) parameterEstimates(fit)->Resultats[[.dico[["txt_estimated_parameters_not_standardized"]]]] + if(any(output== "parSt") | any(output==.dico[["txt_standardized_parameters"]])) standardizedSolution(fit)->Resultats[[.dico[["txt_estimated_parameters_standardized"]]]] + if(any(output== .dico[["txt_covariance_matrix_adjusted"]]) | any(output=="fitted.cov")) fitted(fit)->Resultats[[.dico[["txt_covariance_matrix_adjusted"]]]] + if(any(output== .dico[["txt_residue_standardized"]]) | any(output=="res.St")) resid(fit, type="standardized")->Resultats[[.dico[["txt_residue_standardized"]]]] + if(any(output== .dico[["txt_non_standardized_residuals"]]) | any(output=="res.Unst")) resid(fit)->Resultats[[.dico[["txt_non_standardized_residuals"]]]] + if(any(output== "vcov") | any(output==.dico[["txt_covariance_matrix_estimated"]])) vcov(fit)->Resultat[[.dico[["txt_covariance_matrix_estimated"]]]] if(any(output== "AIC") ) AIC(fit)->Resultats$AIC if(any(output== "BIC") ) BIC(fit)->Resultats$BIC - if(any(output== txt_adequation_measures) | any(output=="fitM")) fitMeasures(fit)->Resultats[[txt_adjustement_measure]] - if(any(output== txt_inspect_initial_values) | any(output=="start"))inspect(fit, what=start)->Resultats[[txt_init_values]] - if(any(output== txt_inspect_model_matrices) | any(output=="modmat")) inspect(fit)->Resultats[[txt_model_matrix]] - if(any(output== txt_inspect_model_representation) | any(output=="modrep"))inspect(fit, what=list)->Resultats[[txt_model_representation]] + if(any(output== .dico[["txt_adequation_measures"]]) | any(output=="fitM")) fitMeasures(fit)->Resultats[[.dico[["txt_adjustement_measure"]]]] + if(any(output== .dico[["txt_inspect_initial_values"]]) | any(output=="start"))inspect(fit, what=start)->Resultats[[.dico[["txt_init_values"]]]] + if(any(output== .dico[["txt_inspect_model_matrices"]]) | any(output=="modmat")) inspect(fit)->Resultats[[.dico[["txt_model_matrix"]]]] + if(any(output== .dico[["txt_inspect_model_representation"]]) | any(output=="modrep"))inspect(fit, what=list)->Resultats[[.dico[["txt_model_representation"]]]] semPaths(fit, what="path", whatLabels="std", edge.label.cex = 0.65,edge.color="black", exoVar = FALSE,exoCov =T) @@ -522,7 +522,7 @@ ez.cfa <- def.values<-list(mimic="default", fixed.x="default", missing="default",information="default", zero.keep.margins="default",zero.add=c(0.5,0), estimator="ml",group=NULL, test="standard",se="standard",std.ov=T, orthogonal=F, likelihood="default", link="probit",int.ov.free=FALSE, int.lv.free=FALSE,fixed.x="default", std.lv=FALSE, n.boot=1000, group.w.free=F, - group.equal=c("loadings", "intercepts", "means", "thresholds", txt_regressions, "residuals", "residual.covariances", + group.equal=c("loadings", "intercepts", "means", "thresholds", .dico[["txt_regressions"]], "residuals", "residual.covariances", "lv.variances" , "lv.covariances")) if(!is.null(cfa.options$ord)) paste(cfa.options$ord, collapse="','", sep="")->ord diff --git a/R/ez.html.R b/R/ez.html.R index c2b2b5c..33ff420 100644 --- a/R/ez.html.R +++ b/R/ez.html.R @@ -7,8 +7,8 @@ ez.html <- dir.create(path= paste0(tempdir(),"\\easieR") , showWarnings = FALSE) outputb<-c("---", - desc_title, - desc_author, + .dico[["desc_title"]], + .dico[["desc_author"]], paste("date:","'", date(),"'"), if(html) { c("output:", @@ -132,19 +132,19 @@ ez.html <- "tableau<-data.results[[i]]", "tableau<-as.data.frame(tableau)", "if(!is.null(dimnames(tableau)[[1]])) tableau<-data.frame(' '=dimnames(tableau)[[1]], tableau, check.names=F)", - paste0("if(any(grepl('",txt_p_dot_val,"', names(tableau)))) {"), - paste0("col<-which(grepl('",txt_p_dot_val,"', names(tableau)))"), + paste0("if(any(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))) {"), + paste0("col<-which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))"), "if(length(col)>1) {is<-unique(unlist(apply(tableau[,col], 2,myf )))+1", "tableau[,col]<-apply(tableau[,col], 2, round.ps) }else{", - paste0("is<-which(tableau[, which(grepl('",txt_p_dot_val,"', names(tableau)))]<0.05)"), + paste0("is<-which(tableau[, which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))]<0.05)"), "is<-is+1", - paste0("tableau[, which(grepl('",txt_p_dot_val,"', names(tableau)))]<-round.ps(tableau[, which(grepl('",txt_p_dot_val,"', names(tableau)))])}}"), + paste0("tableau[, which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))]<-round.ps(tableau[, which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))])}}"), " ht <- as_hux(tableau, add_colnames = TRUE)", "number_format(ht) <- list(function(x) prettyNum(x, big.mark = ' ', scientific = FALSE) )", "bottom_border(ht)[1,]<-1", "top_border(ht)[1,]<-1", " bottom_border(ht)[dim(ht)[1],]<-1", - paste0("if(any(grepl('",txt_p_dot_val,"', names(tableau)))) {"), + paste0("if(any(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))) {"), "ht<-set_text_color(ht, row = is,col =everywhere , value='red')", "}", "if(any(class(table)=='p.value')){", @@ -174,19 +174,19 @@ ez.html <- "tableau<-data.results[[i]]", "tableau<-as.data.frame(tableau)", "if(!is.null(dimnames(tableau)[[1]])) tableau<-data.frame(' '=dimnames(tableau)[[1]], tableau, check.names=F)", - paste0("if(any(grepl('",txt_p_dot_val,"', names(tableau)))) {"), - paste0("col<-which(grepl('",txt_p_dot_val,"', names(tableau)))"), + paste0("if(any(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))) {"), + paste0("col<-which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))"), "if(length(col)>1) {is<-unique(unlist(apply(tableau[,col], 2,myf )))+1", "tableau[,col]<-apply(tableau[,col], 2, round.ps) }else{", - paste0("is<-which(tableau[, which(grepl('",txt_p_dot_val,"', names(tableau)))]<0.05)"), + paste0("is<-which(tableau[, which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))]<0.05)"), "is<-is+1", - paste0("tableau[, which(grepl('",txt_p_dot_val,"', names(tableau)))]<-round.ps(tableau[, which(grepl('",txt_p_dot_val,"', names(tableau)))])}}"), + paste0("tableau[, which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))]<-round.ps(tableau[, which(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))])}}"), " ht <- as_hux(tableau, add_colnames = TRUE)", "number_format(ht) <- list(function(x) prettyNum(x, big.mark = ' ', scientific = FALSE) )", "bottom_border(ht)[1,]<-1", "top_border(ht)[1,]<-1", " bottom_border(ht)[dim(ht)[1],]<-1", - paste0("if(any(grepl('",txt_p_dot_val,"', names(tableau)))) {"), + paste0("if(any(grepl('",.dico[["txt_p_dot_val"]],"', names(tableau)))) {"), "ht<-set_text_color(ht, row = is,col =everywhere , value='red')", "}", "if(any(class(table)=='p.value')){", diff --git a/R/ez.imp.R b/R/ez.imp.R index 9778847..9f6cbd0 100644 --- a/R/ez.imp.R +++ b/R/ez.imp.R @@ -19,32 +19,32 @@ ez.imp <- deparse(substitute(data))->nom } nom<-paste0(nom,".complet") - if(dial || imp%in% c(txt_do_nothing_keep_all_obs, txt_delete_observations_with_missing_values, txt_replace_by_mean, - txt_replace_by_median,txt_multiple_imputation_amelia,"rien","rm", "mean","median", "amelia") == FALSE){ - writeLines(ask_missing_value_treatment) + if(dial || imp%in% c(.dico[["txt_do_nothing_keep_all_obs"]], .dico[["txt_delete_observations_with_missing_values"]], .dico[["txt_replace_by_mean"]], + .dico[["txt_replace_by_median"]],.dico[["txt_multiple_imputation_amelia"]],"rien","rm", "mean","median", "amelia") == FALSE){ + writeLines(.dico[["ask_missing_value_treatment"]]) print(sapply(data, function(x) sum(length(which(is.na(x))))) ) - imp<- dlgList(c(txt_do_nothing_keep_all_obs, txt_delete_observations_with_missing_values, txt_replace_by_mean, - txt_replace_by_median,txt_multiple_imputation_amelia), preselect=FALSE, multiple = FALSE, title=txt_missing_values_treatment)$res + imp<- dlgList(c(.dico[["txt_do_nothing_keep_all_obs"]], .dico[["txt_delete_observations_with_missing_values"]], .dico[["txt_replace_by_mean"]], + .dico[["txt_replace_by_median"]],.dico[["txt_multiple_imputation_amelia"]]), preselect=FALSE, multiple = FALSE, title=.dico[["txt_missing_values_treatment"]])$res if(length(imp)==0){ return(NULL) } } if(length(imp)==0) return(NULL) - if(imp == txt_do_nothing_keep_all_obs || imp=="rien") return(data) - if(imp== txt_delete_observations_with_missing_values|| imp=="rm"){ + if(imp == .dico[["txt_do_nothing_keep_all_obs"]] || imp=="rien") return(data) + if(imp== .dico[["txt_delete_observations_with_missing_values"]]|| imp=="rm"){ data<-data[complete.cases(data),] if(dial) assign(nom, data, envir=.GlobalEnv) } - if(imp==txt_replace_by_mean|| imp=="mean"){ + if(imp==.dico[["txt_replace_by_mean"]]|| imp=="mean"){ for(i in 1 : length(data)) {data[which(is.na(data[,i])),i]<-mean(data[,i], na.rm=T)} if(dial) assign(nom, data, envir=.GlobalEnv) } - if(imp== txt_replace_by_median|| imp=="median"){ + if(imp== .dico[["txt_replace_by_median"]]|| imp=="median"){ for(i in 1 : length(data)) {data[which(is.na(data[,i])),i]<-median(data[,i], na.rm=T)} if(dial) assign(nom, data, envir=.GlobalEnv) } - if(imp== txt_multiple_imputation_amelia|| imp=="amelia"){ + if(imp== .dico[["txt_multiple_imputation_amelia"]]|| imp=="amelia"){ amelia(x=data, m = 1, p2s = 0,frontend = FALSE, idvars = id, ts = NULL, cs = NULL, polytime = NULL, splinetime = NULL, intercs = FALSE, lags = NULL, leads = NULL, startvals = 0, tolerance = 0.0001, diff --git a/R/ez.install.R b/R/ez.install.R index 0730d16..aaa03bf 100644 --- a/R/ez.install.R +++ b/R/ez.install.R @@ -70,7 +70,7 @@ ez.install <- # 2c. installer packages manquants si necessaires et si utilisateur le souhaite if(length(pack.uninst)>0){ - writeLines(txt_packages_install) + writeLines(.dico[["txt_packages_install"]]) print(pack.uninst) flush.console() install.packages(pack.uninst, quiet=TRUE) @@ -169,7 +169,7 @@ function(){ ) list()->Resultats - Resultats[[desc_install_correct_packages]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==TRUE) ] - Resultats[[desc_install_bad_packages]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==FALSE) ] + Resultats[[.dico[["desc_install_correct_packages"]]]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==TRUE) ] + Resultats[[.dico[["desc_install_bad_packages"]]]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==FALSE) ] return(Resultats) } diff --git a/R/ez.mediation.R b/R/ez.mediation.R index 037dfe4..785bb21 100644 --- a/R/ez.mediation.R +++ b/R/ez.mediation.R @@ -21,13 +21,13 @@ ez.mediation <- } if (save.pdf == TRUE) { if (save.eps == TRUE) - stop(desc_only_one_file_format_at_time_EPS_PDF) + stop(.dico[["desc_only_one_file_format_at_time_EPS_PDF"]]) if (save.jpg == TRUE) - stop(desc_only_one_file_format_at_time_PDF_JPG) + stop(.dico[["desc_only_one_file_format_at_time_PDF_JPG"]]) } if (save.eps == TRUE) { if (save.jpg == TRUE) - stop(desc_only_one_file_format_at_time_EPS_JPG) + stop(.dico[["desc_only_one_file_format_at_time_EPS_JPG"]]) } if (save.pdf == TRUE | save.eps == TRUE | save.jpg == TRUE) { no.file.name <- FALSE @@ -118,48 +118,48 @@ ez.mediation <- try(lapply(packages, library, character.only=T), silent=T)->test2 if(class(test2)== 'try-error') return(ez.install()) Resultats<-list() - dlgList(c(txt_simple_mediation_effect, - txt_distance_mediation_effect), preselect=NULL, multiple = FALSE, title=ask_mediation_type)$res->choix + dlgList(c(.dico[["txt_simple_mediation_effect"]], + .dico[["txt_distance_mediation_effect"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_mediation_type"]])$res->choix if(length(choix)==0) return(analyse()) choix.data(nom=T)->data if(is.null(data)) return(ez.mediation()) data[[1]]->nom data[[2]]->data listes<-data.frame(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), names(data)) - if(info) writeLines(ask_predictor) + if(info) writeLines(.dico[["ask_predictor"]]) X<-dlgList(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), multiple = F, - title=txt_predictor)$res + title=.dico[["txt_predictor"]])$res if(length(X)==0) return(ez.mediation()) subset(listes, listes[,1] %in% X)[,2]->X as.character(X)->X - if(info) writeLines(ask_mediator) + if(info) writeLines(.dico[["ask_mediator"]]) Mediator<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" ")), multiple = F, - title=txt_mediator)$res + title=.dico[["txt_mediator"]])$res if(length(Mediator)==0) return(ez.mediation()) subset(listes, listes[,1] %in% Mediator)[,2]->Mediator as.character(Mediator)->Mediator - if(choix==txt_distance_mediation_effect){ - writeLines(ask_second_mediator) - Mediator2<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" ")), multiple = F, title=txt_mediator2)$res + if(choix==.dico[["txt_distance_mediation_effect"]]){ + writeLines(.dico[["ask_second_mediator"]]) + Mediator2<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" ")), multiple = F, title=.dico[["txt_mediator2"]])$res if(length(Mediator2)==0) return(ez.mediation()) subset(listes, listes[,1] %in% Mediator2)[,2]->Mediator2 as.character(Mediator2)->Mediator2 } - if(info) writeLines(ask_chose_dependant_variable) + if(info) writeLines(.dico[["ask_chose_dependant_variable"]]) VD<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" ")), multiple = F, - title=txt_dependant_variable)$res + title=.dico[["txt_dependant_variable"]])$res subset(listes, listes[,1] %in% VD)[,2]->VD as.character(VD)->VD - writeLines(ask_bootstrap_number_min_500) - n.boot<-dlgInput(ask_bootstraps_number, 1)$res + writeLines(.dico[["ask_bootstrap_number_min_500"]]) + n.boot<-dlgInput(.dico[["ask_bootstraps_number"]], 1)$res if(length(n.boot)==0) n.boot<-"0" strsplit(n.boot, ":")->n.boot tail(n.boot[[1]],n=1)->n.boot as.numeric(n.boot)->n.boot if(!is.na(n.boot) && any(n.boot>50)) bootstrap<-TRUE else bootstrap<-FALSE - if(choix==txt_simple_mediation_effect){ + if(choix==.dico[["txt_simple_mediation_effect"]]){ MBESS::mediation(data[,X], data[,Mediator], data[,VD], conf.level = 0.95, bootstrap = bootstrap, B = n.boot, which.boot="both", save.bs.replicates=TRUE, complete.set=TRUE)->mediation.out for(i in 1:length(mediation.out)){ if(class(mediation.out[[i]])== "list") for(j in 1 : length(mediation.out[[i]])){ @@ -167,9 +167,9 @@ ez.mediation <- round(mediation.out[[i]], 4)->mediation.out[[i]]} } Resultats$Analyse.mediation<-mediation.out - Resultats$Information<-txt_for_a_detailed_results_description_mediation + Resultats$Information<-.dico[["txt_for_a_detailed_results_description_mediation"]] mediation.effect.bar.plot2(data[,X], data[,Mediator], data[,VD],main = "Mediation Effect Bar Plot", width = 1, left.text.adj = 0,right.text.adj = 0, rounding = 3, file = "", save.pdf = FALSE,save.eps = FALSE, save.jpg = FALSE) - }else { print(desc_unavailable_distal_mediations) + }else { print(.dico[["desc_unavailable_distal_mediations"]]) #data2<-data[,c(X, Mediator, Mediator2, VD)] #names(data2)<-c("x", "m1","m2","y") #distal.med(data2)->results @@ -178,21 +178,21 @@ ez.mediation <- #round(as.numeric(as.character(results$SE)),4)->results$SE #round(as.numeric(as.character(results[,3])),3)->results$t.ratio #round(as.numeric(as.character(results$Med.Ratio)),4)->results$Med.Ratio - #names(results)<-c(txt_effect, "Erreur.st","test.t", "Ratio.med") - #results->Resultats[[txt_distance_mediator]] - #Resultats$Information<-txt_for_a_detailed_results_description_distal + #names(results)<-c(.dico[["txt_effect"]], "Erreur.st","test.t", "Ratio.med") + #results->Resultats[[.dico[["txt_distance_mediator"]]]] + #Resultats$Information<-.dico[["txt_for_a_detailed_results_description_distal"]] #distmed.boot <- boot(data2, distInd.ef, R=n.boot) #boot.ci(distmed.boot, conf=.95, type=c("basic","perc", "norm"))->IC.boot #round(matrix(c(IC.boot$normal[,2:3],IC.boot$basic[,4:5],IC.boot$percent[,4:5]), ncol=2 ),4)->IC.boot #dimnames(IC.boot)[[1]]<-c("normal","basic","percentile") #dimnames(IC.boot)[[2]]<-c("limite.inf","limite.sup") - #IC.boot->Resultats[[txt_confidence_interval_estimated_by_bootstrap]] + #IC.boot->Resultats[[.dico[["txt_confidence_interval_estimated_by_bootstrap"]]]] } - dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=ask_save_results)$res->sauvegarde + dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=.dico[["ask_save_results"]])$res->sauvegarde if(length(sauvegarde)==0) sauvegarde<-FALSE if(sauvegarde) save(Resultats=Resultats, choix=choix, env=.e) - ref1(packages)->Resultats[[txt_references]] + ref1(packages)->Resultats[[.dico[["txt_references"]]]] if(html) ez.html(Resultats) return(Resultats) diff --git a/R/ez.rank.R b/R/ez.rank.R index 6f44987..2391c2c 100644 --- a/R/ez.rank.R +++ b/R/ez.rank.R @@ -17,14 +17,14 @@ ez.rank <- data[[1]]->nom1 data[[2]]->data} if(!is.null(X)) dial<-FALSE else dial<-TRUE - msg.pre1<-ask_specify_variables_for_ranks - .var.type(X=X, info=T, data=data, type="numeric", message=msg.pre1,multiple=T, title=txt_variables)->X1 + msg.pre1<-.dico[["ask_specify_variables_for_ranks"]] + .var.type(X=X, info=T, data=data, type="numeric", message=msg.pre1,multiple=T, title=.dico[["txt_variables"]])->X1 if(is.null(X1)) return(preprocess()) if(!is.null(X) && X1$X!=X) dial<-TRUE X1$X->X if(dial){ - if(info) writeLines(ask_how_to_treat_exaequo_rank) - ties.method<-dlgList(c("average", "first", "last", "random", "max", "min"), multiple = F, preselect="average", title=ask_specify_sample)$res + if(info) writeLines(.dico[["ask_how_to_treat_exaequo_rank"]]) + ties.method<-dlgList(c("average", "first", "last", "random", "max", "min"), multiple = F, preselect="average", title=.dico[["ask_specify_sample"]])$res } if(length(X)==1) rangs<-rank(data[,X],ties.method=ties.method, na.last="keep" ) else sapply(data[,X], rank, ties.method=ties.method, na.last="keep")->rangs if(length(X)==1) data.frame(rangs)->rangs diff --git a/R/ez.report.R b/R/ez.report.R index 9290192..e171e4f 100644 --- a/R/ez.report.R +++ b/R/ez.report.R @@ -2,7 +2,7 @@ ez.report<-function(html=NULL){ options (warn=-1) require(svDialogs) if(is.null(html)){ choix<- c("html", "MS WORD") - title<-ask_which_output + title<-.dico[["ask_which_output"]] choix<-dlgList(choix, preselect=NULL, multiple = FALSE, title=title)$res if(length(choix)==0) return(donnees()) diff --git a/R/ez.reshape.R b/R/ez.reshape.R index bc992b0..c60c6df 100644 --- a/R/ez.reshape.R +++ b/R/ez.reshape.R @@ -93,20 +93,20 @@ ez.reshape<-function(data=NULL, varying = NULL, v.names = NULL, # type : either "msg" or "title" # number : number of message #if(grepl("French",Sys.setlocale()) | grepl("fr",Sys.setlocale())) { - msg<-c(ask_chose_cols_corresponding_to_repeated_measures, - txt_col_correspoding_to_variable, - desc_should_specify_nb_factors_repeated_measure, - ask_did_not_specify_nb_factors_repeated_measure_exit, - desc_non_numeric_value, - desc_you_have_selected, txt_cols, - desc_modalities_product_must_correspond_to_cols_selected, - ask_press_enter_to_continue) + msg<-c(.dico[["ask_chose_cols_corresponding_to_repeated_measures"]], + .dico[["txt_col_correspoding_to_variable"]], + .dico[["desc_should_specify_nb_factors_repeated_measure"]], + .dico[["ask_did_not_specify_nb_factors_repeated_measure_exit"]], + .dico[["desc_non_numeric_value"]], + .dico[["desc_you_have_selected"]], .dico[["txt_cols"]], + .dico[["desc_modalities_product_must_correspond_to_cols_selected"]], + .dico[["ask_press_enter_to_continue"]]) - title<-c(txt_cols_in_repeated_measure ,txt_nb_variables_measured, txt_measured_variable_name, - ask_nb_factors_repeated_measure, - txt_factor_name,ask_how_many_modalities,txt_modality, txt_modalities_name_for, - ask_is_long_format_correct) + title<-c(.dico[["txt_cols_in_repeated_measure"]] ,.dico[["txt_nb_variables_measured"]], .dico[["txt_measured_variable_name"]], + .dico[["ask_nb_factors_repeated_measure"]], + .dico[["txt_factor_name"]],.dico[["ask_how_many_modalities"]],.dico[["txt_modality"]], .dico[["txt_modalities_name_for"]], + .dico[["ask_is_long_format_correct"]]) #} else { # msg<-c("Please choose all the columns corresponding to the repeaed measure levels", @@ -120,7 +120,7 @@ ez.reshape<-function(data=NULL, varying = NULL, v.names = NULL, # title<-c("Columns in repeated measures", "Number of measured variables", "Name of measured variable", # "How many repeated measures variables ?", # "name of the factor", "How many levels", "level", "Name of levels for", - # ask_is_long_format_correct) + # .dico[["ask_is_long_format_correct"]]) #} ifelse(type=="msg", r<-msg, r<-title) @@ -183,7 +183,7 @@ ez.reshape<-function(data=NULL, varying = NULL, v.names = NULL, } else { varying[[1]]<- varying2 - v.names <- dlgInput(paste(.ez.reshape.msg("title",3),1), txt_variable)$res + v.names <- dlgInput(paste(.ez.reshape.msg("title",3),1), .dico[["txt_variable"]])$res if(length(v.names)==0) { return(ez.reshape())} strsplit(v.names, ":")->v.names v.names<-gsub("[^[:alnum:]]", ".", v.names) @@ -251,7 +251,7 @@ ez.reshape<-function(data=NULL, varying = NULL, v.names = NULL, writeLines(paste(.ez.reshape.msg("msg",6), length(varying[[1]]),.ez.reshape.msg("msg",7) )) writeLines(.ez.reshape.msg("msg",8)) for(i in 1:N.facteurs) { - dlgInput(paste(.ez.reshape.msg("title",5),i), paste(txt_variable,i, sep="."))$res->IV.names[[i]] + dlgInput(paste(.ez.reshape.msg("title",5),i), paste(.dico[["txt_variable"]],i, sep="."))$res->IV.names[[i]] if(length(IV.names[[i]])==0) return(ez.reshape(data=data, varying=varying)) strsplit(IV.names[[i]], ":")->IV.names[[i]] tail(IV.names[[i]][[1]],n=1)->IV.names[[i]] @@ -335,7 +335,7 @@ ez.reshape<-function(data=NULL, varying = NULL, v.names = NULL, LCS <- function (a, b) { m <- length(a) n <- length(b) - if (m == 0 || n == 0) stop (txt_vector_length_zero) + if (m == 0 || n == 0) stop (.dico[["txt_vector_length_zero"]]) # creates a table M <- matrix(nrow = m + 1, ncol = n + 1) diff --git a/R/factor.an.R b/R/factor.an.R index 84c45ad..07bd6c5 100644 --- a/R/factor.an.R +++ b/R/factor.an.R @@ -1,5 +1,5 @@ factor.an <- - function(data=NULL, X=NULL, nF=NULL, rotation="none", methode="ml", sat=0.3, outlier=c(txt_complete_dataset), + function(data=NULL, X=NULL, nF=NULL, rotation="none", methode="ml", sat=0.3, outlier=c(.dico[["txt_complete_dataset"]]), imp=NULL, ord=NULL, sauvegarde=FALSE, scor.fac=FALSE,n.boot=1, hier=F, nfact2=1, choix="afe",info=T, html=T){ # data : dataframe @@ -9,7 +9,7 @@ factor.an <- # "promax", "oblimin", "simplimax","bentlerQ", "geominQ","biquartimin", "cluster") # methode : character. One among c("ml", "minres" "minchi", "wls","gls","pa") # sat : numeric. Level of loading below which loading is not printed. - # outlier : one among txt_complete_dataset or txt_without_outliers + # outlier : one among .dico[["txt_complete_dataset"]] or .dico[["txt_without_outliers"]] # imp : character. How should missing values be treated ? One among "mean" (use mean), "median" (use median), "amelia", "rm" (remove) # ord : character vector. Which variables among X are ordinal ? (or dichotomous) # sauvegarde : logical. Should result be saved in rtf ? @@ -25,15 +25,15 @@ factor.an <- Resultats<-list() if(is.null(data) | is.null(X)) {dial<-TRUE}else dial<-F - if(dial || is.null(choix) || length(choix)!=1 ||choix %in% c(txt_factorial_exploratory_analysis,"afe", - "afc","acp",txt_confirmatory_factorial_analysis,txt_principal_component_analysis)==FALSE){ + if(dial || is.null(choix) || length(choix)!=1 ||choix %in% c(.dico[["txt_factorial_exploratory_analysis"]],"afe", + "afc","acp",.dico[["txt_confirmatory_factorial_analysis"]],.dico[["txt_principal_component_analysis"]])==FALSE){ dial<-T - if(info) writeLines(ask_chose_analysis) - dlgList(c(txt_factorial_exploratory_analysis, - txt_confirmatory_factorial_analysis, - txt_principal_component_analysis), preselect=NULL, multiple = FALSE, title=ask_which_analysis)$res->choix + if(info) writeLines(.dico[["ask_chose_analysis"]]) + dlgList(c(.dico[["txt_factorial_exploratory_analysis"]], + .dico[["txt_confirmatory_factorial_analysis"]], + .dico[["txt_principal_component_analysis"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_which_analysis"]])$res->choix if(length(choix)==0) return(NULL) - if(choix==txt_confirmatory_factorial_analysis) return(ez.cfa()) + if(choix==.dico[["txt_confirmatory_factorial_analysis"]]) return(ez.cfa()) try( windows(record=T), silent=T)->win if(class(win)=='try-error') quartz() @@ -48,11 +48,11 @@ factor.an <- }else{ deparse(substitute(data))->nom } - if(choix=="fa" | choix==txt_factorial_exploratory_analysis) msg3<-ask_chose_variables_at_least_five else{ - msg3<-ask_chose_variables_at_least_three + if(choix=="fa" | choix==.dico[["txt_factorial_exploratory_analysis"]]) msg3<-.dico[["ask_chose_variables_at_least_five"]] else{ + msg3<-.dico[["ask_chose_variables_at_least_three"]] } - X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=txt_variables, out=NULL) + X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=.dico[["txt_variables"]], out=NULL) data<-X$data X<-X$X if(is.null(X) || length(X)<3) { @@ -61,91 +61,91 @@ factor.an <- - if(dial || length(outlier)>1 || outlier %in% c(txt_complete_dataset, txt_without_outliers) ==FALSE){ - if(info) writeLines(ask_analysis_on_complete_data_or_remove_outliers) - if(info) writeLines(desc_outliers_identified_on_mahalanobis) - outlier<- dlgList(c(txt_complete_dataset, txt_without_outliers), preselect=txt_complete_dataset,multiple = FALSE, title=ask_results_desired)$res + if(dial || length(outlier)>1 || outlier %in% c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]) ==FALSE){ + if(info) writeLines(.dico[["ask_analysis_on_complete_data_or_remove_outliers"]]) + if(info) writeLines(.dico[["desc_outliers_identified_on_mahalanobis"]]) + outlier<- dlgList(c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]), preselect=.dico[["txt_complete_dataset"]],multiple = FALSE, title=.dico[["ask_results_desired"]])$res if(length(outlier)==0) { Resultats<-fa.in() return(Resultats)} } - if(outlier==txt_without_outliers){ + if(outlier==.dico[["txt_without_outliers"]]){ inf<-VI.multiples(data,X) - Resultats[[txt_labeled_outliers]]<-inf[[txt_labeled_outliers]] + Resultats[[.dico[["txt_labeled_outliers"]]]]<-inf[[.dico[["txt_labeled_outliers"]]]] data<-inf$data } if(dial){ - if(info) writeLines(ask_variables_type_correlations) - if(length(unique(unlist(data[,X])))<9) {type<-dlgList(c(txt_dichotomic_ordinal,txt_continuous, "mixte"), preselect=NULL, multiple = FALSE, title=ask_variables_type)$res}else { - type<-dlgList(c(txt_continuous, "mixte"), preselect=NULL, multiple = FALSE, title=ask_variables_type)$res + if(info) writeLines(.dico[["ask_variables_type_correlations"]]) + if(length(unique(unlist(data[,X])))<9) {type<-dlgList(c(.dico[["txt_dichotomic_ordinal"]],.dico[["txt_continuous"]], "mixte"), preselect=NULL, multiple = FALSE, title=.dico[["ask_variables_type"]])$res}else { + type<-dlgList(c(.dico[["txt_continuous"]], "mixte"), preselect=NULL, multiple = FALSE, title=.dico[["ask_variables_type"]])$res } if(length(type)==0) {Resultats<-fa.in() return(Resultats)} - } else{if(is.null(ord)) type<-txt_continuous else type<-txt_dichotomic_ordinal + } else{if(is.null(ord)) type<-.dico[["txt_continuous"]] else type<-.dico[["txt_dichotomic_ordinal"]] } - if(type==txt_continuous){ methode<-c("ml") + if(type==.dico[["txt_continuous"]]){ methode<-c("ml") cor<-"cor" Matrice<-corr.test(data[,X], method="pearson")$r }else { cor<-"poly" methode<-c("minres") if(type=="mixte") {cor<-"mixed" - if(info) writeLines(ask_ordinal_variables) - ord<-dlgList(X, multiple = TRUE, title=ask_ordinal_variables)$res + if(info) writeLines(.dico[["ask_ordinal_variables"]]) + ord<-dlgList(X, multiple = TRUE, title=.dico[["ask_ordinal_variables"]])$res if(length(ord)==0) {Resultats<-fa.in() return(Resultats)} }else ord<-X Matrice<-try(tetrapoly(data=data[,X],X=X,info=T, ord=ord,group=NULL,estimator='two.step',output='cor',imp=imp, html=F)[[1]],silent=T) if(all(class(Matrice)!="matrix")) { - sortie<-dlgMessage(ask_correlation_matrix_could_not_be_computed, type="yesno")$res + sortie<-dlgMessage(.dico[["ask_correlation_matrix_could_not_be_computed"]], type="yesno")$res if(sortie=="yes") return(NULL) else Matrice<-try(tetrapoly(data=data[,X],X=X,info=T, ord=ord,group=NULL,estimator='two.step',output='cor', imp="rm")[[1]],silent=T) if(class(Matrix)=='try-error') {Matrice<-corr.test(data[,X], method="Spearman")$r - msgBox(desc_polyc_correlations_failed_rho_used_instead)} + msgBox(.dico[["desc_polyc_correlations_failed_rho_used_instead"]])} } } Matrice1 <- mat.sort(Matrice) if(length(X)>30) numbers<-F else numbers<-T - try(cor.plot(Matrice1, show.legend=FALSE, main=txt_correlations_matrix_afe, labels=NULL, n.legend=0, MAR=TRUE, numbers=numbers,cex=1), silent=T) - round(Matrice,3)->Resultats[[txt_correlations_matrix]] + try(cor.plot(Matrice1, show.legend=FALSE, main=.dico[["txt_correlations_matrix_afe"]], labels=NULL, n.legend=0, MAR=TRUE, numbers=numbers,cex=1), silent=T) + round(Matrice,3)->Resultats[[.dico[["txt_correlations_matrix"]]]] round(unlist(cortest.bartlett(data[,X])),4)->bartlett - names(bartlett)<-c(txt_chi_dot_squared,txt_p_dot_val,txt_df) + names(bartlett)<-c(.dico[["txt_chi_dot_squared"]],.dico[["txt_p_dot_val"]],.dico[["txt_df"]]) ### doit etre significatif (attention depend de la taille de l echantillon) - bartlett->Resultats[[txt_adequation_measurement_of_matrix]][[txt_barlett_test]] + bartlett->Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]][[.dico[["txt_barlett_test"]]]] KMO1<-KMO(Matrice) - if(any(is.na(KMO1))) {msgBox(desc_kmo_on_matrix_could_not_be_obtained_trying) + if(any(is.na(KMO1))) {msgBox(.dico[["desc_kmo_on_matrix_could_not_be_obtained_trying"]]) Matrice<-cor.smooth(Matrice) KMO1<-KMO(Matrice)} if(any(is.na(KMO1))) { - msgBox(desc_kmo_on_matrix_could_not_be_obtained) - Resultats[[txt_adequation_measurement_of_matrix]][[txt_kaiser_meyer_olkin_index]]<-desc_kmo_could_not_be_computed_verify_matrix + msgBox(.dico[["desc_kmo_on_matrix_could_not_be_obtained"]]) + Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]][[.dico[["txt_kaiser_meyer_olkin_index"]]]]<-.dico[["desc_kmo_could_not_be_computed_verify_matrix"]] } else { - round(KMO1$MSA,3)->Resultats[[txt_adequation_measurement_of_matrix]][[txt_kaiser_meyer_olkin_index]] ### doit etre superieur a 0.5 sinon la matrice ne convient pas pour analyse factorielle. Dans lĂƒÂƒĂ‚Â‚ĂƒÂ‚Ă‚Â’ideal, avoir au moins 0.8. Si des X presentent un KMO<0.5, on peut envisager de les supprimer. - round(KMO1$MSAi,3)->Resultats[[txt_adequation_measurement_of_matrix]]$'Indice de Kaiser-Meyer-Olkin par item' - round(det(Matrice),5)->Resultats[[txt_adequation_measurement_of_matrix]][[txt_correlation_matrix_determinant]] - Resultats[[txt_adequation_measurement_of_matrix]][[txt_correlation_matrix_determinant_information]]<-desc_multicolinearity_risk + round(KMO1$MSA,3)->Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]][[.dico[["txt_kaiser_meyer_olkin_index"]]]] ### doit etre superieur a 0.5 sinon la matrice ne convient pas pour analyse factorielle. Dans lĂƒÂƒĂ‚Â‚ĂƒÂ‚Ă‚Â’ideal, avoir au moins 0.8. Si des X presentent un KMO<0.5, on peut envisager de les supprimer. + round(KMO1$MSAi,3)->Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]]$'Indice de Kaiser-Meyer-Olkin par item' + round(det(Matrice),5)->Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]][[.dico[["txt_correlation_matrix_determinant"]]]] + Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]][[.dico[["txt_correlation_matrix_determinant_information"]]]]<-.dico[["desc_multicolinearity_risk"]] } if(dial){ - print(Resultats[[txt_adequation_measurement_of_matrix]]) - print(desc_kmo_must_strictly_be_more_than_a_half) - cat (txt_press_enter_to_continue) + print(Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]]) + print(.dico[["desc_kmo_must_strictly_be_more_than_a_half"]]) + cat (.dico[["txt_press_enter_to_continue"]]) line <- readline() - dlgMessage(c(ask_sufficient_matrix_for_afe, ask_continue), "okcancel")$res->res.kmo - if(res.kmo=="cancel") {print(desc_you_exited_afe) + dlgMessage(c(.dico[["ask_sufficient_matrix_for_afe"]], .dico[["ask_continue"]]), "okcancel")$res->res.kmo + if(res.kmo=="cancel") {print(.dico[["desc_you_exited_afe"]]) return(analyse())} } if(dial || length(methode)>1 || is.null(methode) || methode%in%c("minres","wls","gls","pa", "ml","minchi")==FALSE){ - if(info) writeLines(desc_for_ordinal_and_dicho_varible_prefer_min_res) - methode<-dlgList(c("minres","wls","gls","pa", "ml","minchi"), preselect= methode, multiple = FALSE, title=ask_which_algorithm)$res + if(info) writeLines(.dico[["desc_for_ordinal_and_dicho_varible_prefer_min_res"]]) + methode<-dlgList(c("minres","wls","gls","pa", "ml","minchi"), preselect= methode, multiple = FALSE, title=.dico[["ask_which_algorithm"]])$res if(length(methode)==0) {Resultats<-fa.in() return(Resultats)} @@ -154,16 +154,16 @@ factor.an <- eigen(Matrice)$values->eigen parallel(length(data[,1]), length(X), 100)->P1 nScree(x =eigen, aparallel=P1$eigen$mevpea)->result - result->Resultats[[txt_parallel_analysis]] + result->Resultats[[.dico[["txt_parallel_analysis"]]]] plotnScree(result) if(dial | is.null(nF) | !is.numeric(nF)) { - msgBox(paste(txt_factors_to_keep_accord_to_parallel_analysis_is,result$Components$nparallel, txt_factors )) - cat (txt_press_enter_to_continue) + msgBox(paste(.dico[["txt_factors_to_keep_accord_to_parallel_analysis_is"]],result$Components$nparallel, .dico[["txt_factors"]] )) + cat (.dico[["txt_press_enter_to_continue"]]) line <- readline() nF<-NA while(!is.numeric(nF)) { - writeLines(ask_factors_number) - nF <- dlgInput(ask_factors_number, 2)$res + writeLines(.dico[["ask_factors_number"]]) + nF <- dlgInput(.dico[["ask_factors_number"]], 2)$res if(length(nF)==0) {Resultats<-fa.in() return(Resultats) } @@ -171,7 +171,7 @@ factor.an <- tail(nF[[1]],n=1)->nF as.numeric(nF)->nF if(any((nF%%1==0)%in% c(FALSE, NA))|| nF<0 || nF>(length(X)/2) ){ - msgBox(desc_facotrs_must_be_positive_int_inferior_to_variables_num) + msgBox(.dico[["desc_facotrs_must_be_positive_int_inferior_to_variables_num"]]) nF<-NA } } @@ -181,18 +181,18 @@ factor.an <- if(dial & nF>1 || (length(rotation)>1 | rotation %in% c("none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT","bifactor", "promax", "oblimin", "simplimax","bentlerQ", "geominQ","biquartimin", "cluster")==FALSE)){ - if(choix=="acp" | choix==txt_principal_component_analysis) rotation<- c("none", "varimax", "quartimax", "promax", "oblimin", "simplimax","cluster") else{ + if(choix=="acp" | choix==.dico[["txt_principal_component_analysis"]]) rotation<- c("none", "varimax", "quartimax", "promax", "oblimin", "simplimax","cluster") else{ rotation<-c("none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT","bifactor", "promax", "oblimin", "simplimax","bentlerQ", "geominQ","biquartimin", "cluster") } - writeLines(ask_chose_rotation) - rotation<-dlgList(rotation, preselect= "oblimin", multiple = FALSE, title=ask_which_rotation)$res + writeLines(.dico[["ask_chose_rotation"]]) + rotation<-dlgList(rotation, preselect= "oblimin", multiple = FALSE, title=.dico[["ask_which_rotation"]])$res if(length(rotation)==0) {Resultats<-fa.in() return(Resultats)} } if(dial | !is.logical(scor.fac)){ - writeLines(ask_integrate_factorial_scores_in_data) - dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=ask_factorial_scores)$res->scor.fac + writeLines(.dico[["ask_integrate_factorial_scores_in_data"]]) + dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=.dico[["ask_factorial_scores"]])$res->scor.fac if(length(scor.fac)==0) {Resultats<-fa.in() return(Resultats)} } @@ -201,8 +201,8 @@ factor.an <- sat<-NULL } while(is.null(sat)){ - if(info) writeLines(desc_saturation_criterion_show_only_above_threshold) - sat <- dlgInput(ask_which_saturation_criterion, 0.3)$res + if(info) writeLines(.dico[["desc_saturation_criterion_show_only_above_threshold"]]) + sat <- dlgInput(.dico[["ask_which_saturation_criterion"]], 0.3)$res if(length(sat)==0) {Resultats<-fa.in() return(Resultats) } @@ -210,32 +210,32 @@ factor.an <- tail(sat[[1]],n=1)->sat as.numeric(sat)->sat if(is.na(sat)) {sat<-NULL - msgBox(desc_saturation_criterion_must_be_between_zero_and_one) } + msgBox(.dico[["desc_saturation_criterion_must_be_between_zero_and_one"]]) } } - if(choix==txt_factorial_exploratory_analysis) { + if(choix==.dico[["txt_factorial_exploratory_analysis"]]) { if(!is.null(n.boot) && ((class(n.boot)!="numeric" & class(n.boot)!="integer") || n.boot%%1!=0 || n.boot<1)){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } while(is.null(n.boot)){ - writeLines(ask_bootstrap_numbers_1_for_none) - n.boot<-dlgInput(ask_bootstraps_number, 1)$res + writeLines(.dico[["ask_bootstrap_numbers_1_for_none"]]) + n.boot<-dlgInput(.dico[["ask_bootstraps_number"]], 1)$res if(length(n.boot)==0) {Resultats<-fa.in() return(Resultats)} strsplit(n.boot, ":")->n.boot tail(n.boot[[1]],n=1)->n.boot as.numeric(n.boot)->n.boot if(is.na(n.boot) || n.boot%%1!=0 || n.boot<1){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } } if(dial & nF>1 & methode!="pa" & rotation%in%c("oblimin","simplimax", "promax") || hier==T && nFact2>=nF/2){ - if(info) writeLines(ask_test_hierarchical_structure) - dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=ask_hierarchical_analysis)$res->hier + if(info) writeLines(.dico[["ask_test_hierarchical_structure"]]) + dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=.dico[["ask_hierarchical_analysis"]])$res->hier if(length(hier)==0) {Resultats<-fa.in() return(Resultats) } @@ -243,8 +243,8 @@ factor.an <- nfact2<-NA while(!is.numeric(nfact2)) { nfact2<-NA - writeLines(ask_factors_number_for_hierarchical_structure) - nfact2 <- dlgInput(ask_factors_superior_level, 1)$res + writeLines(.dico[["ask_factors_number_for_hierarchical_structure"]]) + nfact2 <- dlgInput(.dico[["ask_factors_superior_level"]], 1)$res if(length(nfact2)==0) {Resultats<-fa.in() return(Resultats) } @@ -252,7 +252,7 @@ factor.an <- tail(nfact2[[1]],n=1)->nfact2 as.numeric(nfact2)->nfact2 if(any(nfact2%%1==0 %in% c(FALSE, NA))|| nfact2<0 || nfact2>=nF/2 ){ - msgBox(desc_nb_factors_must_be_positive_integer) + msgBox(.dico[["desc_nb_factors_must_be_positive_integer"]]) nfact2<-NA } } @@ -264,8 +264,8 @@ factor.an <- if(dial | !is.logical(sauvegarde)){ - if(info) writeLines(ask_save_results_in_external_file) - dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=ask_save)$res->sauvegarde + if(info) writeLines(.dico[["ask_save_results_in_external_file"]]) + dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=.dico[["ask_save"]])$res->sauvegarde if(length(sauvegarde)==0) {Resultats<-fa.in() return(Resultats) } @@ -293,65 +293,65 @@ factor.an <- fa.out<-function(Matrice, data, X, nF, methode, rotation, sat, scor.fac, n.boot, nom, hier=FALSE, cor="cor", nfact2){ - if( cor=="cor") { Resultats[[txt_multivariate_normality]]<-.normalite(data, X)} else cor<-"mixed" + if( cor=="cor") { Resultats[[.dico[["txt_multivariate_normality"]]]]<-.normalite(data, X)} else cor<-"mixed" if(n.boot==1) { FA.results<-fa(Matrice,nfactors= nF, n.obs=length(data[,1]),fm=methode, rotate=rotation, n.iter=1) # realise l AFE } else { FA.results<-try(fa(data[,X], nfactors= nF, fm=method, rotate=rotation, n.iter=n.boot, cor=cor), silent=T) if(class(FA.results)=='try-error') { - msgBox(desc_model_could_not_converge) + msgBox(.dico[["desc_model_could_not_converge"]]) FA.results<-try(fa(data[,X], nfactors= nF, fm=methode, rotate=rotation, n.iter=1, cor="cor", SMC=F), silent=T) if(class(FA.results)=='try-error'){ - msgBox(ask_could_not_converge_model_verify_correlation_matrix) + msgBox(.dico[["ask_could_not_converge_model_verify_correlation_matrix"]]) return(analyse())} } } Resultats<-list() - Resultats$analyse<-paste(txt_factorial_analysis_using_fa_with_method, FA.results$fm) - if(rotation=="none") Resultats$rotation<-desc_there_is_no_rotation else Resultats$rotation<-paste(txt_rotation_is_a_rotation, rotation) + Resultats$analyse<-paste(.dico[["txt_factorial_analysis_using_fa_with_method"]], FA.results$fm) + if(rotation=="none") Resultats$rotation<-.dico[["desc_there_is_no_rotation"]] else Resultats$rotation<-paste(.dico[["txt_rotation_is_a_rotation"]], rotation) FA.results<-fa.sort(FA.results,polar=FALSE) loadfa<-round(as(FA.results$loadings, "matrix"),3) loadfa[which(abs(loadfa)communaute - c("communaute", txt_specificity, txt_complexity)->names(communaute) - Resultats[[desc_standardized_saturation_on_correlation_matrix]]<-data.frame(loadfa, communaute) + c("communaute", .dico[["txt_specificity"]], .dico[["txt_complexity"]])->names(communaute) + Resultats[[.dico[["desc_standardized_saturation_on_correlation_matrix"]]]]<-data.frame(loadfa, communaute) var.ex <- round(FA.results$Vaccounted,3) - if(nF>1){dimnames(var.ex)[[1]]<-c(txt_saturations_sum_of_squares, txt_explained_variance_ratio, - txt_cumulated_explained_variance_ratio, txt_explaination_ratio, - txt_cumulated_explaination_ratio)} else { - dimnames(var.ex)[[1]]<-c(txt_saturations_sum_of_squares, txt_explained_variance_ratio) + if(nF>1){dimnames(var.ex)[[1]]<-c(.dico[["txt_saturations_sum_of_squares"]], .dico[["txt_explained_variance_ratio"]], + .dico[["txt_cumulated_explained_variance_ratio"]], .dico[["txt_explaination_ratio"]], + .dico[["txt_cumulated_explaination_ratio"]])} else { + dimnames(var.ex)[[1]]<-c(.dico[["txt_saturations_sum_of_squares"]], .dico[["txt_explained_variance_ratio"]]) } - Resultats[[txt_explained_variance]]<-var.ex + Resultats[[.dico[["txt_explained_variance"]]]]<-var.ex paste("ML",1:nF)->noms1 if(nF>1 & rotation=="oblimin"){ round(FA.results$Phi, 3)->cor.f - Resultats[[txt_correlations_between_factors]]<-cor.f} - paste(txt_mean_complexity_is, round(mean(FA.results$complexity),3), txt_this_tests_if, nF, txt_sufficient_factors )-> Resultats[[txt_mean_complexity]] + Resultats[[.dico[["txt_correlations_between_factors"]]]]<-cor.f} + paste(.dico[["txt_mean_complexity_is"]], round(mean(FA.results$complexity),3), .dico[["txt_this_tests_if"]], nF, .dico[["txt_sufficient_factors"]] )-> Resultats[[.dico[["txt_mean_complexity"]]]] if(length(X)>5){ round(matrix(c(FA.results$null.chisq, FA.results$null.dof,FA.results$null.model, FA.results$dof, FA.results$objective, FA.results$RMSEA, FA.results$TLI,FA.results$BIC, FA.results$SABIC,FA.results$rms, FA.results$crms, FA.results$fit.off, FA.results$chi, FA.results$EPVAL, FA.results$STATISTIC, FA.results$PVAL, FA.results$n.obs), ncol=1),4)->stats - c(txt_chi_squared_null_model, txt_null_model_degrees_of_freedom, txt_objective_function_of_null_model, - txt_model_degrees_of_freedom, txt_objective_function_of_model, "RMSEA", txt_lower_bound_rmsea, txt_upper_bound_rmsea, - txt_confidance_threshold, txt_tucker_lewis_fiability_factor, "BIC", "EBIC", - "RMSR", "RMSR corrige", txt_adequation_outside_diagonal, txt_chi_squared_empirical, txt_empirical_chi_square_proba_value, - txt_chi_squared_likelihood_max, txt_max_likelihood_chi_squared_proba_value, desc_total_observations)->dimnames(stats)[[1]] - - txt_values->dimnames(stats)[[2]] - stats->Resultats[[txt_adequation_adjustement_indexes]] + c(.dico[["txt_chi_squared_null_model"]], .dico[["txt_null_model_degrees_of_freedom"]], .dico[["txt_objective_function_of_null_model"]], + .dico[["txt_model_degrees_of_freedom"]], .dico[["txt_objective_function_of_model"]], "RMSEA", .dico[["txt_lower_bound_rmsea"]], .dico[["txt_upper_bound_rmsea"]], + .dico[["txt_confidance_threshold"]], .dico[["txt_tucker_lewis_fiability_factor"]], "BIC", "EBIC", + "RMSR", "RMSR corrige", .dico[["txt_adequation_outside_diagonal"]], .dico[["txt_chi_squared_empirical"]], .dico[["txt_empirical_chi_square_proba_value"]], + .dico[["txt_chi_squared_likelihood_max"]], .dico[["txt_max_likelihood_chi_squared_proba_value"]], .dico[["desc_total_observations"]])->dimnames(stats)[[1]] + + .dico[["txt_values"]]->dimnames(stats)[[2]] + stats->Resultats[[.dico[["txt_adequation_adjustement_indexes"]]]] if(all(FA.results$R2<1)){ round(rbind((FA.results$R2)^0.5,FA.results$R2,2*FA.results$R2-1),2)->stats - dimnames(stats)[[1]]<-c(txt_correlation_between_scores_and_factors, txt_multiple_r_square_of_factors_scores, - txt_min_correlation_between_scores_and_factors) + dimnames(stats)[[1]]<-c(.dico[["txt_correlation_between_scores_and_factors"]], .dico[["txt_multiple_r_square_of_factors_scores"]], + .dico[["txt_min_correlation_between_scores_and_factors"]]) dimnames(stats)[[2]]<-noms1 - stats->Resultats[[txt_correlation_between_scores_and_factors]] + stats->Resultats[[.dico[["txt_correlation_between_scores_and_factors"]]]] } if(n.boot>1) { @@ -360,10 +360,10 @@ factor.an <- cbind(round(FA.results$cis$ci[,i],3), round(as(FA.results$loadings, "matrix"),3)[,i], round(FA.results$cis$ci[,i+nF],3))->IC2 - dimnames(IC2)[[2]]<-c(txt_inferior_limit, dimnames(FA.results$loadings)[[2]][i],txt_ci_superior_limit) + dimnames(IC2)[[2]]<-c(.dico[["txt_inferior_limit"]], dimnames(FA.results$loadings)[[2]][i],.dico[["txt_ci_superior_limit"]]) cbind(IC, IC2)->IC } - IC->Resultats[[txt_confidence_interval_of_saturations_on_bootstrap]] + IC->Resultats[[.dico[["txt_confidence_interval_of_saturations_on_bootstrap"]]]] } } print(fa.diagram(FA.results))#representation graphique des saturations} @@ -377,14 +377,14 @@ factor.an <- } data<-data.frame(data,Scores.fac) - names(data)[(length(data)+1-nF):length(data)]<-paste0(txt_factor, 1:nF) + names(data)[(length(data)+1-nF):length(data)]<-paste0(.dico[["txt_factor"]], 1:nF) assign(nom, data,envir=.GlobalEnv) } if(hier) { if(cor!="cor") poly<-TRUE else poly<-FALSE - Resultats[[txt_hierarchical_factorial_analysis]]$Omega<-psych::omega(data[,X], nfactors=nF, n.iter=n.boot,fm=methode, poly=poly, flip=T, digits=3, sl=T, plot=T, n.obs=length(data[,1]), rotate=rotation) + Resultats[[.dico[["txt_hierarchical_factorial_analysis"]]]]$Omega<-psych::omega(data[,X], nfactors=nF, n.iter=n.boot,fm=methode, poly=poly, flip=T, digits=3, sl=T, plot=T, n.obs=length(data[,1]), rotate=rotation) multi<-fa.multi(Matrice, nfactors=nF, nfact2=nfact2, n.iter=1,fm=methode, n.obs=length(data[,1]), rotate=rotation) multi$f2->FA.results @@ -394,36 +394,36 @@ factor.an <- data.frame("communaute"=round(FA.results$communality,3), txt_specificity=round(FA.results$uniquenesses,3), txt_complexity=round(FA.results$complexity,2))->communaute - c("communaute", txt_specificity, txt_complexity)->names(communaute) - Resultats[[txt_hierarchical_factorial_analysis]][[desc_standardized_saturation_on_correlation_matrix]]<-data.frame(loadfa, communaute) + c("communaute", .dico[["txt_specificity"]], .dico[["txt_complexity"]])->names(communaute) + Resultats[[.dico[["txt_hierarchical_factorial_analysis"]]]][[.dico[["desc_standardized_saturation_on_correlation_matrix"]]]]<-data.frame(loadfa, communaute) var.ex <- round(FA.results$Vaccounted,3) - if(nfact2>1){dimnames(var.ex)[[1]]<-c(txt_saturations_sum_of_squares, txt_explained_variance_ratio, - txt_cumulated_explained_variance_ratio, txt_explaination_ratio, - txt_cumulated_explaination_ratio)} else { - dimnames(var.ex)[[1]]<-c(txt_saturations_sum_of_squares, txt_explained_variance_ratio) + if(nfact2>1){dimnames(var.ex)[[1]]<-c(.dico[["txt_saturations_sum_of_squares"]], .dico[["txt_explained_variance_ratio"]], + .dico[["txt_cumulated_explained_variance_ratio"]], .dico[["txt_explaination_ratio"]], + .dico[["txt_cumulated_explaination_ratio"]])} else { + dimnames(var.ex)[[1]]<-c(.dico[["txt_saturations_sum_of_squares"]], .dico[["txt_explained_variance_ratio"]]) } - Resultats[[txt_hierarchical_factorial_analysis]][[txt_explained_variance]]<-var.ex + Resultats[[.dico[["txt_hierarchical_factorial_analysis"]]]][[.dico[["txt_explained_variance"]]]]<-var.ex paste("ML",1:nfact2)->noms1 - paste(txt_mean_complexity_is, round(mean(FA.results$complexity),3), txt_this_tests_if, nF, txt_sufficient_factors )-> Resultats[[txt_mean_complexity]] + paste(.dico[["txt_mean_complexity_is"]], round(mean(FA.results$complexity),3), .dico[["txt_this_tests_if"]], nF, .dico[["txt_sufficient_factors"]] )-> Resultats[[.dico[["txt_mean_complexity"]]]] round(matrix(c( FA.results$null.dof,FA.results$null.model, FA.results$dof, FA.results$objective, FA.results$rms, FA.results$fit.off), ncol=1),4)->stats - c( txt_null_model_degrees_of_freedom, txt_objective_function_of_null_model, - txt_model_degrees_of_freedom, txt_objective_function_of_model, "RMSR", - txt_adequation_outside_diagonal)->dimnames(stats)[[1]] + c( .dico[["txt_null_model_degrees_of_freedom"]], .dico[["txt_objective_function_of_null_model"]], + .dico[["txt_model_degrees_of_freedom"]], .dico[["txt_objective_function_of_model"]], "RMSR", + .dico[["txt_adequation_outside_diagonal"]])->dimnames(stats)[[1]] - txt_values->dimnames(stats)[[2]] - stats->Resultats[[txt_hierarchical_factorial_analysis]][[txt_adequation_adjustement_indexes]] + .dico[["txt_values"]]->dimnames(stats)[[2]] + stats->Resultats[[.dico[["txt_hierarchical_factorial_analysis"]]]][[.dico[["txt_adequation_adjustement_indexes"]]]] if(all(FA.results$R2<1)){ round(rbind((FA.results$R2)^0.5,FA.results$R2,2*FA.results$R2-1),2)->stats - dimnames(stats)[[1]]<-c(txt_correlation_between_scores_and_factors, txt_multiple_r_square_of_factors_scores, - txt_min_correlation_between_scores_and_factors) + dimnames(stats)[[1]]<-c(.dico[["txt_correlation_between_scores_and_factors"]], .dico[["txt_multiple_r_square_of_factors_scores"]], + .dico[["txt_min_correlation_between_scores_and_factors"]]) dimnames(stats)[[2]]<-noms1 - stats->Resultats[[txt_hierarchical_factorial_analysis]][[txt_correlation_between_scores_and_factors]] + stats->Resultats[[.dico[["txt_hierarchical_factorial_analysis"]]]][[.dico[["txt_correlation_between_scores_and_factors"]]]] fa.multi.diagram(multi) } } @@ -433,8 +433,8 @@ factor.an <- acp.out<-function(Matrice, data, X, nF, methode, rotation, sat, scor.fac, nom){ principal(Matrice, nfactors= nF, n.obs=length(data[,1]), rotate=rotation)->PCA list()->Resultats - Resultats$analyse<-paste(txt_principal_analysis_using_psych_with_algo, PCA$fm) - if(!is.null(rotation)) Resultats$rotation<-paste(txt_rotation_is_a_rotation, rotation) + Resultats$analyse<-paste(.dico[["txt_principal_analysis_using_psych_with_algo"]], PCA$fm) + if(!is.null(rotation)) Resultats$rotation<-paste(.dico[["txt_rotation_is_a_rotation"]], rotation) PCA<-fa.sort(PCA,polar=FALSE) loadfa<-round(as(PCA$loadings, "matrix"),3) @@ -442,33 +442,33 @@ factor.an <- data.frame("communaute"=round(PCA$communality,3), txt_specificity=round(PCA$uniquenesses,3), txt_complexity=round(PCA$complexity,2))->communaute - c("communaute", txt_specificity, txt_complexity)->names(communaute) - Resultats[[desc_standardized_saturation_on_correlation_matrix]]<-data.frame(loadfa, communaute) + c("communaute", .dico[["txt_specificity"]], .dico[["txt_complexity"]])->names(communaute) + Resultats[[.dico[["desc_standardized_saturation_on_correlation_matrix"]]]]<-data.frame(loadfa, communaute) var.ex<-round(PCA$Vaccounted,3) - if(nF>1){dimnames(var.ex)[[1]]<-c(txt_saturations_sum_of_squares, txt_explained_variance_ratio, - txt_cumulated_explained_variance_ratio, txt_explaination_ratio, - txt_cumulated_explaination_ratio)} else { - dimnames(var.ex)[[1]]<-c(txt_saturations_sum_of_squares, txt_explained_variance_ratio) + if(nF>1){dimnames(var.ex)[[1]]<-c(.dico[["txt_saturations_sum_of_squares"]], .dico[["txt_explained_variance_ratio"]], + .dico[["txt_cumulated_explained_variance_ratio"]], .dico[["txt_explaination_ratio"]], + .dico[["txt_cumulated_explaination_ratio"]])} else { + dimnames(var.ex)[[1]]<-c(.dico[["txt_saturations_sum_of_squares"]], .dico[["txt_explained_variance_ratio"]]) } - Resultats[[txt_explained_variance]]<-var.ex + Resultats[[.dico[["txt_explained_variance"]]]]<-var.ex paste("TC",1:nF)->noms1 if(nF>1 & rotation=="oblimin"){ round(PCA$r.scores,3)->cor.f - Resultats[[txt_correlations_between_factors]]<-cor.f} - paste(txt_mean_complexity_is, mean(PCA$complexity), txt_this_tests_if, nF, txt_sufficient_factors )-> Resultats[[txt_mean_complexity]] + Resultats[[.dico[["txt_correlations_between_factors"]]]]<-cor.f} + paste(.dico[["txt_mean_complexity_is"]], mean(PCA$complexity), .dico[["txt_this_tests_if"]], nF, .dico[["txt_sufficient_factors"]] )-> Resultats[[.dico[["txt_mean_complexity"]]]] round(matrix(c(PCA$null.dof,PCA$null.model, PCA$dof, PCA$objective, PCA$rms, PCA$fit.off, PCA$chi, PCA$EPVAL, PCA$STATISTIC, PCA$PVAL, PCA$n.obs), ncol=1),4)->stats - c(txt_null_model_degrees_of_freedom, txt_objective_function_of_null_model,txt_model_degrees_of_freedom, txt_objective_function_of_model, - "RMSR", txt_adequation_outside_diagonal, txt_chi_squared_empirical, txt_empirical_chi_square_proba_value, - txt_chi_squared_likelihood_max, txt_max_likelihood_chi_squared_proba_value, desc_total_observations)->dimnames(stats)[[1]] + c(.dico[["txt_null_model_degrees_of_freedom"]], .dico[["txt_objective_function_of_null_model"]],.dico[["txt_model_degrees_of_freedom"]], .dico[["txt_objective_function_of_model"]], + "RMSR", .dico[["txt_adequation_outside_diagonal"]], .dico[["txt_chi_squared_empirical"]], .dico[["txt_empirical_chi_square_proba_value"]], + .dico[["txt_chi_squared_likelihood_max"]], .dico[["txt_max_likelihood_chi_squared_proba_value"]], .dico[["desc_total_observations"]])->dimnames(stats)[[1]] - txt_values->dimnames(stats)[[2]] - stats->Resultats[[txt_adequation_adjustement_indexes]] + .dico[["txt_values"]]->dimnames(stats)[[2]] + stats->Resultats[[.dico[["txt_adequation_adjustement_indexes"]]]] if(scor.fac){ Scores.fac<-c() sapply(data[,X], scale)->centrees @@ -479,7 +479,7 @@ factor.an <- cbind(Scores.fac,centrees2)->Scores.fac } data<-data.frame(data,Scores.fac) - names(data)[(length(data)+1-nF):length(data)]<-paste0(txt_factor, 1:nF) + names(data)[(length(data)+1-nF):length(data)]<-paste0(.dico[["txt_factor"]], 1:nF) assign(nom, data,envir=.GlobalEnv) } @@ -512,18 +512,18 @@ factor.an <- cor<-fa.options$cor hier<-fa.options$hier nfact2<-fa.options$nfact2 - Resultats[[txt_correlations_matrix]]<-fa.options[[txt_correlations_matrix]] - Resultats[[txt_adequation_measurement_of_matrix]]<-fa.options[[txt_adequation_measurement_of_matrix]] - Resultats[[txt_parallel_analysis]]<-fa.options[[txt_parallel_analysis]] + Resultats[[.dico[["txt_correlations_matrix"]]]]<-fa.options[[.dico[["txt_correlations_matrix"]]]] + Resultats[[.dico[["txt_adequation_measurement_of_matrix"]]]]<-fa.options[[.dico[["txt_adequation_measurement_of_matrix"]]]] + Resultats[[.dico[["txt_parallel_analysis"]]]]<-fa.options[[.dico[["txt_parallel_analysis"]]]] - if(fa.options$choix== txt_factorial_exploratory_analysis |choix=="afe"){ - Resultats[[txt_factorial_analysis]]<-fa.out(Matrice=Matrice, data=data, X=X, nF=nF, methode=methode, rotation=rotation, sat=sat, + if(fa.options$choix== .dico[["txt_factorial_exploratory_analysis"]] |choix=="afe"){ + Resultats[[.dico[["txt_factorial_analysis"]]]]<-fa.out(Matrice=Matrice, data=data, X=X, nF=nF, methode=methode, rotation=rotation, sat=sat, scor.fac=scor.fac, n.boot=n.boot, nom=nom, hier=hier, cor=cor, nfact2=nfact2) } - if(fa.options$choix== txt_principal_component_analysis |choix=="acp"){ - Resultats[[txt_principal_component_analysis]]<-acp.out(Matrice=Matrice, data=data, X=X, nF=nF, methode=methode, rotation=rotation, sat=sat, scor.fac=scor.fac, nom=nom) + if(fa.options$choix== .dico[["txt_principal_component_analysis"]] |choix=="acp"){ + Resultats[[.dico[["txt_principal_component_analysis"]]]]<-acp.out(Matrice=Matrice, data=data, X=X, nF=nF, methode=methode, rotation=rotation, sat=sat, scor.fac=scor.fac, nom=nom) } @@ -540,7 +540,7 @@ factor.an <- if(fa.options$sauvegarde) save(Resultats=Resultats, choix=fa.options$choix, env=.e) - ref1(packages)->Resultats[[desc_references]] + ref1(packages)->Resultats[[.dico[["desc_references"]]]] if(html) ez.html(Resultats) return(Resultats) diff --git a/R/fiabilite.R b/R/fiabilite.R index e4e0a24..e8e11fc 100644 --- a/R/fiabilite.R +++ b/R/fiabilite.R @@ -2,7 +2,7 @@ fiabilite <- - function(X=NULL,Y=NULL, data=NULL, choix=NULL, ord=NULL,outlier=txt_complete_dataset, keys=NULL, n.boot=NULL, sauvegarde=F, + function(X=NULL,Y=NULL, data=NULL, choix=NULL, ord=NULL,outlier=.dico[["txt_complete_dataset"]], keys=NULL, n.boot=NULL, sauvegarde=F, imp=NULL, html=TRUE){ # choix @@ -15,10 +15,10 @@ fiabilite <- .e<- environment() Resultats<-list() if(is.null(data) | is.null(X)) {dial<-TRUE}else dial<-F - if(dial || is.null(choix) || length(choix)!=1 ||choix %in% c(txt_cronbach_alpha,"alpha","ICC","CCK",txt_intraclass_correlation,txt_kendall_coeff)==FALSE){ + if(dial || is.null(choix) || length(choix)!=1 ||choix %in% c(.dico[["txt_cronbach_alpha"]],"alpha","ICC","CCK",.dico[["txt_intraclass_correlation"]],.dico[["txt_kendall_coeff"]])==FALSE){ dial<-T - writeLines(ask_chose_analysis) - dlgList(c(txt_cronbach_alpha, txt_intraclass_correlation,txt_kendall_coeff), preselect=NULL, multiple = FALSE, title=ask_analysis_type)$res->choix + writeLines(.dico[["ask_chose_analysis"]]) + dlgList(c(.dico[["txt_cronbach_alpha"]], .dico[["txt_intraclass_correlation"]],.dico[["txt_kendall_coeff"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_analysis_type"]])$res->choix if(length(choix)==0) return(analyse()) } @@ -32,16 +32,16 @@ fiabilite <- deparse(substitute(data))->nom } - if(choix=="CCK" | choix==txt_kendall_coeff){ - msg3<-ask_chose_first_judge + if(choix=="CCK" | choix==.dico[["txt_kendall_coeff"]]){ + msg3<-.dico[["ask_chose_first_judge"]] type<-"factor" - title<-txt_judge1 + title<-.dico[["txt_judge1"]] multiple<-T } else{ multiple<-T - msg3<-ask_chose_variable + msg3<-.dico[["ask_chose_variable"]] type<-"numeric" - title<-txt_variables + title<-.dico[["txt_variables"]] } X<-.var.type(X=X, data=data, type=type, check.prod=F, message=msg3, multiple=multiple, title=title, out=NULL) @@ -51,41 +51,41 @@ fiabilite <- data<-X$data X<-X$X - if(choix %in% c(txt_cronbach_alpha,txt_intraclass_correlation,"ICC","alpha") ){ - if(dial || length(outlier)>1 || outlier %in% c(txt_complete_dataset, txt_without_outliers) ==FALSE){ - writeLines(ask_analysis_on_complete_data_or_remove_outliers) - writeLines(desc_outliers_identified_on_mahalanobis) - outlier<- dlgList(c(txt_complete_dataset, txt_without_outliers), preselect=txt_complete_dataset,multiple = FALSE, title=ask_results_desired)$res + if(choix %in% c(.dico[["txt_cronbach_alpha"]],.dico[["txt_intraclass_correlation"]],"ICC","alpha") ){ + if(dial || length(outlier)>1 || outlier %in% c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]) ==FALSE){ + writeLines(.dico[["ask_analysis_on_complete_data_or_remove_outliers"]]) + writeLines(.dico[["desc_outliers_identified_on_mahalanobis"]]) + outlier<- dlgList(c(.dico[["txt_complete_dataset"]], .dico[["txt_without_outliers"]]), preselect=.dico[["txt_complete_dataset"]],multiple = FALSE, title=.dico[["ask_results_desired"]])$res if(length(outlier)==0) { Resultats<-fiabilite() return(Resultats)} } - if(outlier==txt_without_outliers){ + if(outlier==.dico[["txt_without_outliers"]]){ inf<-VI.multiples(data[,X]) - Resultats[[txt_labeled_outliers]]<-inf[[txt_labeled_outliers]] + Resultats[[.dico[["txt_labeled_outliers"]]]]<-inf[[.dico[["txt_labeled_outliers"]]]] data<-inf$data } - if(choix %in% c(txt_cronbach_alpha,"alpha")) { + if(choix %in% c(.dico[["txt_cronbach_alpha"]],"alpha")) { if(dial){ - writeLines(ask_variables_types_correlations) - type<-dlgList(c(txt_dichotomic_ordinal, txt_continuous, "mixte"), preselect=NULL, multiple = FALSE, title=ask_variables_type)$res + writeLines(.dico[["ask_variables_types_correlations"]]) + type<-dlgList(c(.dico[["txt_dichotomic_ordinal"]], .dico[["txt_continuous"]], "mixte"), preselect=NULL, multiple = FALSE, title=.dico[["ask_variables_type"]])$res if(length(type)==0) {Resultats<-fiabilite() return(Resultats) - }} else{if(is.null(ord)) type<-txt_continuous else type<-txt_dichotomic_ordinal} + }} else{if(is.null(ord)) type<-.dico[["txt_continuous"]] else type<-.dico[["txt_dichotomic_ordinal"]]} if(dial){ - writeLines(ask_are_there_inversed_items) - rev<-dlgList(c(TRUE,FALSE), multiple = FALSE, title=ask_inversed_items)$res + writeLines(.dico[["ask_are_there_inversed_items"]]) + rev<-dlgList(c(TRUE,FALSE), multiple = FALSE, title=.dico[["ask_inversed_items"]])$res if(length(rev)==0) { Resultats<-fiabilite() return(Resultats) } } if(rev=="TRUE" || !is.null(keys) && any(keys %in% X==FALSE)){ - writeLines(ask_specify_inverted_item) - keys<-dlgList(X, multiple = TRUE, title=ask_inversed_items)$res + writeLines(.dico[["ask_specify_inverted_item"]]) + keys<-dlgList(X, multiple = TRUE, title=.dico[["ask_inversed_items"]])$res if(length(keys)==0) { Resultats<-fiabilite() return(Resultats) @@ -94,21 +94,21 @@ fiabilite <- - if(type==txt_continuous){ + if(type==.dico[["txt_continuous"]]){ if(!is.null(n.boot) && ((class(n.boot)!="numeric" & class(n.boot)!="integer") || n.boot%%1!=0 || n.boot<1)){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } while(is.null(n.boot)){ - writeLines(ask_bootstrap_numbers_1_for_none) - n.boot<-dlgInput(ask_bootstraps_number, 1)$res + writeLines(.dico[["ask_bootstrap_numbers_1_for_none"]]) + n.boot<-dlgInput(.dico[["ask_bootstraps_number"]], 1)$res if(length(n.boot)==0) {Resultats<-fiabilite() return(Resultats)} strsplit(n.boot, ":")->n.boot tail(n.boot[[1]],n=1)->n.boot as.numeric(n.boot)->n.boot if(is.na(n.boot) || n.boot%%1!=0 || n.boot<1){ - msgBox(desc_bootstraps_number_must_be_positive) + msgBox(.dico[["desc_bootstraps_number_must_be_positive"]]) n.boot<-NULL } } @@ -116,8 +116,8 @@ fiabilite <- }else{ n.boot<-0 if(type=="mixte") { - writeLines(ask_ordinal_variables) - ord<-dlgList(X, multiple = TRUE, title=ask_ordinal_variables)$res + writeLines(.dico[["ask_ordinal_variables"]]) + ord<-dlgList(X, multiple = TRUE, title=.dico[["ask_ordinal_variables"]])$res if(length(ord)==0){ Resultats<-fiabilite() return(Resultats) @@ -125,45 +125,45 @@ fiabilite <- }else ord<-X Matrice<-tetrapoly(data=data[,X],X=X,info=T, ord=ord,group=NULL,estimator='two.step',output='cor', imp=imp,html=F)[[1]] if(all(class(Matrice)!="matrix")) { - sortie<-dlgMessage(ask_exit_because_of_alpha_on_non_matrix, type="yesno")$res + sortie<-dlgMessage(.dico[["ask_exit_because_of_alpha_on_non_matrix"]], type="yesno")$res if(sortie=="yes") return(analyse()) else Matrice<-tetrapoly(data=data[,X],X=X,info=T, ord=ord,group=NULL,estimator='two.step',output='cor', imp="rm")[[1]] } psych::alpha(Matrice, keys=keys,n.obs=length(data[,1]))->cron } - round(cron$total,3)->Resultats[[txt_cronbach_alpha_on_whole_scale]] - if(n.boot>1) cron$boot.ci->Resultats[[txt_confidence_interval_on_bootstrap]] + round(cron$total,3)->Resultats[[.dico[["txt_cronbach_alpha_on_whole_scale"]]]] + if(n.boot>1) cron$boot.ci->Resultats[[.dico[["txt_confidence_interval_on_bootstrap"]]]] cron$total[,1]->a1 cron$total[,6]->ase - data.frame(Lim.inf.IC.95=a1-1.96*ase, alpha=a1, Lim.sup.IC.95=a1+1.96*ase)->Resultats[[txt_confidence_interval_on_standard_error]] - round(data.frame(cron$alpha.drop, cron$item.stats ),3)->Resultats[[txt_fiability_by_removed_item]] + data.frame(Lim.inf.IC.95=a1-1.96*ase, alpha=a1, Lim.sup.IC.95=a1+1.96*ase)->Resultats[[.dico[["txt_confidence_interval_on_standard_error"]]]] + round(data.frame(cron$alpha.drop, cron$item.stats ),3)->Resultats[[.dico[["txt_fiability_by_removed_item"]]]] } - if(choix==txt_intraclass_correlation| choix=="ICC"){psych::ICC(data[,X], missing=FALSE)->ICC.out - ICC.out[[1]]->Resultats[[txt_intraclass_correlation]] - names(Resultats[[txt_intraclass_correlation]])<-c("type", "ICC", "F", txt_df1, txt_df2, txt_p_dot_val, txt_inferior_limit,txt_ci_superior_limit) - Resultats$"informations"<-paste(desc_number_of_judge_is, length(X), txt_and_the_number_of_obs, ICC.out$n.obs) } + if(choix==.dico[["txt_intraclass_correlation"]]| choix=="ICC"){psych::ICC(data[,X], missing=FALSE)->ICC.out + ICC.out[[1]]->Resultats[[.dico[["txt_intraclass_correlation"]]]] + names(Resultats[[.dico[["txt_intraclass_correlation"]]]])<-c("type", "ICC", "F", .dico[["txt_df1"]], .dico[["txt_df2"]], .dico[["txt_p_dot_val"]], .dico[["txt_inferior_limit"]],.dico[["txt_ci_superior_limit"]]) + Resultats$"informations"<-paste(.dico[["desc_number_of_judge_is"]], length(X), .dico[["txt_and_the_number_of_obs"]], ICC.out$n.obs) } } - if(choix==txt_kendall_coeff){ - msg4<-ask_chose_second_judge - Y<-.var.type(X=Y, data=data, type=type, check.prod=F, message=msg4, multiple=F, title=txt_judge2, out=X) + if(choix==.dico[["txt_kendall_coeff"]]){ + msg4<-.dico[["ask_chose_second_judge"]] + Y<-.var.type(X=Y, data=data, type=type, check.prod=F, message=msg4, multiple=F, title=.dico[["txt_judge2"]], out=X) if(is.null(Y)) { Resultats<-fiabilite() return(Resultats)} data<-Y$data Y<-Y$X cohen.kappa(data[,c(X,Y)], w=NULL,n.obs=NULL,alpha=.05)->CK.out - dimnames(CK.out$confid)<-list(c(txt_non_pondered_coeff,txt_pondered_kappa),c(txt_inferior_limit,txt_estimation,txt_ci_superior_limit)) - round(CK.out$confid,3)->Resultats[[txt_kendall_coeff]] - CK.out$agree->Resultats[[txt_agreement]] - Resultats$information<-paste(desc_number_of_observations_is, CK.out$n.obs) + dimnames(CK.out$confid)<-list(c(.dico[["txt_non_pondered_coeff"]],.dico[["txt_pondered_kappa"]]),c(.dico[["txt_inferior_limit"]],.dico[["txt_estimation"]],.dico[["txt_ci_superior_limit"]])) + round(CK.out$confid,3)->Resultats[[.dico[["txt_kendall_coeff"]]]] + CK.out$agree->Resultats[[.dico[["txt_agreement"]]]] + Resultats$information<-paste(.dico[["desc_number_of_observations_is"]], CK.out$n.obs) } - if(dial) dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=ask_save_results)$res->sauvegarde + if(dial) dlgList(c("TRUE","FALSE"), preselect="FALSE", multiple = FALSE, title=.dico[["ask_save_results"]])$res->sauvegarde if(length(sauvegarde)==0) { Resultats<-fiabilite() return(Resultats) @@ -182,7 +182,7 @@ fiabilite <- if(sauvegarde)save(Resultats=Resultats, choix=choix, env=.e) - ref1(packages)->Resultats[[txt_references]] + ref1(packages)->Resultats[[.dico[["txt_references"]]]] if(html) try(ez.html(Resultats), silent=T) return(Resultats) } diff --git a/R/graphiques.R b/R/graphiques.R index 3c0391d..c1e92f2 100644 --- a/R/graphiques.R +++ b/R/graphiques.R @@ -9,7 +9,7 @@ function(){ nom<-data[[1]] data<-data[[2]] - msgBox(desc_close_browser_to_come_back) + msgBox(.dico[["desc_close_browser_to_come_back"]]) print(ref1(packages)) if (Sys.info()[[1]]=='Darwin') { options(browser = 'open') diff --git a/R/import.R b/R/import.R index e450429..87d9c2f 100644 --- a/R/import.R +++ b/R/import.R @@ -16,14 +16,14 @@ import <- c('svDialogs', 'readxl','foreign', 'textclean')->packages lapply(packages, require,character.only=T) Resultats <- list() - if(info==TRUE) print(ask_file_format_to_import) + if(info==TRUE) print(.dico[["ask_file_format_to_import"]]) if(!is.null(type)){ - if (type=="csv") txt_csv_file -> type - if (type=="txt") txt_txt_file -> type - if (type=="excel") txt_excel_file -> type - if (type=="SPSS") txt_spss_file -> type + if (type=="csv") .dico[["txt_csv_file"]] -> type + if (type=="txt") .dico[["txt_txt_file"]] -> type + if (type=="excel") .dico[["txt_excel_file"]] -> type + if (type=="SPSS") .dico[["txt_spss_file"]] -> type } - if(is.null(type)) type <- dlgList(c(txt_csv_file, txt_txt_file, txt_excel_file, txt_spss_file), preselect=txt_excel_file, multiple = FALSE, title=ask_file_format)$res + if(is.null(type)) type <- dlgList(c(.dico[["txt_csv_file"]], .dico[["txt_txt_file"]], .dico[["txt_excel_file"]], .dico[["txt_spss_file"]]), preselect=.dico[["txt_excel_file"]], multiple = FALSE, title=.dico[["ask_file_format"]])$res if(length(type)==0) return(donnees()) if(!is.null(dir)) try(setwd(dir), silent=T) @@ -42,45 +42,45 @@ import <- - if(type!=txt_spss_file){ + if(type!=.dico[["txt_spss_file"]]){ if(dial | (dec %in% c(".",","))==FALSE | (sep %in% c(" ", "\t",";",","))==FALSE |!is.logical(header)){ - if(info==TRUE) print(ask_headers_in_database) - header <- dlgList(c(TRUE, FALSE), preselect=TRUE, multiple = FALSE, title=ask_variables_names)$res + if(info==TRUE) print(.dico[["ask_headers_in_database"]]) + header <- dlgList(c(TRUE, FALSE), preselect=TRUE, multiple = FALSE, title=.dico[["ask_variables_names"]])$res if(length(header)==0) return(import()) - if(info==TRUE) print(ask_missing_values_value_na_on_empty) - na.strings <- dlgInput(ask_value_for_missing_values, "NA")$res + if(info==TRUE) print(.dico[["ask_missing_values_value_na_on_empty"]]) + na.strings <- dlgInput(.dico[["ask_value_for_missing_values"]], "NA")$res if(length(na.strings)==0) na.strings <- "NA" na.strings <- strsplit(na.strings, ":") na.strings <- tail(na.strings[[1]],n=1) - if(type==txt_csv_file|type==txt_txt_file){ - if(info==TRUE) print(ask_col_separation_index) - sep <- dlgList(c(txt_space,"tab",txt_semicolon,txt_comma), preselect=txt_semicolon, multiple = FALSE, title=txt_col_separator)$res + if(type==.dico[["txt_csv_file"]]|type==.dico[["txt_txt_file"]]){ + if(info==TRUE) print(.dico[["ask_col_separation_index"]]) + sep <- dlgList(c(.dico[["txt_space"]],"tab",.dico[["txt_semicolon"]],.dico[["txt_comma"]]), preselect=.dico[["txt_semicolon"]], multiple = FALSE, title=.dico[["txt_col_separator"]])$res if(length(sep)==0) return(import()) - m1 <- matrix(c(txt_space,"tab",txt_semicolon,txt_comma," ","\t",";",","),nrow=4) + m1 <- matrix(c(.dico[["txt_space"]],"tab",.dico[["txt_semicolon"]],.dico[["txt_comma"]]," ","\t",";",","),nrow=4) sep <- subset(m1, m1[,1] %in% sep)[,2] - if(info==TRUE) print(ask_decimal_symbol) - dec <- dlgList(c(txt_dot, txt_comma), preselect=NULL, multiple = FALSE, title=txt_decimal_separator)$res + if(info==TRUE) print(.dico[["ask_decimal_symbol"]]) + dec <- dlgList(c(.dico[["txt_dot"]], .dico[["txt_comma"]]), preselect=NULL, multiple = FALSE, title=.dico[["txt_decimal_separator"]])$res if(length(dec)==0) return(import()) - m1 <- matrix(c(txt_dot, txt_comma,".",","),nrow=2) + m1 <- matrix(c(.dico[["txt_dot"]], .dico[["txt_comma"]],".",","),nrow=2) dec <- subset(m1, m1[,1] %in% dec)[,2] } } } - if(type==txt_spss_file) { + if(type==.dico[["txt_spss_file"]]) { #basename(file)->file data1<-read.spss(file, to.data.frame=TRUE) col.char <-sapply(data1, is.factor) if(any(col.char)) data1[col.char] <- lapply(data1[which(col.char)], factor) } - if(type==txt_csv_file) data1 <- read.csv2(file, header=as.logical(header), sep=sep, dec=dec, na.strings=na.strings) - if(type==txt_txt_file) data1 <- read.table(file, header=as.logical(header), sep=sep, dec=dec, na.strings=na.strings) - if(type==txt_excel_file){ + if(type==.dico[["txt_csv_file"]]) data1 <- read.csv2(file, header=as.logical(header), sep=sep, dec=dec, na.strings=na.strings) + if(type==.dico[["txt_txt_file"]]) data1 <- read.table(file, header=as.logical(header), sep=sep, dec=dec, na.strings=na.strings) + if(type==.dico[["txt_excel_file"]]){ basename(file)->file - writeLines(ask_specify_datasheet_to_import) + writeLines(.dico[["ask_specify_datasheet_to_import"]]) if(is.null(sheet) || (sheet %in% excel_sheets(file))==FALSE){ eval(parse(text=paste0(" dlgList( excel_sheets('",file, "'), preselect=FALSE, multiple = FALSE, title='Quelle feuille ?')$res->sheet"))) @@ -92,14 +92,14 @@ import <- if(any(col.char)) data1[col.char] <- lapply(data1[which(col.char)], factor) } if(dial) { - if(type==txt_excel_file) name<-sheet else name<-file - name <- dlgInput(ask_name_for_dataset, name)$res + if(type==.dico[["txt_excel_file"]]) name<-sheet else name<-file + name <- dlgInput(.dico[["ask_name_for_dataset"]], name)$res if(length(name)==0) name <- "data1" name <- strsplit(name, ":") name <- tail(name[[1]],n=1) } if(grepl("[^[:alnum:]]", name)) { - writeLines(desc_unauthorized_char_replaced) + writeLines(.dico[["desc_unauthorized_char_replaced"]]) gsub("[^[:alnum:]]", ".", name)->name } nameV<-replace_non_ascii(names(data1)) @@ -107,11 +107,11 @@ import <- data1<-data.frame(data1) if(any(nchar(names(data1))>30)) { - dlgMessage(ask_shorten_long_variables_names, "yesno")$res->rn + dlgMessage(.dico[["ask_shorten_long_variables_names"]], "yesno")$res->rn if(rn=="yes"){ which(nchar(names(data1))>30)->rn for(i in 1:length(rn)) { - rn2<- dlgInput(ask_name_to_attribute_to, colnames(data1)[rn[i]])$res + rn2<- dlgInput(.dico[["ask_name_to_attribute_to"]], colnames(data1)[rn[i]])$res if(length(rn2)!=0){ strsplit(rn2, ":")->rn2 tail(rn2[[1]],n=1)->colnames(data1)[rn[i]] @@ -122,12 +122,12 @@ import <- } if(any( grepl("[^[:alnum:][:space:]_.]", names(data1)))) { - writeLines(desc_avoid_spaces_and_punctuations) - dlgMessage(ask_rename_variables_with_special_char, "yesno")$res->rn + writeLines(.dico[["desc_avoid_spaces_and_punctuations"]]) + dlgMessage(.dico[["ask_rename_variables_with_special_char"]], "yesno")$res->rn if(rn=="yes"){ grep("[^[:alnum:][:space:]_.]", names(data1))->rn for(i in 1:length(rn)) { - rn2<- dlgInput(ask_name_to_attribute_to, colnames(data1)[rn[i]])$res + rn2<- dlgInput(.dico[["ask_name_to_attribute_to"]], colnames(data1)[rn[i]])$res strsplit(rn2, ":")->rn2 tail(rn2[[1]],n=1)->colnames(data1)[rn[i]] } @@ -135,15 +135,15 @@ import <- } if(any(is.na(data1))){ - writeLines(desc_number_of_missing_values) + writeLines(.dico[["desc_number_of_missing_values"]]) print(sapply(data1, function(x) sum(length(which(is.na(x))))) ) } assign(x=name, value=data1, envir=.GlobalEnv) - try(View(data1, txt_your_data), silent=T) + try(View(data1, .dico[["txt_your_data"]]), silent=T) str(data1) - Resultats <- desc_succesfully_imported + Resultats <- .dico[["desc_succesfully_imported"]] call.txt<-paste0("import(file='", file, "',dir='",getwd(),"',type='",type,"',dec='",dec, "',sep='",sep,"',na.strings='", na.strings,"',sheet=" , ifelse(is.null(sheet), "NULL",paste0("'", sheet,"'")),",name='",name,"')") diff --git a/R/import.results.R b/R/import.results.R index ee8e61d..0705614 100644 --- a/R/import.results.R +++ b/R/import.results.R @@ -9,6 +9,6 @@ function(){ } if(class(fichier)=='try-error') return(donnees()) openFileInOS(fichier) - Resultats<-paste(desc_result_succesfully_imported_in, fichier) + Resultats<-paste(.dico[["desc_result_succesfully_imported_in"]], fichier) return(Resultats) } diff --git a/R/interfaceR.R b/R/interfaceR.R index e8300df..ed46eb5 100644 --- a/R/interfaceR.R +++ b/R/interfaceR.R @@ -7,52 +7,52 @@ interfaceR <- - choix <- dlgList(c(txt_get_working_dir, - txt_specify_working_dir, - txt_remove_object_in_memory, - txt_list_of_objects_in_mem, - txt_search_for_new_function, - txt_packages_update, - txt_verify_packages_install, - txt_select_language - ), preselect=NULL, multiple = FALSE, title=ask_what_is_your_choice)$res + choix <- dlgList(c(.dico[["txt_get_working_dir"]], + .dico[["txt_specify_working_dir"]], + .dico[["txt_remove_object_in_memory"]], + .dico[["txt_list_of_objects_in_mem"]], + .dico[["txt_search_for_new_function"]], + .dico[["txt_packages_update"]], + .dico[["txt_verify_packages_install"]], + .dico[["txt_select_language"]] + ), preselect=NULL, multiple = FALSE, title=.dico[["ask_what_is_your_choice"]])$res while(length(choix)==0) return(easieR()) - if (choix==txt_get_working_dir) Resultats[[txt_working_dir]] <- getwd() - if (choix==txt_list_of_objects_in_mem) Resultats[[txt_objects_in_mem]] <- ls(envir=.GlobalEnv) - if (choix==txt_specify_working_dir) { - repertoire <- dlgDir(title=ask_chose_the_working_dir)$res + if (choix==.dico[["txt_get_working_dir"]]) Resultats[[.dico[["txt_working_dir"]]]] <- getwd() + if (choix==.dico[["txt_list_of_objects_in_mem"]]) Resultats[[.dico[["txt_objects_in_mem"]]]] <- ls(envir=.GlobalEnv) + if (choix==.dico[["txt_specify_working_dir"]]) { + repertoire <- dlgDir(title=.dico[["ask_chose_the_working_dir"]])$res if(length(repertoire)==0) repertoire <- getwd() setwd(repertoire) - Resultats[[txt_new_dir]] <- paste(desc_working_dir_is_now, repertoire) + Resultats[[.dico[["txt_new_dir"]]]] <- paste(.dico[["desc_working_dir_is_now"]], repertoire) } - if (choix==txt_remove_object_in_memory) { + if (choix==.dico[["txt_remove_object_in_memory"]]) { ls(envir=.GlobalEnv)->tout Filter( function(x) 'function' %in% class( get(x) ), ls(envir=.GlobalEnv) )->fonctions tout[!is.element(tout,fonctions)]->tout - X<-dlgList(tout, multiple = TRUE, title=txt_object_to_remove)$res + X<-dlgList(tout, multiple = TRUE, title=.dico[["txt_object_to_remove"]])$res if(length(X)==0) return(easieR()) rm(list=X, envir=.GlobalEnv) Resultats <- list() - Resultats[[desc_list_of_objects_still_in_mem]] <- ls(envir=.GlobalEnv) + Resultats[[.dico[["desc_list_of_objects_still_in_mem"]]]] <- ls(envir=.GlobalEnv) } - if (choix==txt_search_for_new_function) { + if (choix==.dico[["txt_search_for_new_function"]]) { require(sos) - writeLines(desc_to_find_new_analysis_search_in_english) - critere <- dlgInput(ask_which_analysis_you_looking_for, desc_search_here)$res + writeLines(.dico[["desc_to_find_new_analysis_search_in_english"]]) + critere <- dlgInput(.dico[["ask_which_analysis_you_looking_for"]], .dico[["desc_search_here"]])$res if(length(critere)==0) return(easieR()) critere <- strsplit(critere, ":") critere <- tail(critere[[1]],n=1) Resultats<- findFn(critere) return(Resultats) } - if (choix==txt_packages_update) {update.packages(ask=FALSE)} - if (choix==txt_verify_packages_install) vef.pack()->Resultats[[txt_packages_verification]] + if (choix==.dico[["txt_packages_update"]]) {update.packages(ask=FALSE)} + if (choix==.dico[["txt_verify_packages_install"]]) vef.pack()->Resultats[[.dico[["txt_packages_verification"]]]] - if (choix==txt_select_language) {select_language()} + if (choix==.dico[["txt_select_language"]]) {select_language()} - if (choix==txt_search_for_new_function) packages<-c(packages, 'sos') + if (choix==.dico[["txt_search_for_new_function"]]) packages<-c(packages, 'sos') Resultats$ref<- ref1(packages) return(Resultats) } diff --git a/R/lang.R b/R/lang.R index 2dd485b..875ac40 100644 --- a/R/lang.R +++ b/R/lang.R @@ -1,31 +1,64 @@ +#import_dict <- function(lang) { +# .dico <<- new.env(parent=emptyenv()) +# if (lang=='Français') { +# dictionnary <- file("./Dict_FR.txt","r") +# lines <- readLines(dictionnary) +# for (line in lines) { +# s <- strsplit(line, " ::::: ") +# if (length(s[[1]]) == 2) { assign(s[[1]][[1]], s[[1]][[2]], envir=.dico) } +# } +# } else if (lang=='English') { +# dictionnary <- file("./Dict_EN.txt","r") +# lines <- readLines(dictionnary) +# for (line in lines) { +# s <- strsplit(line, " ::::: ") +# if (length(s[[1]]) == 2) { assign(s[[1]][[1]], s[[1]][[2]], envir=.dico) } +# } +# } else { +# dictionnary <- file("./Dict_EN.txt","r") +# lines <- readLines(dictionnary) +# for (line in lines) { +# s <- strsplit(line, " ::::: ") +# if (length(s[[1]]) == 2) { assign(s[[1]][[1]], s[[1]][[2]], envir=.dico) } +# } +# } +#} + load_language <- function(lang='auto') { if (lang=='auto') { if(grepl('=fr_',Sys.getlocale()) | grepl('French',Sys.getlocale())) { + #import_dict("Français") load_fr_FR() print('[INFO] Version française chargĂ©e.') } else { + #import_dict("English") load_en_EN() print('[INFO] English language loaded (default).') } } else { if (lang=='Français') { load_fr_FR() + #import_dict("Français") print('Version française chargĂ©e.') } else if (lang=='English') { load_en_EN() + #import_dict("English") print('English language loaded (default).') } else { load_en_EN() - print('Not available. English language loaded (default).') + #import_dict("English") + print('Not available. English language loaded (default).') } } } select_language <- function() { - require(svDialogs) - lang <- dlgList(c('English', - 'Français' - ), preselect=NULL, multiple = FALSE, title=ask_what_is_your_choice)$res - if (length(lang)!=0) { load_language(lang=lang) } - return(easieR()) + require(svDialogs) + lang <- dlgList(c('English', + 'Français'), + preselect=NULL, + multiple = FALSE, + title=.dico[["ask_what_is_your_choice"]])$res + if (length(lang)!=0) {load_language(lang=lang) } + return(easieR()) } diff --git a/R/lang_en_EN.R b/R/lang_en_EN.R index 63df07d..3400a23 100644 --- a/R/lang_en_EN.R +++ b/R/lang_en_EN.R @@ -1,1133 +1,1132 @@ load_en_EN <- function() { - .dico <<- new.env(parent = emptyenv()) -assign("ask_2x2_table","Table 2x2?",envir=.dico) -assign("ask_2x2_table_value","Please specify the value for tables 2x2",envir=.dico) -assign("ask_add_a_value_to_empty_cells","Does an empty cell value for polychoric correlations need to be added? To specify the values, choose TRUE, otherwise choose [default]",envir=.dico) -assign("ask_add_value_to_total","do you still want to add a total value?",envir=.dico) -assign("ask_analysis_by_group","Group analysis?",envir=.dico) -assign("ask_analysis_on_complete_data_or_remove_outliers","Will you require analysis on the complete data or on the data for which the influential values have been removed?",envir=.dico) -assign("ask_analysis_type","What analysis do you want to make?",envir=.dico) -assign("ask_are_frequences_free_parameters","is the frequency of the different group a free parameter?",envir=.dico) -assign("ask_are_there_inversed_items","Is there any inverse items?",envir=.dico) -assign("ask_are_you_ready","are you ready?",envir=.dico) -assign("ask_baseline","What is the baseline?",envir=.dico) -assign("ask_bigger_tables_value","Please specify the value for tables larger than 2x2",envir=.dico) -assign("ask_bootstrap_number_min_500","Please specify the number of bootstrap. A minimum of 500 is ideally required. Can take time for N>1000",envir=.dico) -assign("ask_bootstrap_numbers_1_for_none","Please specify the number of bootstrap. To not have bootstrap, choose 1",envir=.dico) -assign("ask_bootstraps_number","Number of bootstraps?",envir=.dico) -assign("ask_cancel_entered_value_not_num","the value you entered is not numerical. Do you want to cancel this analysis?",envir=.dico) -assign("ask_cauchy_apriori_distribution","Please specify the prior distribution of Cauchy",envir=.dico) -assign("ask_center","Center?",envir=.dico) -assign("ask_center_numeric_variables","Do you want to focus the numerical variables? Centrer is generally advised (e.g., Schielzeth, 2010).",envir=.dico) -assign("ask_chi_squared_type","Please specify the type of chi tile you want to achieve.",envir=.dico) -assign("ask_choose_a_variable_with_at_least_two_modalities","A categorical variable must have at least 2 different modes. Please choose a variable with at least two modes",envir=.dico) -assign("ask_chose_analysis","Please choose the analysis you want to perform.",envir=.dico) -assign("ask_chose_categorial_ranking_factor","Please select the categorical classification factor.",envir=.dico) -assign("ask_chose_cols_corresponding_to_repeated_measures","Please choose all the columns corresponding to the modalites of the variables in repetees measures",envir=.dico) -assign("ask_chose_covariables","Please choose the covariates",envir=.dico) -assign("ask_chose_database","Please select the database",envir=.dico) -assign("ask_chose_defining_groups","Please select the group definition",envir=.dico) -assign("ask_chose_dependant_variable","Please select the dependent variable.",envir=.dico) -assign("ask_chose_first_judge","Please select the first judge",envir=.dico) -assign("ask_chose_independant_group_variables","Please choose the variables-s with independent groups",envir=.dico) -assign("ask_chose_interaction_model_predictors","Please choose the predictors to enter into the interaction model. It is necessary to have at least two variables",envir=.dico) -assign("ask_chose_manifest_variables_at_least_three","Please select the obvious variables you want to analyze. You must choose at least 3 variables",envir=.dico) -assign("ask_chose_ranking_categorial_factor","Please select the categorical classification factor.",envir=.dico) -assign("ask_chose_rotation","Please choose the type of rotation. Objection is adapted in the humanities",envir=.dico) -assign("ask_chose_sample_variables","Please select the variable(s) defining the workforce",envir=.dico) -assign("ask_chose_second_judge","Look for the second judge",envir=.dico) -assign("ask_chose_selection_method","Please choose the selection method you wish to use",envir=.dico) -assign("ask_chose_the_working_dir","Please select work directory",envir=.dico) -assign("ask_chose_variables_at_least_five","Please select the variables you want to analyze. You must choose at least 5 variables",envir=.dico) -assign("ask_chose_variables_at_least_three","Please select the variables you want to analyze. You must choose at least 3 variables",envir=.dico) -assign("ask_chose_variable","Please choose the variables you want to analyze.",envir=.dico) -assign("ask_chose_variable_x_axis","Please select the abscess variable",envir=.dico) -assign("ask_chose_variable_y_axis","Please select the variable ordered",envir=.dico) -assign("ask_coding_criterion","What coding criteria do you want?",envir=.dico) -assign("ask_col_separation_index","When saving your file, what is the column separation index?",envir=.dico) -assign("ask_complete_or_outliers","Do you want to perform analyses on complete data or on data without influential values?",envir=.dico) -assign("ask_constant_parameters","Consistent parameters?",envir=.dico) -assign("ask_continue","Continue?",envir=.dico) -assign("ask_contrast_must_respect_ortho","Contrasts must respect orthogonalite. Do you want to continue?",envir=.dico) -assign("ask_control_variables","Please specify the variable(s) to control",envir=.dico) -assign("ask_convert_dependant_variable_to_dichotomic","do you want to convert the dependent variable into a dichotomous variable?",envir=.dico) -assign("ask_correction_desired","Please specify the type of probability correction you want to achieve",envir=.dico) -assign("ask_correction_type","Type of correction?",envir=.dico) -assign("ask_correlated_or_orthogonal_factors","Is the factors correlated (FALSE) or are they orthogonal (TRUE)?",envir=.dico) -assign("ask_correlation_matrix_could_not_be_computed","The correlation matrix could not be achieved. Do you want to try again?",envir=.dico) -assign("ask_correlation_type","Please choose the type of correlations you want to make. For dichotomous variables, correlations will be tetrachoric correlations",envir=.dico) -assign("ask_corr_or_partial_correlations","Partial corrections or correlations?",envir=.dico) -assign("ask_could_not_converge_model_verify_correlation_matrix","We did not succeed in making the model converge. Please check your correlation matrix and try again with other parameters",envir=.dico) -assign("ask_could_not_finish_analysis_respecify_parameters","We were unable to complete the analysis correctly. Please try to respecify the parameters",envir=.dico) -assign("ask_covariables","Covariable-s?",envir=.dico) -assign("ask_criterion_for_dichotomy","Please specify the criteria on which you want to dichotomize your variable. You can use the mediane or choose a specific threshold.",envir=.dico) -assign("ask_criterion_for_obs_to_keep","Please specify the criteria for any comments you wish to keep/guard.",envir=.dico) -assign("ask_criterion_for_variable","What criteria do you want to use for the variable",envir=.dico) -assign("ask_data","DonnĂ©es?",envir=.dico) -assign("ask_data_format","What is the format of your data?",envir=.dico) -assign("ask_decimal_symbol","If some data contain decimals, what is the symbol indicating the decimal?",envir=.dico) -assign("ask_denominator_variable_or_value","Is the denominator a variable or a value?",envir=.dico) -assign("ask_denominator_variable","Please select the variable to the denominator",envir=.dico) -assign("ask_dependant_variable_with_less_than_three_val_verify_dataset","The dependent variable has less than three different values. Check your data or the analysis you are trying to do is not relevant.",envir=.dico) -assign("ask_did_not_specify_nb_factors_repeated_measure_exit","You haven't specified the number of factors you can repetee, do you want to quit?",envir=.dico) -assign("ask_distribution","Distribution?",envir=.dico) -assign("ask_distribution_type","What distribution do you want?",envir=.dico) -assign("ask_empty_cells","Old Cells?",envir=.dico) -assign("ask_enter_different_values","Please enter different values",envir=.dico) -assign("ask_enter_number_of_to_be_removed_variable","You must enter the number to know which observation should be deleted.",envir=.dico) -assign("ask_exit_because_of_alpha_on_non_matrix","You are trying to do an alpha on something other than a matrix. Do you want to leave this analysis?",envir=.dico) -assign("ask_exit_no_lower_bound_specified","You have not specified the lower limit. Do you want to leave the selection?",envir=.dico) -assign("ask_exit_no_upper_bound_specified","You have not specified the upper limit. Do you want to leave the selection?",envir=.dico) -assign("ask_exportation_filename","What name do you want to assign to the file?",envir=.dico) -assign("ask_factorial_scores","factorial scores?",envir=.dico) -assign("ask_factors_number_for_hierarchical_structure","Please specify the number of factors in the hierarchical structure.",envir=.dico) -assign("ask_factors_ortho","Orthogonalitis of factors?",envir=.dico) -assign("ask_factors_superior_level","Number of factors of the higher level?",envir=.dico) -assign("ask_family","Please specify the family (i.e. form of distribution).",envir=.dico) -assign("ask_file_format","File format?",envir=.dico) -assign("ask_file_format_to_import","What format is your file saved?",envir=.dico) -assign("ask_first_categorical_set","Please select the first categorical factor(s) set",envir=.dico) -assign("ask_first_variables_set","Please select the first set of variables",envir=.dico) -assign("ask_fixed_covariables","fixed covariates?",envir=.dico) -assign("ask_freq_constance","Constance of Frequence?",envir=.dico) -assign("ask_f_value","What value do you want to use?",envir=.dico) -assign("ask_group_variable","Variable [groups]?",envir=.dico) -assign("ask_headers_in_database","Is the name of the variables on the first line of your database? Choose TRUE if so",envir=.dico) -assign("ask_hierarchical_analysis","Does it have to make a hierarchical analysis?",envir=.dico) -assign("ask_how_many_modalities","How many modes",envir=.dico) -assign("ask_how_standard_error_must_be_estimated","How should the standard error be estimated?",envir=.dico) -assign("ask_how_to_remove","How do you want to delete them?",envir=.dico) -assign("ask_how_to_treat_missing_values","Missing values were detected. How do you want to treat them? Keeping all observations can bias the results.",envir=.dico) -assign("ask_id_variable","Please select the variable identifying participants",envir=.dico) -assign("ask_imitate","Imitate?",envir=.dico) -assign("ask_independant_variable","Please select the independent variable.",envir=.dico) -assign("ask_information_matrix","Information Matrix?",envir=.dico) -assign("ask_integrate_factorial_scores_in_data","Do you want factorial scores to be integrated with your data?",envir=.dico) -assign("ask_inversed_items","inverse items?",envir=.dico) -assign("ask_is_model_correct","Is your model correct?",envir=.dico) -assign("ask_latent_variables_number","Please specify the number of latent variables",envir=.dico) -assign("ask_level","Please select level",envir=.dico) -assign("ask_likelihood","Treasure?",envir=.dico) -assign("ask_linebase_modalities","Please specify the mode(s) that will be used for the base line (e.g. 0). The other modes will be grouped in category 1.",envir=.dico) -assign("ask_log_base","Please specify the base of the logarithm.To get e, type e",envir=.dico) -assign("ask_lower_bound","Inferior limit?",envir=.dico) -assign("ask_mcnemar_repeated_measure","McNemar test: modalites are not the same for the McNemar test. Is this a factor that is able to repeat?",envir=.dico) -assign("ask_mediation_type","What kind of mediation?",envir=.dico) -assign("ask_mediator","please choose the mediator",envir=.dico) -assign("ask_minus_left_hand_variables","Please select the variable(s) on the left of the symbol *minus*",envir=.dico) -assign("ask_minus_right_hand_variables","Please select the variable(s) on the right of the symbol *minus*.",envir=.dico) -assign("ask_minus_right_operand_variable_or_value","Are the values on the right of the symbol *minus* a variable(s) or a value?",envir=.dico) -assign("ask_missing_values_detected_what_to_do","Missing values have been detected. How do you want to treat them?",envir=.dico) -assign("ask_missing_values_treatment","Treatment of missing values?",envir=.dico) -assign("ask_missing_values_value_na_on_empty","If some data is missing, how are they defined? You can leave NA if the cells are empty.",envir=.dico) -assign("ask_missing_value_treatment","Number of missing values per variable. How do you want to treat them?",envir=.dico) -assign("ask_modalities_for_variable","What modes do you want to select for the variable",envir=.dico) -assign("ask_modalities_to_keep","Please select the modes you want to keep.",envir=.dico) -assign("ask_name_for_dataset","What name do you want to give the data?",envir=.dico) -assign("ask_name_to_attribute_to","What name do you want to assign to",envir=.dico) -assign("ask_nb_factors_repeated_measure","How many factors can be rehearsed?",envir=.dico) -assign("ask_new_variable_name","What name do you want to assign to the new variable?",envir=.dico) -assign("ask_norm_value","What is the standard value?",envir=.dico) -assign("ask_not_enough_obs_verify_dataset","There are not enough observations to make the analysis. Please check your net data to ensure that there are at least three observations per mode of each factor",envir=.dico) -assign("ask_null_hypothesis_tests_or_bayesian_factors","Do you want null hypothesis tests or/and Bayesian factors?",envir=.dico) -assign("ask_numerator_variable_or_value","Is the numerator a variable or a value?",envir=.dico) -assign("ask_numerator_variable","Please select the variable at the numerator",envir=.dico) -assign("ask_obs_to_remove","What observation do you want to remove from analyses? 0 = none",envir=.dico) -assign("ask_other_options","Other options?",envir=.dico) -assign("ask_ponderate_analysis_by_a_sample_var","Does the analysis need to be weighted by an effective variable?",envir=.dico) -assign("ask_positive_val_variable_or_value","Are the positive values a variable(s) or a value?",envir=.dico) -assign("ask_predictor","please specify the predictor",envir=.dico) -assign("ask_press_enter_to_continue","Support [enter] to continue",envir=.dico) -assign("ask_probabilities_for_modalities","Please enter the probabilities corresponding to each mode of the variable.",envir=.dico) -assign("ask_probabilities","Probability?",envir=.dico) -assign("ask_probability_value","What value of probability do you want to use?",envir=.dico) -assign("ask_redefine_analysis_because_modalities_product_is_superior_to_obs","The product of the modalites of the variables defining the groups is superior to your observations. You need at least one observation by combination of modes of your variables. Please redefine your analysis",envir=.dico) -assign("ask_regroup_modalities","Do you want to group between the modes?",envir=.dico) -assign("ask_rename_variables_with_special_char","Some variable names contain special characters that can create bugs. Do you want to rename these variables?",envir=.dico) -assign("ask_results_desired","What results do you want?",envir=.dico) -assign("ask_results_output","Outcomes?",envir=.dico) -assign("ask_sampling_type","What type of sampling have you done for your analysis?",envir=.dico) -assign("ask_save_results_in_external_file","Do you want to save results to an external file?",envir=.dico) -assign("ask_second_categorical_set","Please select the second categorical factor(s) set",envir=.dico) -assign("ask_second_mediator","Please specify the second mediator.",envir=.dico) -assign("ask_second_variables_set","Please select the second set of variables",envir=.dico) -assign("ask_selection_method","What method should be used for the selection method?",envir=.dico) -assign("ask_select_variables_or_modalities_of_repeated_measure_variable","Please select the variables OR modalites of the variables a measure(s).",envir=.dico) -assign("ask_separation_value","Please specify the separation value",envir=.dico) -assign("ask_shorten_long_variables_names","Some variables have particularly long names that can generate playback. Do you want to shorten them?",envir=.dico) -assign("ask_should_intercept_of_latent_variable_be_fixed_to_zero","Does the intercept of latent variables have to be fixed to 0?",envir=.dico) -assign("ask_should_intercept_of_obs_variables_be_fixed_to_zero","Does the intercept of observed variables have to be fixed to 0?",envir=.dico) -assign("ask_simple_or_partial_corr","Single or partial corrections?",envir=.dico) -assign("ask_specify_all_parameters_or_imitate_specific_software","Do you want to specify all the parameters [default] or imitate any particular software?",envir=.dico) -assign("ask_specify_datasheet_to_import","Please specify the worksheet you want to import",envir=.dico) -assign("ask_specify_groups","Specify groups?",envir=.dico) -assign("ask_specify_inverted_item","Please specify the inverse items",envir=.dico) -assign("ask_specify_likelihood","Please specify the likelihood.",envir=.dico) -assign("ask_specify_norm_value","Please specify the value of the standard",envir=.dico) -assign("ask_specify_other_options","Specify other options?",envir=.dico) -assign("ask_specify_sample","Specify actual?",envir=.dico) -assign("ask_specify_sample_variable","Specify the true number?",envir=.dico) -assign("ask_specify_variables_for_ranks","Please specify the variables you wish to make the rows of",envir=.dico) -assign("ask_specify_variables_type","Please specify the type(s) of variable(s) you wish to include in the analysis.nYou can choose several (e.g., for mixed anova or ancova)",envir=.dico) -assign("ask_standard_error","Standard Error?",envir=.dico) -assign("ask_standardization","Standardization?",envir=.dico) -assign("ask_standardization_vl","Standardization VL?",envir=.dico) -assign("ask_standardize_obs_variables_before","Does it standardize (i.e. centrer reduce) the variables observed at prelable (TRUE) or not (FALSE)?",envir=.dico) -assign("ask_statistical_approach","Statistical approach?",envir=.dico) -assign("ask_subgroups","You can compose descriptive statistics by sub-group by choosing one or more categorical variables. Do you want to specify the subgroups?",envir=.dico) -assign("ask_sufficient_matrix_for_afe","Is the matrix satisfactory for an EFA?",envir=.dico) -assign("ask_suppress_this_obs","Do you want to delete this observation?",envir=.dico) -assign("ask_test_hierarchical_structure"," Do you want to test a hierarchical structure? The omega tests a hierarchical structure and a hierarchical AFE will be realized.",envir=.dico) -assign("ask_time1","Please choose time 1.",envir=.dico) -assign("ask_time2","Please choose time 2.",envir=.dico) -assign("ask_transform_numerical_to_categorial_variables","You must use categorical variables. Do you want to turn numerical variables into categorical variables?",envir=.dico) -assign("ask_troncature_threshold","Please set the threshold of the trunk",envir=.dico) -assign("ask_t_test_type","Please specify the type of test t you want to perform.",envir=.dico) -assign("ask_type_correlation","Please specify the type of correlation you want to achieve.",envir=.dico) -assign("ask_upper_bound","High light?",envir=.dico) -assign("ask_value_for_missing_values","By what value are the missing values defined?",envir=.dico) -assign("ask_value_for_operation","Please specify the value for your mathematical operation.",envir=.dico) -assign("ask_value_for_selected_obs","Please specify the value on which observations should be selected.",envir=.dico) -assign("ask_value","Get value?",envir=.dico) -assign("ask_variabels_for_polyc_tetra_mixt_corr","Please select the variables for which polychoric/tetrachoric/mixte correlations should be made.",envir=.dico) -assign("ask_variable_at_this_point","What variable has this etape",envir=.dico) -assign("ask_variable_name","Name of new variable?",envir=.dico) -assign("ask_variables_for_description_statistics","Please choose the variables for which you wish to obtain descriptive statistics",envir=.dico) -assign("ask_variables_groups","Variable groups?",envir=.dico) -assign("ask_variables_names","Name of variables?",envir=.dico) -assign("ask_variables_to_abs","Please select the variables to make the absolute value",envir=.dico) -assign("ask_variables_to_add","Please select the variables to add.",envir=.dico) -assign("ask_variables_to_exp","Please select the variables to which the exponent applies",envir=.dico) -assign("ask_variables_to_log","Please select the variables for which logarithm is required",envir=.dico) -assign("ask_variables_to_mean","Please select the variables to average",envir=.dico) -assign("ask_variables_to_multiply","Please select the variables to multiply.",envir=.dico) -assign("ask_variables_to_order","Please select the variable(s) to sort",envir=.dico) -assign("ask_variables_type_correlations","Please specify the type of variables. Tetra/polychoric correlations will be made on dichotomous/ordinal variables and Bravais-Pearson on continuous variables",envir=.dico) -assign("ask_variables_types_correlations","Please specify the type of variables. Tetra/polychoric correlations will be made on the ordinal and Bravai-Pearson variables on the continuous",envir=.dico) -assign("ask_variables_used_for_exponential","Please select the variables used in the exhibition",envir=.dico) -assign("ask_variables_used_for_groups","Please select the variable(s) defining groups",envir=.dico) -assign("ask_variable","Variable to analyze?",envir=.dico) -assign("ask_wanted_model","Please choose the model you want to analyze with aov.plus",envir=.dico) -assign("ask_what_do_you_want","What do you want?",envir=.dico) -assign("ask_what_is_your_choice","What is your choice?",envir=.dico) -assign("ask_what_to_print","What do you want to show?",envir=.dico) -assign("ask_which_algorithm","What algorithm will you want?",envir=.dico) -assign("ask_which_analysis_you_looking_for","What analysis are you looking for?",envir=.dico) -assign("ask_which_baseline","What is the baseline?",envir=.dico) -assign("ask_which_constant_parameters","What parameters do you want to maintain constant?",envir=.dico) -assign("ask_which_contrasts_for_variable","What contrasts for the variable",envir=.dico) -assign("ask_which_contrasts","What kind of contrast do you want?",envir=.dico) -assign("ask_which_correction","What correction of probability do you want to apply? To not apply a correction, choose +none+",envir=.dico) -assign("ask_which_data_to_analyse","What data do you want to analyze?",envir=.dico) -assign("ask_which_data_to_export","What data do you want to export?",envir=.dico) -assign("ask_which_estimator","What estimator?",envir=.dico) -assign("ask_which_factors_combination_for_adjust_means","What combination of factors do you want to show the adjusted averages?",envir=.dico) -assign("ask_which_information_matrix_for_standard_error_estimation","On which information matrix should the estimation of standard errors be achieved?",envir=.dico) -assign("ask_which_mathematical_operation","Please choose the mathematical operation you wish to achieve",envir=.dico) -assign("ask_which_operation","What operation do you want?",envir=.dico) -assign("ask_which_options","What options?",envir=.dico) -assign("ask_which_options_to_specify","What options do you want to specify?",envir=.dico) -assign("ask_which_output","What format do you want?",envir=.dico) -assign("ask_which_output_results","What results do you want?",envir=.dico) -assign("ask_which_regression_type","What type of regression?",envir=.dico) -assign("ask_which_results_warning_on_default_output","What results do you want? Warning: exits by default cannot be saved. If you want a saver, choose the detail",envir=.dico) -assign("ask_which_rotation","What rotation",envir=.dico) -assign("ask_which_saturation_criterion","What is the saturation criteria you want to use?",envir=.dico) -assign("ask_which_size_effect","What effect size do you want?",envir=.dico) -assign("ask_which_squared_sum","What sum of squares do you want to use?",envir=.dico) -assign("ask_which_test","What test do you want to use?",envir=.dico) -assign("ask_which_value_for_operation","What value do you want for your mathematical operation?",envir=.dico) -assign("ask_which_variable_identifies_participants","What is the variable identifying participants?",envir=.dico) -assign("ask_you_did_not_chose_a_variable_continue_or_abort","You have not chosen a variable. Do you want to continue (ok) or give up (cancel) this analysis?",envir=.dico) -assign("desc_abs_val_applied_to_var","the absolute value has been applied to the variable",envir=.dico) -assign("desc_accepted_values_are_z_and_grubbs","The values accepted for criteria are z and Grubbs",envir=.dico) -assign("desc_all_tests_description","The parametric model returns the classic anova, the nonparametric calculates the Kruskal Wallis test nsi it is a model with independent groups, or a Friedman anova for a model in Measurements repetees.nThe Bayesian model is the equivalent of the model test in the anova by adopting a Bayesian approach,n the robust statistics are anovas on medianes or the truncated averages with or without bootstrap.",envir=.dico) -assign("desc_alpha_increased_with_value_equals_to","you multiply the error of 1e espece. The risk of making an error of 1st species is",envir=.dico) -assign("desc_analysis_aborted","The analysis could not be completed",envir=.dico) -assign("desc_and","and",envir=.dico) -assign("desc_and_variabe","and variable",envir=.dico) -assign("desc_and_variable_y"," and variable",envir=.dico) -assign("desc_applied_correction_is","the correction applied is the correction of",envir=.dico) -assign("desc_at_least_10_obs_needed","It takes at least 10 observations plus the number of variables to make the analysis. Check your data.",envir=.dico) -assign("desc_at_least_independant_variables_or_repeated_measures","It is essential to have at least variables with independent groups or in repete measures",envir=.dico) -assign("desc_at_least_on_contrast_matrix_incorrect","At least one of your contrast matrices is not correct.",envir=.dico) -assign("desc_at_least_one_denom_is_zero","At least one of the values in the denominator is a 0. The value returned in this case is infinite - inf",envir=.dico) -assign("desc_at_least_one_non_numeric","at least one variable is not digital",envir=.dico) -assign("desc_at_least_one_var_is_not_num","at least one of the variables is not numerical",envir=.dico) -assign("desc_authorized_values_for_contrasts","The permitted values for contrasts are +none+ for no contrast, +pairwise+ for comparisons 2 to 2 or a list of contrast coefficients",envir=.dico) -assign("desc_avoid_spaces_and_punctuations","Avoid spaces and punctuation signs, except . and _",envir=.dico) -assign("desc_bayesian_factors_could_not_be_computed","Bayesian factors could not be calculated.",envir=.dico) -assign("desc_beyond_with_lower_and_upper","au-dela (with a lower and higher limit)",envir=.dico) -assign("desc_biased_results_risk_because_of_low_number_of_obs_or_zero_variance","there are less than 3 observations for one of the groups or nthe variance of at least one group is 0. Results are likely to be significantly biased",envir=.dico) -assign("desc_bootstraps_number_must_be_positive","The number of bootstrap must be a positive integer",envir=.dico) -assign("desc_bootstrap_t_adapt_to_truncated_mean","The bootstrap-t method is a bootstrap adapted to the calculation of the truncate mean",envir=.dico) -assign("desc_cannot_compute_mahalanobis","Desole, we cannot calculate the distance of Mahalanobis on your data. The analyses will be carried out on the complete data",envir=.dico) -assign("desc_cannot_group_variables_because_not_described","You cannot have a variable *groups* since all variables must be descripted",envir=.dico) -assign("desc_cannot_have_both_within_RML_arguments","You cannot have both arguments in within and RML",envir=.dico) -assign("desc_cells_for_mcnemar","The cells used to calculate the McNemar are those of the 1st row 2nd column and the 2nd row 1st column",envir=.dico) -assign("desc_centered_data_schielzeth_recommandations","In accordance with the recommendations of Schielzeth 2010, the data were pre-centered",envir=.dico) -assign("desc_chi_squared_adjustment_on_variable_x","chi two adjustment on variable",envir=.dico) -assign("desc_close_browser_to_come_back","Do not forget to close the htmlt window (firexfox, chrome, internet explorer...) to return to the R session",envir=.dico) -assign("desc_cross_validation_is_not_yet_supported","Cross validation is not yet available.",envir=.dico) -assign("desc_data_saved_in","data are saved in",envir=.dico) -assign("desc_data_succesfully_ordered","data have been sorted correctly",envir=.dico) -assign("desc_descriptive_statistics_on","Descriptive statistics on",envir=.dico) -assign("desc_distribution_is_hypergeometric_when","The option *Total fixed effect for rows and columns* when the totals for rows and columns are fixed. The distribution is hypergeometric",envir=.dico) -assign("desc_each_participant_must_appear_only_once_","Each participant must appear once and only once for each combination of the modes",envir=.dico) -assign("desc_effect_size_by_walker","The effect size is calculated from the formula proposed by Walker, 2003",envir=.dico) -assign("desc_entered_value_not_num","value entered is not numerical",envir=.dico) -assign("desc_exponential_has_been_applied_to_var","exponential has been applied to the variable",envir=.dico) -assign("desc_facotrs_must_be_positive_int_inferior_to_variables_num","The number of factors must be a positive integer less than the number of variables",envir=.dico) -assign("desc_fb_ratio_between_models","FB ratio between models",envir=.dico) -assign("desc_file_is_saved_in","file is saved in",envir=.dico) -assign("desc_flattening_and_asymetry_configurable","You can specify truncation and parameters for flattening and asymetry by choosing other options",envir=.dico) -assign("desc_for_bigger_samples_bootstrap_t_prefered","For larger samples, boostrap using method t should be preferred.",envir=.dico) -assign("desc_for_easier_to_work","In order for easieR to work properly, Pandoc must be installed at the following URL: https://github.com/jgm/pandoc/releases",envir=.dico) -assign("desc_graph_thickness_gives_density","The thickness of the graph gives the density, allowing to better define the distribution.",envir=.dico) -assign("desc_has_been_added_to","was added to",envir=.dico) -assign("desc_has_been_added_to_variable","is added to variable",envir=.dico) -assign("desc_has_been_applied_to_variable"," has been applied to the variable",envir=.dico) -assign("desc_has_been_put_to_the_power_of"," has been elevated to power",envir=.dico) -assign("desc_has_multiplied_variables","a multiplies the -les-variables",envir=.dico) -assign("desc_highest_value","Highest Value",envir=.dico) -assign("desc_how_to_cite_easier","To cite easieR in your publication / to quote easieR in you publications use:n Stefaniak, N. (2020).",envir=.dico) -assign("desc_identical_option_total_sample","The total fixed staffing option for columns* is identical to the previous one for columns",envir=.dico) -assign("desc_identified_outliers","Observations considered influential",envir=.dico) -assign("desc_if_true_covariates_as_fixed","If true, exogenous covaria are considered fixed, otherwise they are considered to be aleatory and their parameters are free",envir=.dico) -assign("desc_if_true_latent_residuals_one","If true, the residuals of latent variables are fixed to 1, otherwise the parameters of the latent variable are estimated by setting the first indicator to 1",envir=.dico) -assign("desc_improve_likelihood_for_each_variable","Improving likelihood for each variable",envir=.dico) -assign("desc_incorrect_model","The specified model is incorrect. Check your variables and model",envir=.dico) -assign("desc_instable_model_high_multicolinearity","Multicolinearite is too important. The model is unstable",envir=.dico) -assign("desc_insufficient_obs","The number of observations is insufficient to complete the analyses for this group",envir=.dico) -assign("desc_insufficient_sample_for_combinations_between","The number of combinations between the variable is insufficient",envir=.dico) -assign("desc_in_that_case_non_parametric_is_classical_chi_squared","In this case, the nonparametric test is the classic chi square test",envir=.dico) -assign("desc_issue_in_hierarchical_regression","A problem has been identified in the stages of your hierarchical regression",envir=.dico) -assign("desc_kmo_could_not_be_computed_verify_matrix","The KMO could not be calculated. Check your correlation matrix.",envir=.dico) -assign("desc_kmo_must_strictly_be_more_than_a_half","the KMO must be absolutely superior to 0.5",envir=.dico) -assign("desc_kmo_on_matrix_could_not_be_obtained","The KMO on the matrix could not be obtained.",envir=.dico) -assign("desc_kmo_on_matrix_could_not_be_obtained_trying","The KMO on the matrix could not be obtained. We try to achieve a smoothing of the correlation matrix",envir=.dico) -assign("desc_large_format_must_be_numeric_or_integer","If your data is in large format, all measurements must be numerical or integer",envir=.dico) -assign("desc_list_of_objects_still_in_mem","List of objects still in memory of R",envir=.dico) -assign("desc_log_with_base","the basic logarithm",envir=.dico) -assign("desc_manifest_variables_of","Manifest Variables of",envir=.dico) -assign("desc_manual_contrast_need_coeff_matrice","If you enter contrasts manually, all variables in the analysis must have their coefficient matrix",envir=.dico) -assign("desc_matrix_is_singular_mardia_cannot_be_performed","The matrix is singular and the Marida test cannot be performed. Only univariate analyses can be carried out",envir=.dico) -assign("desc_mcnemar_need_2x2_table_yours_are_different","The McNemar test involves a 2x2 array. The dimensions of your table are different.",envir=.dico) -assign("desc_modalities_product_must_correspond_to_cols_selected","the output of the modes of each variable must correspond to the number of columns selected.",envir=.dico) -assign("desc_model_contains_error","The model cannot be evaluated. It must contain an error.",envir=.dico) -assign("desc_model_could_not_converge","The model could not converge. The parameters have been adapted to allow the model to converge",envir=.dico) -assign("desc_model_seems_incorrect_could_not_be_created","The model seems incorrect and could not be created.",envir=.dico) -assign("desc_most_common_effect_size","the most frequent effect size is the partial square - pes.nThe most precise effect size is the generalized square - ges",envir=.dico) -assign("desc_multicolinearity_risk","multicolinearite risk if matrix determinant is less than 0.00001",envir=.dico) -assign("desc_multiple_ways_to_compute_squares_sum","There are several ways to calculate the sum of squares. The default choice of commercial software is a sum of type 3 squares, prioritizing interactions rather than main effects.",envir=.dico) -assign("desc_must_be_dichotomic","modalites. It is incompatible with a logistic regression. It must be dichotomous.",envir=.dico) -assign("desc_nb_factors_must_be_positive_integer","The number of factors must be a positive integer less than the number of factors",envir=.dico) -assign("desc_need_at_least_three_observation_by_combination","Some combinations of modes have less than 3 observations. You must have at least 3 observations for each combination",envir=.dico) -assign("desc_neg_log_impossible","it is not possible to calculate logarithms for a base is negative. NA is fired",envir=.dico) -assign("desc_no_analysis_can_be_performed_given_your_data","The variables you selected to perform your analysis do not allow any analysis to be made. Please redefine your analysis",envir=.dico) -assign("desc_no_data_in_R_memory","there are no data in the memory of R, please import the data on which to perform the analysis",envir=.dico) -assign("desc_non_equal_independant_variable_modalities_occurrence","The number of occurrences for each modeite of your independent variable is not the same. Please select a participating identifier",envir=.dico) -assign("desc_non_numeric_value","The input value is not numerical, you must enter a numeric value",envir=.dico) -assign("desc_non_numeric_variable","the variable is not digital",envir=.dico) -assign("desc_non_param_are_rho_and_tau","The nonparametric test corresponds to the Spearman rho and the Kendall tau",envir=.dico) -assign("desc_non_param_is_wilcoxon_or_mann_withney","The non-parametric test is the Wilcoxon test (or Mann-Whitney)",envir=.dico) -assign("desc_no_obs_for_combination","no observations for combination",envir=.dico) -assign("desc_no_result_saved","no result has been saved",envir=.dico) -assign("desc_norm_must_be_numeric","The standard must be a numeric value.",envir=.dico) -assign("desc_no_saved_analysis_found","No backup analysis could be found",envir=.dico) -assign("desc_number_of_judge_is","the number of judges",envir=.dico) -assign("desc_number_of_missing_values","Number of missing values per variable",envir=.dico) -assign("desc_number_of_observations_is","number of observations",envir=.dico) -assign("desc_number_outliers_removed","Number of observations withdrawn",envir=.dico) -assign("desc_obs_with_asterisk_are_outliers","The observations marked with an asterisk are considered to be influential at least on a criteria",envir=.dico) -assign("desc_odd_ratio_cannot_be_computed","Or cannot be calculated for tables larger than 2x3 or tables containing 0",envir=.dico) -assign("desc_only_one_dependant_variable_alllowed","There can be only one dependent variable.",envir=.dico) -assign("desc_only_one_file_format_at_time_EPS_JPG","Only one file format for saving figure may be used at a time (you have both EPS and JPG specified).",envir=.dico) -assign("desc_only_one_file_format_at_time_EPS_PDF","Only one file format for saving figure may be used at a time (you have both PDF and EPS specified).",envir=.dico) -assign("desc_only_one_file_format_at_time_PDF_JPG","Only one file format for saving figure may be used at a time (you have both PDF and JPG specified).",envir=.dico) -assign("desc_only_values_above_diagonal_are_adjusted_for_multiple_comp","Only values above the diagonal are adjusted for multiple comparisons",envir=.dico) -assign("desc_operation_succesful","Mathematic operation has gone smoothly.",envir=.dico) -assign("desc_order","sort",envir=.dico) -assign("desc_outliers_identified_on_4_div_n","Influential values are identified based on 4/n",envir=.dico) -assign("desc_outliers_identified_on_mahalanobis","Influential values are identified based on the distance of Mahalanobis with a chi threshold at 0.001",envir=.dico) -assign("desc_outliers_on_4_div_n","Influential values are identified on the basis of 4/n",envir=.dico) -assign("desc_packages_used_for_this_function","Packages used for this function",envir=.dico) -assign("desc_param_is_BP","The parametric test is the Bravais-Pearson correlation",envir=.dico) -assign("desc_param_is_t_test","The parametric test is the classic t test",envir=.dico) -assign("desc_param_test_is_classical_reg_robusts_are_m_estimator","The parametric test is the classic regression and robust tests are an estimate on an M estimater as well as a bootstrap.",envir=.dico) -assign("desc_percentile_bootstrap_prefered_for_small_samples","the percentile boottrap method must be preferred for small samples",envir=.dico) -assign("desc_perfectly_correlated_variables_in_matrix_trying_to_solve","you try to make a matrix of correlations with perfectly correlated variables. This is a concern for the calculation of Mahalanobis' distance. We are trying to solve the problem.",envir=.dico) -assign("desc_polyc_correlations_failed_rho_used_instead","Polychoric correlations have failed. The correlations used are Spearman rho",envir=.dico) -assign("desc_proba_and_IC_estimated_on_bootstrap","Probabilities and ICs are estimated on the basis of a bootrap. The IC is corrected for multiple comparison, unlike the probability reported.",envir=.dico) -assign("desc_probabilities_vector_please_no_fraction","Vector of probabilities. Note: do not enter fractions",envir=.dico) -assign("desc_red_dot_is_mean_error_is_sd","The red dot is the average. The error bar is the scale-type",envir=.dico) -assign("desc_references","References for packages used for this analysis",envir=.dico) -assign("desc_removed_variable","deleted variable",envir=.dico) -assign("desc_removing_outliers_weakens_sample_size","Removal of influential values results in too small a number of modalites to complete the analysis",envir=.dico) -assign("desc_result_succesfully_imported_in","Results were correctly imported into",envir=.dico) -assign("desc_robusts_statistics_could_not_be_computed","The robust statistics could not be achieved",envir=.dico) -assign("desc_robust_statistics_are_alternative_to_the_principal_but_slower","The robust statistics are alternative to the main analysis, usually involving bootstraps. These analyses are often slower",envir=.dico) -assign("desc_saturation_criterion_must_be_between_zero_and_one","The saturation criterion must be between 0 and 1.",envir=.dico) -assign("desc_search_here","Type your search here",envir=.dico) -assign("desc_selected_obs_are_in","the observations you have selected are in",envir=.dico) -assign("desc_selection_for_bayesian_factor_does_not_apply_to_complex_models","The selection methods for Bayesian factors do not apply for complex models.",envir=.dico) -assign("desc_should_specify_nb_factors_repeated_measure","you need to specify the number of factors you can repetee",envir=.dico) -assign("desc_single_dependant_variable_allowed_in_paired_t","There can be only one dependent variable for tstudents for matched samples",envir=.dico) -assign("desc_singular_matrix_mahalanobis_on_max_info","Your matrix is singular, which is a concern. We are trying to solve the problem. If possible, the distance from Mahalanobis will then be calculated on the maximum information while avoiding the singularite.",envir=.dico) -assign("desc_some_values_are_not_numeric","Not all entered values are numeric. Please enter numeric values only",envir=.dico) -assign("desc_special_characters_have_been_removed","Special accents/characters have been deliberately removed to ensure the portability of easieR on all computers.",envir=.dico) -assign("desc_specify_f_value","You must specify the value of the F. This value must be greater than 1",envir=.dico) -assign("desc_specify_lower_bound","you must specify the lower limit",envir=.dico) -assign("desc_specify_probability_value","You must specify the value of probability. This value shall be between 0 and 1",envir=.dico) -assign("desc_specify_upper_bound","you must specify the upper limit",envir=.dico) -assign("desc_standardized_saturation_on_correlation_matrix","standardized saturations based on the correlation matrix",envir=.dico) -assign("desc_succesfully_imported","data were imported correctly",envir=.dico) -assign("desc_succesful_operation","Operation has been done correctly",envir=.dico) -assign("desc_tested_model_is","the model test is",envir=.dico) -assign("desc_there_is_no_rotation","there is no rotation",envir=.dico) -assign("desc_the_variable_lower","variable",envir=.dico) -assign("desc_the_variable_upper","The variable",envir=.dico) -assign("desc_this_analysis_will_not_be_performed",". This analysis will not be done.",envir=.dico) -assign("desc_this_index_is_prefered_for_most_cases"," This index is adapted in most situations. The modified M-estimator must be preferred for N<20",envir=.dico) -assign("desc_this_is_large_format","this is the wide format",envir=.dico) -assign("desc_this_is_long_format","this is the long format",envir=.dico) -assign("desc_times_less","times less",envir=.dico) -assign("desc_times_more","times more",envir=.dico) -assign("desc_to_display_results_use_summary","To display the results, please use summary(model.cfa)",envir=.dico) -assign("desc_total_observations","total number of observations",envir=.dico) -assign("desc_truncature_on_m_estimator_adapts_to_sample","The truncation on the M-estimetor adapts to the characteristics of the sample.",envir=.dico) -assign("desc_two_cols_are_needed","For a large-format repeat factor, it takes at least two columns",envir=.dico) -assign("desc_two_modalities_for_independante_categorial_variable","You must use an independent categorical variable with 2 modes",envir=.dico) -assign("desc_unauthorized_char_replaced","Unauthorized characters were used for the name. These characters were replaced by points",envir=.dico) -assign("desc_unavailable_distal_mediations","Distal mediations are not currently available / Distal mediations are not available for now",envir=.dico) -assign("desc_user_exited_aov_plus","you left aov.plus",envir=.dico) -assign("desc_value_must_be_between_zero_and_one","The value must be between 0 and 1",envir=.dico) -assign("desc_value_must_be_numeric","The value must be numerical and between the minimum and maximum of the dependent variable.",envir=.dico) -assign("desc_variable_added","Variable adds",envir=.dico) -assign("desc_variable_must_be_numeric_and_of_non_null_variance","the variable must be digital and have a nonzero variance.",envir=.dico) -assign("desc_variable_must_be_positive_int","the variable must be a positive *integer* integer",envir=.dico) -assign("desc_variables_are_in","selected variables are in",envir=.dico) -assign("desc_we_could_not_compute_anova_on_medians","Desole, we could not calculate the anova on the medianes, possibly due to a large number of ex aequo.",envir=.dico) -assign("desc_we_could_not_compute_robust_anova","Desole, we couldn't calculate the robust anova.",envir=.dico) -assign("desc_working_dir_is_now","The work directory is present",envir=.dico) -assign("desc_you_can_chose_predefined_or_manual_contrasts","You can choose predefined contrasts or specify them manually. In the latter case, please choose to specify the contrasts",envir=.dico) -assign("desc_you_can_still_add","You can still add a specific value to the total. Leave 0 if you don't want to add anything",envir=.dico) -assign("desc_you_can_still_multiply","You can still multiply the total by a specific value. Leave 1 if you no longer want to multiply by a new value",envir=.dico) -assign("desc_you_did_this_operation","you have done the following operation:",envir=.dico) -assign("desc_you_exited_afe","you left the AFE",envir=.dico) -assign("desc_you_have_selected","you have selected",envir=.dico) -assign("desc_you_must_give_obs_number","You must enter the observation number",envir=.dico) -assign("desc_your_dependant_variable_has","Your real dependent a",envir=.dico) -assign("desc_z_must_be_a_number","z must be a number",envir=.dico) -assign("desc_author","author: 'Generate automatically by easieR'",envir=.dico) -assign("desc_title","title: 'Results of your analyses'",envir=.dico) -assign("txt_absolute_value","absolute",envir=.dico) -assign("txt_added_variables_graph","Added variable scale",envir=.dico) -assign("txt_additions","adds",envir=.dico) -assign("txt_additive_effects","Additive effects",envir=.dico) -assign("txt_additive_model_variables","Variable additive model",envir=.dico) -assign("txt_add_of_cols","add columns",envir=.dico) -assign("txt_add_of_specific_value","addition of a specific value",envir=.dico) -assign("txt_adequation_adjustement_indexes","Adquest and adjustment indices",envir=.dico) -assign("txt_adequation_measurement_of_matrix","Measurement of the matrix",envir=.dico) -assign("txt_adequation_measures","Adequation measures",envir=.dico) -assign("txt_adequation_outside_diagonal","Adequation based on values outside the diagonal",envir=.dico) -assign("txt_adjusted_data_loftus_masson","Adjusted data (Loftus & Masson, 1994)",envir=.dico) -assign("txt_adjusted_means_graph","Adjusted-Graphic Averages",envir=.dico) -assign("txt_adjusted_means","Adjusted Averages",envir=.dico) -assign("txt_adjustement_measure","Adjustment measures",envir=.dico) -assign("txt_agreement","Agreement",envir=.dico) -assign("txt_adjusted_p_dot_value","Adjusted p value",envir=.dico) -assign("txt_aic_criterion","AIC - Akaike Information criteria",envir=.dico) -assign("txt_alpha_warning","Alpha warning",envir=.dico) -assign("txt_alternative","alternative",envir=.dico) -assign("txt_analysis_factor_component","factor and component analyses",envir=.dico) -assign("txt_analysis_on","analysis on",envir=.dico) -assign("txt_analysis_on_truncated_means","Analysis on truncated means",envir=.dico) -assign("txt_analysis_on_variable","Analysis on the variable",envir=.dico) -assign("txt_analysis_premature_abortion","Premature stop of analysis",envir=.dico) -assign("txt_ancova_application_conditions","Terms of application of the ancova",envir=.dico) -assign("txt_and_the_number_of_obs","and the number of observations",envir=.dico) -assign("txt_and_YZ","and YZ",envir=.dico) -assign("txt_anova_ancova","variance and covariance analysis",envir=.dico) -assign("txt_anova","Anova",envir=.dico) -assign("txt_anova_on","anova on",envir=.dico) -assign("txt_anova_on_modified_huber_estimator","Anova on Huber's Modified Localization Estimator",envir=.dico) -assign("txt_anova_on_truncated_means","Anova based on truncated averages",envir=.dico) -assign("txt_anova_with_welch_correction","Anova with Welch correction for heterogene variances",envir=.dico) -assign("txt_apparied_correlations","correlations paired",envir=.dico) -assign("txt_apriori","a priori",envir=.dico) -assign("txt_autocorrelation","Autocorrection",envir=.dico) -assign("txt_backward","Backward",envir=.dico) -assign("txt_backward_step_descending","Backward- not-has-not descending",envir=.dico) -assign("txt_barlett_test","Barlett Test",envir=.dico) -assign("txt_bayes_factor_10","Bayes Factor (10)",envir=.dico) -assign("txt_bayes_factor","BayesFactor",envir=.dico) -assign("txt_bayesian_approach_hierarchical_models","Bayesian approach of hierarchical models",envir=.dico) -assign("txt_bayesian_factor_by_group","Baysian actor by group",envir=.dico) -assign("txt_bayesian_factor","Baysian reactor",envir=.dico) -assign("txt_bayesian_factor_of_model","Model FB",envir=.dico) -assign("txt_bayesian_factors_10","Bayesian reactor 10",envir=.dico) -assign("txt_bayesian_factors_compute_null_with_bayesian_approach","Bayesian factors: calculates the equivalent of the null hypothesis test by adopting a Bayesian approach.",envir=.dico) -assign("txt_bayesian_factors_for_BP","Bayesian forces for Bravais-Pearson correlation",envir=.dico) -assign("txt_bayesian_factors_for_spearman","Bayesian forces for Spearman correlation",envir=.dico) -assign("txt_bayesian_factors_sequential","Sequential Bayesian Factors",envir=.dico) -assign("txt_bca_bootstrap_on_m_estimator","Bootstrap BCa type on the M-estimetor",envir=.dico) -assign("txt_beta_table","Beta table",envir=.dico) -assign("txt_between","between",envir=.dico) -assign("txt_bidirectionnal","Bidirectional",envir=.dico) -assign("txt_b_m_estimator","b (M estimator)",envir=.dico) -assign("txt_bootstrap_on_BP","Bootstrap on the Bravais Pearson correlation",envir=.dico) -assign("txt_bootstrap_t_method","bootstrap-t method",envir=.dico) -assign("txt_bootstrap_t_method_on_truncated_means","Bootstrap using t method on truncated averages",envir=.dico) -assign("txt_BP_correlation_by_group","Bravais-Pearson group correction",envir=.dico) -assign("txt_breusch_pagan_test","Verification of the non-constency of the error variance (Breusch-Pagan test)",envir=.dico) -assign("txt_cancel","cancel",envir=.dico) -assign("txt_cauchy_prior_width","Cauchy Prior With (r)",envir=.dico) -assign("txt_center_or_center_reduce","Center / center reduce",envir=.dico) -assign("txt_center_reduce","center reduce",envir=.dico) -assign("txt_ceres_graph_linearity","Character of Ceres Testing Linearitis",envir=.dico) -assign("txt_chi_adjustement","Adjustment",envir=.dico) -assign("txt_chi_independance","Independence",envir=.dico) -assign("txt_chi_results_between_var_x","Results of chi.two between variable",envir=.dico) -assign("txt_chi_squared","chi two",envir=.dico) -assign("txt_chi_squared_empirical","chi square empirical",envir=.dico) -assign("txt_chi_squared_likelihood_max","chi square of the maximum likelihood",envir=.dico) -assign("txt_chi_squared_null_model","chi square of model null",envir=.dico) -assign("txt_chi_squared_type","Khi type two",envir=.dico) -assign("#txt_choice","choice",envir=.dico) -assign("txt_coeff_table","Table of coefficients",envir=.dico) -assign("txt_col_correspoding_to_variable","Columns corresponding to the variable",envir=.dico) -assign("txt_col_mean","mean columns",envir=.dico) -assign("txt_cols","columns",envir=.dico) -assign("txt_col_separator","Column Separator",envir=.dico) -assign("txt_cols_in_repeated_measure","Columns in Repeated Measures",envir=.dico) -assign("txt_cols_multiplication","column multiplication",envir=.dico) -assign("txt_comma","virgulate",envir=.dico) -assign("txt_compare_to_baseline","Comparison with a base line",envir=.dico) -assign("txt_compare_two_correlations","Comparison of two correlations",envir=.dico) -assign("txt_comparison_of_two_correlations","comparison of the two correlations",envir=.dico) -assign("txt_comparison_on_truncated_means","Comparison based on truncated averages",envir=.dico) -assign("txt_comparisons_XY","Comparison of XY",envir=.dico) -assign("txt_comparison_to_norm","Comparison with a Standard",envir=.dico) -assign("txt_comparison_two_by_two","Comparison 2 to 2",envir=.dico) -assign("txt_compile_report","generate a report",envir=.dico) -assign("txt_complementary_results","Complementary results (e.g. interaction contrasts and adjusted averages)",envir=.dico) -assign("txt_complete_dataset","Complete data",envir=.dico) -assign("txt_complete_model","Complete Model",envir=.dico) -assign("txt_complexity","complexity",envir=.dico) -assign("txt_complex_model","complex model",envir=.dico) -assign("txt_confidance_threshold","Confidence threshold (1- alpha)",envir=.dico) -assign("txt_confidence_interval_estimated_by_bootstrap","Interval of trust estimates by bootstrap",envir=.dico) -assign("txt_confidence_interval","Confidential Interval",envir=.dico) -assign("txt_confidence_interval_superior_limit","Upper bound CI",envir=.dico) -assign("txt_confidence_interval_inferior_limit ","Lower bound CI",envir=.dico) -assign("txt_confidence_interval_of_saturations_on_bootstrap","Interval of confidence of saturations on the basis of bootstrap - may be biased in presence of Heyhood case",envir=.dico) -assign("txt_confidence_interval_on_bootstrap","Trust interval based on bootstrap",envir=.dico) -assign("txt_confidence_interval_on_standard_error","Confidence interval based on standard alpha error",envir=.dico) -assign("txt_confirmatory_factorial_analysis"," confirmatory factor analysis",envir=.dico) -assign("txt_contrast","contrast",envir=.dico) -assign("txt_contrasts","contrasts",envir=.dico) -assign("txt_contrasts_for","Contrasts for",envir=.dico) -assign("txt_contrasts_table_imitating_commercial_softwares","Table of contrasts imitating commercial software",envir=.dico) -assign("txt_contrasts_table","Contrast table",envir=.dico) -assign("txt_control_variables","Variable-s to control",envir=.dico) -assign("txt_correction_for_polyc_corr_must_be_between_zero_and_one","The correction for the calculation of polycoric correlations shall be between 0 and 1.",envir=.dico) -assign("txt_correlation_between_scores_and_factors","Correlations of scores with factors",envir=.dico) -assign("txt_correlation_between_var_x","Correlation between variable",envir=.dico) -assign("txt_correlation_is","correction",envir=.dico) -assign("txt_correlation_matrix_determinant","Determining the correlation matrix",envir=.dico) -assign("txt_correlation_matrix_determinant_information","Determining the correlation matrix: information",envir=.dico) -assign("txt_correlations_between_factors","correlations between factors",envir=.dico) -assign("txt_correlations_comparison","comparison of correlations",envir=.dico) -assign("txt_correlations_matrix_afe","Correlation matrix used for AFE",envir=.dico) -assign("txt_covariance_matrix_adjusted","Adjusted covariance matrix",envir=.dico) -assign("txt_covariance_matrix_estimated","Estimated covariance matrix",envir=.dico) -assign("txt_cox_snell_r_2","Cox and Snell R^2",envir=.dico) -assign("txt_cronbach_alpha","Cronbach Alpha",envir=.dico) -assign("txt_cronbach_alpha_on_whole_scale","Cronbach Alpha on Scale Totalite",envir=.dico) -assign("txt_cross_validation","Validation crossee",envir=.dico) -assign("txt_csv_file","CSV file",envir=.dico) -assign("txt_cumulated_explaination_ratio","Cumulative share of explanation",envir=.dico) -assign("txt_cumulated_explained_variance_ratio","proportion of variance explained cumulated",envir=.dico) -assign("txt_dataframe_choice","Dataframe selection",envir=.dico) -assign("txt_data_import_export_save","Data - (Import, export, backup)",envir=.dico) -assign("txt_decimal_separator","Separator of decimals",envir=.dico) -assign("txt_default_outputs","Releases by default",envir=.dico) -assign("txt_delete_observations_with_missing_values","Deletion of observations with missing values",envir=.dico) -assign("txt_denominator","Denominator",envir=.dico) -assign("txt_dependant_variables","Variable-dependent-s",envir=.dico) -assign("txt_dependant_variable","dependent variable",envir=.dico) -assign("txt_descriptive_statistics_by_group","Descriptive statistics by group",envir=.dico) -assign("txt_detailed_corr_analysis","Analysis desize (Bravais Pearson/Spearman/tau) for one or few correlations",envir=.dico) -assign("txt_deviation","Deviance",envir=.dico) -assign("txt_dichotomic_ordinal","dichotomics/ordinal",envir=.dico) -assign("txt_difference","Difference",envir=.dico) -assign("txt_distance_mediation_effect","Remote mediation effect",envir=.dico) -assign("txt_distance_mediator","Mediation distance",envir=.dico) -assign("txt_do_nothing_keep_all_obs","Do nothing - Keep all observations",envir=.dico) -assign("txt_dot","point",envir=.dico) -assign("txt_durbin_watson_test_autocorr","Durbin-Watson test - autocorrelations",envir=.dico) -assign("txt_dw_statistic","D-W statistics",envir=.dico) -assign("txt_dynamic_crossed_table","Dynamic Cross Table",envir=.dico) -assign("txt_effect","Effect",envir=.dico) -assign("txt_equals_to","egal a",envir=.dico) -assign("txt_error","error",envir=.dico) -assign("txt_estimated_parameters_not_standardized","Non-standardized Parameters",envir=.dico) -assign("txt_estimated_parameters","Advised parameters",envir=.dico) -assign("txt_estimated_parameters_standardized","Standardized estimated parameters",envir=.dico) -assign("txt_estimation","estimate",envir=.dico) -assign("txt_excel_file"," Excel file",envir=.dico) -assign("txt_exogenous_fixed_variables","Variables exogenes fixed [fixed.x,default]",envir=.dico) -assign("txt_expected","Attended",envir=.dico) -assign("txt_expected_sample","Expected effects",envir=.dico) -assign("txt_experimental_pan_between","Pan experimental enters",envir=.dico) -assign("txt_explaination_ratio","Proportion of explanation",envir=.dico) -assign("txt_explained_variance_ratio","proportion of variance explained",envir=.dico) -assign("txt_explained_variance","Variance explained",envir=.dico) -assign("txt_exponant","exposant",envir=.dico) -assign("txt_exponant_or_root","exposant or root",envir=.dico) -assign("txt_exponential","exponential",envir=.dico) -assign("txt_export_data","export data",envir=.dico) -assign("txt_factorial_analysis","factorial analysis",envir=.dico) -assign("txt_factorial_analysis_using_fa_with_method","factorial analysis using the fa function of the psych package with the method",envir=.dico) -assign("txt_factorial_exploratory_analysis","Exploratory factor analysis",envir=.dico) -assign("txt_factor_name","factor name",envir=.dico) -assign("txt_factors","factors.",envir=.dico) -assign("txt_factors_ortho","Orthogonality of factors [orthogonal,FALSE]",envir=.dico) -assign("txt_factors_to_keep_accord_to_parallel_analysis_is","the number of factors to remember according to the parallel analysis is",envir=.dico) -assign("txt_fiability_analysis","Analysis of reliability and agreement",envir=.dico) -assign("txt_fiability_by_removed_item","reliability by item deletes",envir=.dico) -assign("txt_for_a_detailed_results_description_distal","For a detailed description of the results, ?distal.med",envir=.dico) -assign("txt_for_a_detailed_results_description_mediation","For a detailed description of the results, ?mediation",envir=.dico) -assign("txt_forward_step_ascending","Forward - not-a-not ascending",envir=.dico) -assign("txt_friedman_anova_pairwise_comparison","Comparison 2 to 2 for Friedman's ANOVA",envir=.dico) -assign("txt_f_value","F value",envir=.dico) -assign("txt_get_working_dir","get work directory",envir=.dico) -assign("txt_global_model_estimation","Global Model Estimation",envir=.dico) -assign("txt_graphic_mean_sd","Graphic representation - Medium and level-type",envir=.dico) -assign("txt_graphics","Graphics",envir=.dico) -assign("txt_graphics_informations","Information on graphics",envir=.dico) -assign("txt_group_analysis","Group Analysis",envir=.dico) -assign("txt_groups_analysis","group analysis",envir=.dico) -assign("txt_groups_variables","Variable-s groups",envir=.dico) -assign("txt_grubbs_test"," Grubbs Test",envir=.dico) -assign("txt_hierarchical_factorial_analysis","Hierarchical factor analysis",envir=.dico) -assign("txt_hierarchical_model_analysis","Hierarchical model analysis",envir=.dico) -assign("txt_hierarchical_models_complete_model_sig_at_each_step","Hierarchical models - significativite of the complete model to each step",envir=.dico) -assign("txt_hierarchical_models_deviance_table","Table of the analysis of the deviance of hierarchical models",envir=.dico) -assign("txt_hierarchical_models","hierarchical models",envir=.dico) -assign("txt_hierarchical_models_variance_analysis_table","Table of variance analysis of hierarchical models",envir=.dico) -assign("txt_hosmer_lemeshow_r_2","Hosmer and Lemeshow R^2",envir=.dico) -assign("txt_hypergeom_total_sample_fixed_rows_cols","hypergeom - Total fixed strength for rows and columns",envir=.dico) -assign("txt_hypothesis_analysis","Analysis - Hypothesis tests",envir=.dico) -assign("txt_identified_outliers_synthesis","Synthesis of the number of observations considered to be influential",envir=.dico) -assign("txt_identifying_outliers","Identification of influential values",envir=.dico) -assign("txt_id_variable","Variable *Identifier*",envir=.dico) -assign("txt_import_data","import data",envir=.dico) -assign("txt_imput_missing_values","Impacting Missing Values",envir=.dico) -assign("txt_independant_correlations","Independent adjustments",envir=.dico) -assign("txt_independant_group_variables","Variables to independent groups",envir=.dico) -assign("txt_independant_variable","Independent variable",envir=.dico) -assign("txt_indepmulti_fixed_sample_rows_cols","indepMulti - Fixed number for columns - variable",envir=.dico) -assign("txt_indepmulti_total_fixed_rows_cols","indepMulti - Total fixed strength for lines - variable",envir=.dico) -assign("txt_inferior","Inner",envir=.dico) -assign("txt_inferior_or_equal_to","inferior or equal",envir=.dico) -assign("txt_inferior_proba","inferior probability",envir=.dico) -assign("txt_inferior_to","under a",envir=.dico) -assign("txt_inflation_variance_factor","Inflation factor of variance",envir=.dico) -assign("txt_influence_method","Influence Measurement",envir=.dico) -assign("txt_information","Information",envir=.dico) -assign("txt_init_values","Departure values",envir=.dico) -assign("txt_inspect_initial_values","Inspect start values",envir=.dico) -assign("txt_inspect_model_matrices","Inspect model matrices",envir=.dico) -assign("txt_inspect_model_representation","Inspect model representation",envir=.dico) -assign("txt_interaction_effects","Interaction effects",envir=.dico) -assign("txt_interactive_model_variables","Interactive models",envir=.dico) -assign("txt_is_different_from","is different from",envir=.dico) -assign("txt_jointmulti_total_fixed_sample","jointMulti - Total fixed staff",envir=.dico) -assign("txt_judge1","Juge 1",envir=.dico) -assign("txt_judge2","Juge 2",envir=.dico) -assign("txt_kaiser_meyer_olkin_index","Kaiser-Meyer-Olkin global index",envir=.dico) -assign("txt_keep_default_values","Keep values by default",envir=.dico) -assign("txt_kendall_coeff","Kendall Match Coefficient",envir=.dico) -assign("txt_kendall_partial_semipartial_tau","Kendall partial/semipartial",envir=.dico) -assign("txt_kendall_partial_tau","Kendall partial rate",envir=.dico) -assign("txt_kendall_semipartial_tau","Kendall Semi-Partial",envir=.dico) -assign("txt_kendall_tau","Kendall Tau",envir=.dico) -assign("txt_kolmogorov_smirnov_comparing_two_distrib","Kolmogorov-Smirnov test comparing two distributions",envir=.dico) -assign("txt_labeled_outliers","Values considered influential",envir=.dico) -assign("txt_latent_variable_name","Latent variable name",envir=.dico) -assign("txt_less_square_diagonally_pondered","mind square weight diagonally",envir=.dico) -assign("txt_less_square_generalized","mind tile generalises",envir=.dico) -assign("txt_less_square_not_pondered","mind unweighted square",envir=.dico) -assign("txt_less_square_pondered","mind square",envir=.dico) -assign("txt_levene_test_verifying_homogeneity_variances","Levene test checking variance homogeneity",envir=.dico) -assign("txt_likelihood_only_for_estimator","True (only for estimator=ML) [likelihood= default]",envir=.dico) -assign("txt_likelihood_ratio_g_test","Ratio of likelihood (G test)",envir=.dico) -assign("txt_lilliefors_d","D de Lilliefors",envir=.dico) -assign("txt_linearity_graph_between_predictors_and_dependant_variable","Character testing linearite between predictors and dependent variable",envir=.dico) -assign("txt_link_only_for_estimator","Link (only for estimator = ML) [link =probit]",envir=.dico) -assign("txt_list_of_objects_in_mem","list of objects in memory",envir=.dico) -assign("txt_logarithm","logarithm",envir=.dico) -assign("txt_long_or_large_format","Long format wide format",envir=.dico) -assign("txt_lower_bound_rmsea","inferior limit of RMSEA",envir=.dico) -assign("txt_mann_whitney_test"," Mann-Whitney test - Wilcoxon",envir=.dico) -assign("txt_mathematical_operations_on_variables","Mathematic operations on variables",envir=.dico) -assign("txt_matrix_type","matrix type",envir=.dico) -assign("txt_max_likelihood_chi_squared_proba_value","value of the probability of the chi carre maximum likelihood",envir=.dico) -assign("txt_max_likelihood","maximum likelihood",envir=.dico) -assign("txt_mcnemar_results_between_var_x","Results of McNemar test between variable",envir=.dico) -assign("txt_mcnemar_test","McNemar Test",envir=.dico) -assign("txt_mcnemar_test_with_continuity_correction","McNemar test with continuity correction",envir=.dico) -assign("txt_mcnemar_test_without_yates_correction","McNemar test without continuity correction",envir=.dico) -assign("txt_mcnemar_test_with_yates_correction","McNemar Test with Yates Correction",envir=.dico) -assign("txt_mean1","Average1",envir=.dico) -assign("txt_mean2","Average2",envir=.dico) -assign("txt_mean_complexity","Medium Complexity",envir=.dico) -assign("txt_mean_complexity_is","average complexity is of",envir=.dico) -assign("txt_means_adjusted_standard_errors","adjusted averages and standard errors",envir=.dico) -assign("txt_means_comparison","Comparison of Averages",envir=.dico) -assign("txt_mean_sd_for_adjusted_data","Average and scale-type for adjusted data",envir=.dico) -assign("txt_mean_sd_for_non_adjusted_data","Average and scale-type for unadjusted data",envir=.dico) -assign("txt_mean_sd","Average and scale-type",envir=.dico) -assign("txt_measured_variable_name","Measuring variable name",envir=.dico) -assign("txt_median","Mediane",envir=.dico) -assign("txt_mediation_effect","Mediation Effects",envir=.dico) -assign("txt_mediator2","Mediator 2",envir=.dico) -assign("txt_mediator","Mediator",envir=.dico) -assign("txt_method_choice","Choice of the method",envir=.dico) -assign("txt_min_correlation_between_scores_and_factors","Minimum possible correlation of scores with factors",envir=.dico) -assign("txt_minus","less",envir=.dico) -assign("txt_missing_values_treatment","Treatment of Missing Values",envir=.dico) -assign("txt_mixt_correlations","mixed correlations",envir=.dico) -assign("txt_modalities_name_for","Names of the modes for",envir=.dico) -assign("txt_modalities_to_regroup","Modalites to group",envir=.dico) -assign("txt_modality","modality",envir=.dico) -assign("txt_model_degrees_of_freedom","degrees of model freedom",envir=.dico) -assign("txt_model_matrix","Model Matrix",envir=.dico) -assign("txt_model_representation","Model representation",envir=.dico) -assign("txt_model_significance","Significativite of the global model",envir=.dico) -assign("txt_multicolinearity_tests","Multicolinearite tests",envir=.dico) -assign("txt_multicolinearity_test","Multicolinearite test",envir=.dico) -assign("txt_multiple_imputation_amelia","Multiple imputation - Amelia",envir=.dico) -assign("txt_multiple_r_square_of_factors_scores","R multiple square scores with factors",envir=.dico) -assign("txt_multiplication","multiplication",envir=.dico) -assign("txt_multivariate_normality","Normalite multivarie",envir=.dico) -assign("txt_nb_variables_measured","Number of variables measured",envir=.dico) -assign("txt_negative_values","negative values",envir=.dico) -assign("txt_new_data_set","new data set",envir=.dico) -assign("txt_new_dir","new directory",envir=.dico) -assign("txt_N_of_XY_corr","XY correlation N",envir=.dico) -assign("txt_N_of_XY_NUM_corr","N of XY:TXT",envir=.dico) -assign("txt_N_of_XZ_corr","N of XZ correlation",envir=.dico) -assign("txt_N_of_XZ_NUM_corr","N of XZ:TXT",envir=.dico) -assign("txt_non_adjusted_data","Unadjusted data",envir=.dico) -assign("txt_non_centered","No center",envir=.dico) -assign("txt_no","no",envir=.dico) -assign("txt_non_parametric_test","Nonparametric test",envir=.dico) -assign("txt_non_param_model","Non-parametric model",envir=.dico) -assign("txt_non_param_test","non-parametric test",envir=.dico) -assign("txt_non_pondered_coeff","Kappa coefficient non-weight",envir=.dico) -assign("txt_non_standardized_residuals","Non-standardised residues",envir=.dico) -assign("txt_null_hypothesis_tests","H0 test",envir=.dico) -assign("txt_null_model_degrees_of_freedom","Degrees of null model freedom",envir=.dico) -assign("txt_numerator","Numerator",envir=.dico) -assign("txt_objective_function_of_model","objective model function",envir=.dico) -assign("txt_objective_function_of_null_model","objective null model function",envir=.dico) -assign("txt_objects_in_mem","Memory objects",envir=.dico) -assign("txt_object_to_remove","Objects to be deleted",envir=.dico) -assign("txt_observed","Observations",envir=.dico) -assign("txt_observed_sample","Observed Effects",envir=.dico) -assign("txt_odd_ratio","Odd ratio",envir=.dico) -assign("txt_order","Sort",envir=.dico) -assign("txt_orthogonals_inverse","orthogonal reverses",envir=.dico) -assign("txt_orthogonals","orthogonal",envir=.dico) -assign("txt_other_correlations","Other correlations",envir=.dico) -assign("txt_other_data","other data",envir=.dico) -assign("txt_outliers","Influential observations",envir=.dico) -assign("txt_outliers_synthesis","Synthesis of influential observations",envir=.dico) -assign("txt_outliers_values","Influential values",envir=.dico) -assign("txt_packages_install","Installation of packages",envir=.dico) -assign("txt_packages_update","packages update",envir=.dico) -assign("txt_packages_verification","Verification of packages",envir=.dico) -assign("txt_parallel_analysis","parallel analyses",envir=.dico) -assign("txt_param_model","parametric model",envir=.dico) -assign("txt_param_tests","Parametric tests",envir=.dico) -assign("txt_param_test","parametric test",envir=.dico) -assign("txt_partial_and_semi_correlations","Partial and semi-partial corrections",envir=.dico) -assign("txt_partial_corr_BP_by_group","Partial correction of Bravais-Pearson by group",envir=.dico) -assign("txt_partial_correlations_matrix","Partial Correlations Matrix",envir=.dico) -assign("txt_partial_rho","Rho partiale de Spearman",envir=.dico) -assign("txt_partial_semi_BP","Partial/semi-partial correction of Bravais Pearson",envir=.dico) -assign("txt_partial_semi_partial_rho","Partial/Semipartial Rho",envir=.dico) -assign("txt_partial_spearman_by_group","Partial patch of Spearman by group",envir=.dico) -assign("txt_participants_id","participating identifier",envir=.dico) -assign("txt_partila_correlations","Partial corrections",envir=.dico) -assign("txt_percentage_col","Percentage by column",envir=.dico) -assign("txt_percentage_row","Percentage per line",envir=.dico) -assign("txt_percentage_total","Total percentage",envir=.dico) -assign("txt_percentile_bootstrap_on_m_estimators","Percentile bootstrap on M-estimetor",envir=.dico) -assign("txt_p_estimation_with_monter_carlo","Value estimated by Monte Carlo simulation",envir=.dico) -assign("txt_plus","plus",envir=.dico) -assign("txt_poisson_total_not_fixed_sample","fish - total non-fixed",envir=.dico) -assign("txt_polyc_correlations","polychoric correlations",envir=.dico) -assign("txt_polynomials","polynomials",envir=.dico) -assign("txt_pondered_kappa","Kappa weight coefficient",envir=.dico) -assign("txt_positive_values","positive values",envir=.dico) -assign("txt_predicted_probabilities","Probability",envir=.dico) -assign("txt_predictor","Predictor",envir=.dico) -assign("txt_principal_analysis","Main Analysis",envir=.dico) -assign("txt_principal_analysis_using_psych_with_algo","main component analysis using the [principal] function of the psych package, the algorithm is",envir=.dico) -assign("txt_principal_component_analysis","Main Component Analysis",envir=.dico) -assign("txt_probabilities","probabilities",envir=.dico) -assign("txt_probability_matrix","probability matrix",envir=.dico) -assign("txt_probability_value","probability value",envir=.dico) -assign("txt_proper_values_index","Index of own values",envir=.dico) -assign("txt_pseudo_r_square_delta","Delta du pseudo R carre",envir=.dico) -assign("txt_p_value_with_monte_carlo","Value p by Monte Carlo simulation",envir=.dico) -assign("txt_ranks_lower","ranges",envir=.dico) -assign("txt_ranks_upper","Rangs",envir=.dico) -assign("txt_references","References",envir=.dico) -assign("txt_remove_object_in_memory","Deletion of memory object",envir=.dico) -assign("txt_replace_by_mean","Replace by Average",envir=.dico) -assign("txt_replace_by_median","Replace with media",envir=.dico) -assign("txt_residual_distribution","Distribution of residual",envir=.dico) -assign("txt_residual_error","Residual error",envir=.dico) -assign("txt_residual","residual",envir=.dico) -assign("txt_residuals_distribution","Distribution of survivors",envir=.dico) -assign("txt_residue","Residus",envir=.dico) -assign("txt_residues_significativity_holm_correction","Significativite des residus - probability corrected by applying the Holm method",envir=.dico) -assign("txt_residue_standardized_adjusted","Residues standardises fit",envir=.dico) -assign("txt_residue_standardized","Residue standardized",envir=.dico) -assign("txt_result","Result",envir=.dico) -assign("txt_rho","Rho de Spearman",envir=.dico) -assign("txt_robust_analysis","Sturdy analyses",envir=.dico) -assign("txt_robusts","robust",envir=.dico) -assign("txt_robusts_statistics","Strudy statistics",envir=.dico) -assign("txt_robust_statistics","Strudy statistics - can take time",envir=.dico) -assign("txt_robusts_tests_with_bootstraps","Sturdy test - involving bootstraps",envir=.dico) -assign("txt_rotation_is_a_rotation","rotation is a rotation",envir=.dico) -assign("txt_sample_size_NUM","Size of sample:TXT",envir=.dico) -assign("txt_saturations_sum_of_squares","Sum of squares of saturations",envir=.dico) -assign("txt_search_for_new_function","Search for a new function",envir=.dico) -assign("txt_second_variables_set","Second set of variables",envir=.dico) -assign("txt_selected_data","Give that you just selected",envir=.dico) -assign("txt_selection_method_akaike","Selection method - Akaike information criteria",envir=.dico) -assign("txt_selection_method_bayesian_factor","Selection methods: Bayesian factors",envir=.dico) -assign("txt_selection_method","Selection method",envir=.dico) -assign("txt_selection_methods","Selection methods",envir=.dico) -assign("txt_selection","selection",envir=.dico) -assign("txt_select_obs","Select Observations",envir=.dico) -assign("txt_select_variables","Select variables",envir=.dico) -assign("txt_semi_BP","Semi-partial correction of Bravais Pearson",envir=.dico) -assign("txt_semicolon","point comma",envir=.dico) -assign("txt_semi_partial_rho","Spearman Semi-Partial Rho",envir=.dico) -assign("txt_sequential_bayesian_factors_robustness_analysis","Sequential Bayesian Factors - Robustness Analysis",envir=.dico) -assign("txt_shapiro_wilk","W de Shapiro-Wilk",envir=.dico) -assign("txt_simple_mediation_effect","simple mediation effects",envir=.dico) -assign("txt_slopes_homogeneity_between_groups_on_dependant_variable","Test of homogeneite slopes between groups on the dependent variable",envir=.dico) -assign("txt_spearman_kendall_corr_by_group","Spearman/Kendall group correction",envir=.dico) -assign("txt_specific_val_multiplication","multiplication of a specific value",envir=.dico) -assign("txt_specify_contrasts","specify your contrasts",envir=.dico) -assign("txt_specify_model","Specify the model",envir=.dico) -assign("txt_specify_working_dir","specify work directory",envir=.dico) -assign("txt_spss_file","SPSS file",envir=.dico) -assign("txt_square","square",envir=.dico) -assign("txt_rectangular","rectangular",envir=.dico) -assign("txt_standardized_parameters","Standardized Parameters",envir=.dico) -assign("txt_statistic","statistic",envir=.dico) -assign("txt_step","etape",envir=.dico) -assign("txt_student_bootstrap_on_truncated_means","Student bootstrap on truncated means",envir=.dico) -assign("txt_student_t_by_group","Student t by group",envir=.dico) -assign("txt_student_t_independant","tstudent for independent samples",envir=.dico) -assign("txt_student_t","Student t",envir=.dico) -assign("txt_student_t_test_norm","Student test - comparison to a standard",envir=.dico) -assign("txt_student_t_test_paired","Student test - comparison of two matching samples",envir=.dico) -assign("txt_substraction","subtraction",envir=.dico) -assign("txt_sufficient_factors","sufficient factors",envir=.dico) -assign("txt_superior_or_equal_to","upper or equal a",envir=.dico) -assign("txt_superior_proba","high probability",envir=.dico) -assign("txt_superior","Superior",envir=.dico) -assign("txt_superior_to","upper a",envir=.dico) -assign("txt_supports_alternative","In favour of alternative hypothesis",envir=.dico) -assign("txt_supports_null","In favour of null hypothesis",envir=.dico) -assign("txt_suppress_all_outliers","Deleting all outliers",envir=.dico) -assign("txt_suppress_outliers_manually","Manual Deletion",envir=.dico) -assign("txt_synthesis_table","Summary Table",envir=.dico) -assign("txt_teaching_material","Pedagogical material",envir=.dico) -assign("txt_tetra_polyc_corr_matrix_or_mixt","Tetrachoric/polychoric or mixed correlation matrix",envir=.dico) -assign("txt_this_tests_if","It tests if",envir=.dico) -assign("txt_threshold","Threshold",envir=.dico) -assign("txt_time_1","time 1",envir=.dico) -assign("txt_time1","time1",envir=.dico) -assign("txt_time_2","time 2",envir=.dico) -assign("txt_time2","time2",envir=.dico) -assign("txt_tolerance","Tolerance",envir=.dico) -assign("txt_total_sample_not_fixed","Total non-fixed effect",envir=.dico) -assign("txt_troncature_num","Troncature:TXT",envir=.dico) -assign("txt_truncated_means","Truncated averages",envir=.dico) -assign("txt_t_test_choice","Test selection t",envir=.dico) -assign("txt_tucker_lewis_fiability_factor","Tucker Lewis reliability factor - TLI",envir=.dico) -assign("txt_two_independant_samples","Two independent samples",envir=.dico) -assign("txt_two_paired_samples","Two paired samples",envir=.dico) -assign("txt_txt_file","Txt file",envir=.dico) -assign("txt_type","Type",envir=.dico) -assign("txt_understanding_alpha_and_power","Understanding Alpha and Power",envir=.dico) -assign("txt_understanding_bayesian_inference","Understanding a Bayesian Inference",envir=.dico) -assign("txt_understanding_central_limit_theorem","Understanding the central theorem limit",envir=.dico) -assign("txt_understanding_confidance_interval","Understanding a confidence interval",envir=.dico) -assign("txt_understanding_corr_2","Understanding a correlation 2",envir=.dico) -assign("txt_understanding_corr","Understanding correlation",envir=.dico) -assign("txt_understanding_heterogenous_variance_effects","Understanding the effects of heterogene variances",envir=.dico) -assign("txt_understanding_likelihood","Understanding the maximum likelihood",envir=.dico) -assign("txt_understanding_negative_positive_predic_power","Understanding positive predictive power and negative predictive power",envir=.dico) -assign("txt_understanding_prev_sens_specificity_2","Understanding Prevalence, Sensibility and Specificity 2",envir=.dico) -assign("txt_understanding_prev_sens_specificity","Understanding prevalence, sensitivity and specificity",envir=.dico) -assign("txt_upper_bound_rmsea","the upper limit of the RMSEA",envir=.dico) -assign("txt_user_exited_easieR","You left easieR",envir=.dico) -assign("txt_values","values",envir=.dico) -assign("txt_value","value",envir=.dico) -assign("txt_variable_descriptive_statistics","Descriptive variable statistics",envir=.dico) -assign("txt_variables_coeff_matrix","variable coefficient matrix",envir=.dico) -assign("txt_variables_contribution_to_model","Contribution of variables to the model",envir=.dico) -assign("txt_variables_from_step","Variable of this step",envir=.dico) -assign("txt_verify_packages_install","Check package installation",envir=.dico) -assign("txt_view_data","see data",envir=.dico) -assign("txt_warning","Warning",envir=.dico) -assign("txt_wilcoxon_by_group","Wilcoxon by group",envir=.dico) -assign("txt_without_outliers","Data without influential value",envir=.dico) -assign("txt_without_welch_correction","without Welch correction",envir=.dico) -assign("txt_without_yates_correction","Without Yates Correction",envir=.dico) -assign("txt_with_welch_correction","with Welch correction",envir=.dico) -assign("txt_with_yates_correction","With Yates Correction",envir=.dico) -assign("txt_working_dir","Work Directory",envir=.dico) -assign("txt_x_axis_variables","Variable-s in abscess",envir=.dico) -assign("txt_XY_correlation","Correlation between XY",envir=.dico) -assign("txt_XY_NUM_correlation","Correlation between XY:TXT",envir=.dico) -assign("txt_XZ_correlation","Correlation between XZ",envir=.dico) -assign("txt_XZ_NUM_correlation","Correlation between XZ:TXT",envir=.dico) -assign("txt_y_axis_variables","Variable-s ordered",envir=.dico) -assign("txt_yes","yes",envir=.dico) -assign("txt_your_data","Your data",envir=.dico) -assign("txt_YZ_correlation","Correlation between YZ",envir=.dico) -assign("txt_YZ_NUM_correlation","Correlation between YZ:TXT",envir=.dico) -assign("ask_probability_correction","Which p adjustment do you want ? If you do not want any p adjustment, choose +none+",envir=.dico) -assign("ask_contrasts_must_be_ortho","The contrasts must be orthogonal. Do you want to continue?",envir=.dico) -assign("desc_bayesian_factors_chosen_in","Baysian factors is choosen in",envir=.dico) -assign("desc_cross_validation_issues","cross validation is encountering some issues",envir=.dico) -assign("desc_easier_metapackage","easieR: An R metapackage. Retrieved from https://github.com/NicolasStefaniak/easieR",envir=.dico) -assign("desc_first_time_easier"," If you are using easieR for the first time, please use the function ez.install in order to ensure that easieR will work properly.n If you are using easieR for the first time, please use the ez.install function to ensure that easieR works properly.",envir=.dico) -assign("ask_chose_variables","please choose the variable(s)",envir=.dico) -assign("ask_correlations_type","Type of correlations?",envir=.dico) -assign("ask_dependant_variable_name","What is the name of the dependent variable?",envir=.dico) -assign("ask_factors_number","Number of factors?",envir=.dico) -assign("ask_filename","What name do you want to give the file?",envir=.dico) -assign("ask_independant_variable_name","What is the name of the independent variable?",envir=.dico) -assign("ask_is_long_format_correct","Is the structure in a long format of your data correct?",envir=.dico) -assign("ask_model","Model?",envir=.dico) -assign("ask_ordinal_variables","Ordinal Variables?",envir=.dico) -assign("ask_save_results","Save Results?",envir=.dico) -assign("ask_save","Do you want to save?",envir=.dico) -assign("ask_specify_contrasts","Please specify contrasts.",envir=.dico) -assign("ask_variables","What are the variables to select?",envir=.dico) -assign("ask_variables_type","Nature of variables?",envir=.dico) -assign("ask_what_to_do","What do you want to do?",envir=.dico) -assign("ask_which_analysis","What analysis do you want?",envir=.dico) -assign("desc_all_contrasts_description","The a priori contrasts correspond to the contrasts that allow to test hypotheses a priori.nThe contrasts 2 to 2 allow to make all comparisons 2 to 2 by applying or not a correction to probabilities",envir=.dico) -assign("desc_contrasts_must_be_coeff_matrices_in_list","Contracts must be matrix coefficients placed in a list whose name of each level corresponds to a factor",envir=.dico) -assign("desc_percentage_outliers","% of observations considered influential",envir=.dico) -assign("desc_robusts_statistics_could_not_be_computed_verify_WRS","The robust statistics could not be achieved. Check the installation of the WRS package",envir=.dico) -assign("desc_some_participants_have_missing_values_on_repeated_measures","Some participants have missing values on the repeted measurement factors. They will be removed from the analyses.",envir=.dico) -assign("txt_absence_of_difference_between_groups_test_on","Test of absence of difference between groups on",envir=.dico) -assign("txt_anova_on_medians","Anova on the media",envir=.dico) -assign("txt_anova_on_m_estimator","ANOVA on M estimator",envir=.dico) -assign("txt_bayesian_factors","Baysian Factors",envir=.dico) -assign("txt_BP_correlation","Bravais-Pearson Correlation",envir=.dico) -assign("txt_center","center",envir=.dico) -assign("txt_cohen_d","D of Cohen",envir=.dico) -assign("txt_correlations","Correlations",envir=.dico) -assign("txt_correlations_matrix","Correlations Matrix",envir=.dico) -assign("txt_descriptive_statistics_of_interaction_between_x","Descriptive statistics of the interaction between",envir=.dico) -assign("txt_descriptive_statistics","Descriptive statistics",envir=.dico) -assign("txt_empirical_chi_square_proba_value","value of the probabilities of empirical chi tile",envir=.dico) -assign("txt_factor","factor.",envir=.dico) -assign("txt_friedman_anova","Anova de Friedman",envir=.dico) -assign("txt_import_results","import results",envir=.dico) -assign("txt_interface_objects_in_memory","Interface - objects in memory, clean memory, working directory, language",envir=.dico) -assign("txt_intraclass_correlation","Intraclass correlation",envir=.dico) -assign("txt_kruskal_wallis_pairwise","Kruskal-Wallis Test - Comparison Two to Two",envir=.dico) -assign("txt_kruskal_wallis_test","Kruskal-Wallis Test",envir=.dico) -assign("txt_latent_variables_intercept","Intercept of latent variables [int.lv.free = FALSE]",envir=.dico) -assign("txt_observed_variables_intercept","Intercept of observed variables [int.ov.free = FALSE]",envir=.dico) -assign("txt_logistic_regressions","Logistical Regressions",envir=.dico) -assign("txt_mauchly_test_sphericity_covariance_matrix","Mauchly test testing the sphericite of the covariance matrix",envir=.dico) -assign("txt_none","none",envir=.dico) -assign("txt_non_param_analysis","Nonparametric analysis",envir=.dico) -assign("txt_normality_tests","Standardity test",envir=.dico) -assign("txt_pairwise_comparisons","Comparations 2 to 2",envir=.dico) -assign("txt_pairwise","pairwise",envir=.dico) -assign("txt_partial_corr_BP","Partial correction of Bravais-Pearson",envir=.dico) -assign("txt_preprocess_sort_select_operations","Pretreatments (tri, selection, mathematical operations, Missing value processing)",envir=.dico) -assign("txt_press_enter_to_continue","Press [enter] to continue",envir=.dico) -assign("txt_regressions","regressions",envir=.dico) -assign("txt_repeated_measures","Measures repeats",envir=.dico) -assign("txt_sample_size","size of sample",envir=.dico) -assign("txt_test_model","Test model",envir=.dico) -assign("txt_variables","variables",envir=.dico) -assign("txt_variable","variable",envir=.dico) -assign("txt_VIF","VIF",envir=.dico) -assign("desc_corr_group_analysis_spec","If you want to perform the analysis for different subsamples based on a categorical criterion (i.e.; perform a group analysis) \n choose yes. In this case, the analysis is done on the complete sample and on the subsamples. \n If you want the analysis for the complete sample only, choose no. The group analysis does not apply to robust statistics.",envir=.dico) -assign("desc_outliers_removal_implications","Delete all outliers removes all values beyond p(chi.two)< 0.001. Delete one observation at a time makes it possible to make a detailed analysis of each observation considered to be influential from the most extreme value. The procedure stops when no more observations are considered influential",envir=.dico) -assign("txt_bilateral","Bilateral",envir=.dico) -assign("desc_no_compatible_object_in_mem_for_aov","there is no object compatible with aov.plus in the memory of R. You must make an analysis of variance to the prerequisite",envir=.dico) -assign("desc_this_function_means_and_sd_adjusted_interaction_effect_possible","This function provides the adjusted averages and standard errors as well as the corresponding graph. With the post hoc choice on interactions, you can test the interaction effects 2 a 2 and the simple effects.",envir=.dico) -assign("txt_anova_plus","Anova plus",envir=.dico) -assign("desc_center_and_center_reduce_explaination","Center allows you to have a zero average by keeping the chart-type. Centrer reduce corresponds to the formula of z. The average is 0 and the standard scale is 1. The lower probability corresponds to the probability of having a lower or equal z. The higher probability corresponds to the probability of having a higher or equal z",envir=.dico) -assign("desc_proba_sum_is_not_one_or_not_enough_proba","The sum of probabilities is different from 1 or the number of probabilities does not correspond to the number of modes of the variable. Please enter a valid probability vector",envir=.dico) -assign("desc_if_non_fixed_sample_poisson_law","If the total number is not fixed, it is hypothesized that the observations occur according to a fish law. Distribution on the levels of a factor occurs with a fixed probability. Distribution is a fish distribution",envir=.dico) -assign("desc_distribution_is_joint_multinomial","The option *Fixed total effect* must be chosen if the null hypothesis is made that the distribution in each of the cells in the table is fixed. Distribution is a multinomial distribution attached",envir=.dico) -assign("desc_distribution_is_independant_multinomial","The fixed total number option for lines* must be chosen if the number of staff for each line is the same, as if you want to ensure a matching between groups. Distribution is an independent multinomial distribution",envir=.dico) -assign("desc_corr_detailed_analysis","the size analysis allows to have descriptive statistics, normalite tests, the cloud of points, \n robust statistics, all correlation coefficients. \n the correlation matrix allows to control the error of 1e species and is adapted for a large number of correlations \n the correlation comparison allows to compare 2 dependent or independent correlations \n The choice + other correlations + allows to have the tetrachoric and polychoric correlation",envir=.dico) -assign("desc_corr_values_must_be_between_min_1_and_1","The correlation values must be between -1 and 1/n and the numbers must be positive integers",envir=.dico) -assign("desc_you_can_choose_contrasts_you_want","You can choose the contrasts you want. Nevertheless, the rules concerning the application of contrasts must be respected. Contrasts can be specified manually. In this case, please select the contrasts",envir=.dico) -assign("desc_square_matrix_rectangular_matrix","A square matrix is a matrix with all Correlations 2 to 2. A rectangular matrix is a matrix in which a first set of variables is correlated with a second set of variables.",envir=.dico) -assign("desc_complete_dataset_vs_identification_outliers_vs_without_outliers","the complete data represent the classical analysis on all usable data, the identification of the influential values allows to identify the observations which are considered statistically to influence the results. data analysis without influential values performs analysis after removal of influential values. This option stores in the memory of R a new database of data without influential value in an object bearing the name *nettoyees*",envir=.dico) -assign("desc_welcome_in_easieR","Welcome in easieR - For more information, please visit:https://theeasierproject.wordpress.com/",envir=.dico) -assign("ask_variables_type_for_anova","Please specify the type(s) of variable(s) you want to include in the analysis.\nYou can choose several (e.g., for mixed annova or ancova)",envir=.dico) -assign("ask_correction_anova_contrasts","Correction?",envir=.dico) -assign("txt_independant_groups","Independent groups",envir=.dico) -assign("txt_covariables","Covariates",envir=.dico) -assign("txt_cfa_information_default","information [information = default]",envir=.dico) -assign("txt_cfa_continuity_correction_zero_keep_margins_default","correction of continuity [zero.keep.margins=default]",envir=.dico) -assign("txt_cfa_estimator_ml_default","estimator [estimator=ml]",envir=.dico) -assign("txt_cfa_groups_null_default","groups [group=NULL]",envir=.dico) -assign("txt_cfa_test_standard_default","test",envir=.dico) -assign("txt_cfa_standard_error_default","standard error",envir=.dico) -assign("txt_cfa_observed_variabes_standardization_true_default","standardization of observed variables",envir=.dico) -assign("txt_cfa_latent_variables_indicators_estimates_true_default","Estimation of indicators of latent variables [std.lv=FALSE]",envir=.dico) -assign("desc_wls_corresponds_to_adf_plus_explaination_other_estimators","[WLS] corresponds to [ADF]. Estimators with extensions [M],[MV],[MVSF],[R] are robust versions of classic estimators [MV],[WLS], [DWLS], [ULS]",envir=.dico) -assign("ask_observed_variables_intercept_zero","Intercept VO = '0'?",envir=.dico) -assign("ask_latent_variables_intercept_zero","Intercept VL = '0'?",envir=.dico) -assign("ask_how_to_treat_exaequo_rank","How do you want to treat ex-aequo? The method *warning* is the average between ex aequo (the most usual), *first* assigns the first ranking ex aequo to the first value in the data, *last* to the last, *min* assigns the minimum value to all ex aequo and *max* the maximum value.",envir=.dico) -assign("desc_for_ordinal_and_dicho_varible_prefer_min_res","For the ordinal and dichomic variables, choose the method of minimum residus - minres - or least weighting squares - wls. For continuous variables, the maximum likelihood if normalite is respected - ml",envir=.dico) -assign("desc_saturation_criterion_show_only_above_threshold","The saturation criterion allows the results table to show only saturation above the fixed threshold",envir=.dico) -assign("desc_to_find_new_analysis_search_in_english","To find a new analysis, it is necessary to do your search in English. You can use several words in the search. A html page containing all the packages referring to the search analysis will open.",envir=.dico) -assign("txt_division","division",envir=.dico) -assign("desc_if_you_select_both_operations_value_will_be_added_to_chose_cols","If you select both options at the same time, the specified value will be added to all the selected columns and then the selected columns will be added. To add a specific value to the total, select the column addition option only.",envir=.dico) -assign("desc_if_you_select_both_operations_value_will_be_multiplied_to_chose_cols","If you select both options at the same time, the specified value will be multiplied to all the selected columns and then the selected columns will be multiplied among them. To multiply a specific value in total, please select the column multipication option only.",envir=.dico) -assign("ask_chose_values_on_left_of_minus_symbol","Please select the values to the left of the symbol *minus*. If several variables are selected, the rules of the matrix calculation are applied.",envir=.dico) -assign("desc_one_or_same_number_cols_on_both_sides_only","There shall be only one column or the number of columns to the right of the symbol *less* shall be equal to the number of columns to the left of the symbol *less*",envir=.dico) -assign("ask_specify_exponant_value","Please specify the value of the exhibitor. NOTE: For roots, the exponent is the inverse value. For example, the square root is equal to 1/2, the cubic root 1/3...",envir=.dico) -assign("desc_expression_must_be_correct_example","The expression must be correct. You can use the variables name directly the operators are +,-,*,/,^,(,). A correct expression would be:",envir=.dico) -assign("ask_chose_relation_between_vars_regressions_log","Please choose the type(s) of relationships between variables. Additive effects take the form of y=X1+X2 while interaction effects take the form of Y=X1+X2+X1:X2",envir=.dico) -assign("ask_variables_order_for_max_likelihood","The order of entry of the variables is important for the calculation of the maximum likelihood. Please specify the order of entry of variables",envir=.dico) -assign("ask_integrate_probabilities_to_dataset","Do you want to integrate probabilities into your database?",envir=.dico) -assign("ask_specify_other_options_regressions","Do you want to specify other options? You can select several. The selection methods allow you to select the best model based on statistical criteria. Hierarchical models allow to compare several models. Cross validations make it possible to check if a model is not dependent on the data. This option is to be used with selection methods. The group analysis makes it possible to achieve the same regression for subgroups. Influence measurements are the other measures usually used to identify influential values.",envir=.dico) -assign("desc_possible_apply_multiple_selection_criterion","It is possible to apply several selection criteria simultaneously, involving or not several variables. Please specify the number of variables you want to apply one or more selection criteria. Please choose the variables on which you should apply a selection",envir=.dico) -assign("desc_skew_and_kurtosis_between_1_and_3","Type of skew and kurtosis, shall be between 1 and 3:TXT",envir=.dico) -assign("desc_with_two_equal_means_ratio_must_be_5_percent","With two equal averages, or almost equal, the error rate must be 5%. Gradually modify the gap between the scratch-types and see how the alpha error rate will be changed",envir=.dico) -assign("desc_bilateral_superior_inferior_test_t","Bilateral analysis tests the existence of a difference. Superior choice test if average is strictly superior \n The lower choice tests the existence of a strictly inferior difference",envir=.dico) -assign("txt_numeric_variables","Numeric variables",envir=.dico) -assign("txt_select_language","Choose language",envir=.dico) -assign("txt_dot_adjusted",".adjusted",envir=.dico) -assign("txt_bca_inferior_limit","Bca lim inf",envir=.dico) -assign("txt_bca_inferior_limit","Bca.lim.inf",envir=.dico) -assign("txt_bca_superior_limit"," Bca.lim.sup",envir=.dico) -assign("txt_bca_superior_limit","Bca lim sup",envir=.dico) -assign("txt_bca_superior_limit","Bca.lim.sup",envir=.dico) -assign("txt_centered_dot_reduced","centered.reduced",envir=.dico) -assign("txt_chi_dot_squared","chi.2",envir=.dico) -assign("txt_chi_dot_squared_model","chi.2.model",envir=.dico) -assign("txt_chi_dot_squared","chi.squared",envir=.dico) -assign("txt_chi_dot_squared","chi.two",envir=.dico) -assign("txt_chi_dot_squared_adjustment","chi.two adjustment",envir=.dico) -assign("txt_pairwise_comparison","pairwaise comparison",envir=.dico) -assign("txt_continuous","continuous",envir=.dico) -assign("txt_greenhouse_geisser_huynn_feldt_correction","Correction : Greenhouse-Geisser & Hyunh-Feldt",envir=.dico) -assign("txt_df","df",envir=.dico) -assign("txt_df1","df1",envir=.dico) -assign("txt_df_parenthesis_1","Df(1)",envir=.dico) -assign("txt_df2","df2",envir=.dico) -assign("txt_df_parenthesis_2","Df(2)",envir=.dico) -assign("txt_df_denom","df.denom",envir=.dico) -assign("txt_df_parenthesis_denom","Df (dnom)",envir=.dico) -assign("txt_df_effect","df.effet",envir=.dico) -assign("txt_df_num","df.num",envir=.dico) -assign("txt_df_parenthesis_num","Df (num)",envir=.dico) -assign("txt_df_predictor","df predictor",envir=.dico) -assign("txt_df_residual","df.resid",envir=.dico) -assign("txt_df_residuals","df.residuals",envir=.dico) -assign("txt_delta_r_squared","Delta R.two",envir=.dico) -assign("txt_error","Error",envir=.dico) -assign("txt_error_BP","Error.BP",envir=.dico) -assign("txt_error_spearman","Error.Spearman",envir=.dico) -assign("txt_error_dot_standard_short","error.st",envir=.dico) -assign("txt_error_dot_standard","error.standard",envir=.dico) -assign("txt_error_dot_standard","Error.standard",envir=.dico) -assign("txt_space","space",envir=.dico) -assign("txt_estimator","estimator",envir=.dico) -assign("txt_global_model_estimate","Global model estimation",envir=.dico) -assign("txt_hf_p_value","HF.p.value",envir=.dico) -assign("txt_ci_inferior","CI Inf",envir=.dico) -assign("txt_ci_inferior_limit","CI lim inf",envir=.dico) -assign("txt_ci_superior_limit","CI lim sup",envir=.dico) -assign("txt_ci_superior","CI Sup",envir=.dico) -assign("txt_large","large",envir=.dico) -assign("txt_large_half","large - 0.5",envir=.dico) -assign("txt_inferior_limit","lim.inf",envir=.dico) -assign("txt_ci_inferior_limit_dot","lim.inf.CI",envir=.dico) -assign("txt_ci_inferior_limit_dot","Lim.inf.CI",envir=.dico) -assign("txt_ci_superior_limit","lim.sup",envir=.dico) -assign("txt_ci_superior_limit_dot","lim.sup.CI",envir=.dico) -assign("txt_ci_superior_limit_dot","Lim.sup.CI",envir=.dico) -assign("txt_r_squared_matrix","matrix r.two",envir=.dico) -assign("txt_truncated_m","M.truncated",envir=.dico) -assign("txt_multiplied_by","multiplied.by",envir=.dico) -assign("txt_dot_cleaned",".cleaned",envir=.dico) -assign("txt_cleaned","cleaned",envir=.dico) -assign("txt_bootstrap_dot_number","Number.bootstraps",envir=.dico) -assign("txt_odd_ratio_dot","Odd.ratio",envir=.dico) -assign("desc_install_bad_packages","Package.mal.installes",envir=.dico) -assign("desc_install_correct_packages","packages.installes.correctement",envir=.dico) -assign("txt_critical_p_corrected","crit.p.corrected",envir=.dico) -assign("txt_percentile_inferior_limit_dot","Percentile.lim.inf",envir=.dico) -assign("txt_percentile_superior_limit_dot","Percentile.lim.sup",envir=.dico) -assign("txt_percentage_removed_obs","Percentage.obs.removed",envir=.dico) -assign("txt_percent_removed_obs","Percent.obs.removed",envir=.dico) -assign("txt_r_dot_square","r.square",envir=.dico) -assign("txt_r_square","R square",envir=.dico) -assign("txt_r_dot_square","R.square",envir=.dico) -assign("txt_r_dot_two","r.two",envir=.dico) -assign("txt_r_dot_two","R.two",envir=.dico) -assign("txt_r_dot_two_adjusted","R.two.aj",envir=.dico) -assign("txt_log_regression_dot","Regressions.logistic",envir=.dico) -assign("txt_multiple_regressions_dot","regressions.multiples",envir=.dico) -assign("txt_multiple_regressions_dot","Regressions.multiples",envir=.dico) -assign("txt_rho_dot_square","rho.two",envir=.dico) -assign("txt_critical_dot_threshold","critic.threshold",envir=.dico) -assign("txt_critical_dot_threshold","Critic.threshold",envir=.dico) -assign("txt_spearman_df","Spearman.df",envir=.dico) -assign("txt_specificity","specifity",envir=.dico) -assign("txt_ultrawide","ultra wide",envir=.dico) -assign("txt_ultrawide","ultrawide",envir=.dico) -assign("txt_ultrawide_val","ultra wide - 0.707",envir=.dico) -assign("txt_absolute_dot_val","value.absolute.",envir=.dico) -assign("txt_contrast_dot_val","Value.contrast",envir=.dico) -assign("txt_critical_dot_val","Value.critical",envir=.dico) -assign("txt_p_dot_val","p.value",envir=.dico) -assign("txt_p_dot_val_lilliefors","p.value Llfrs",envir=.dico) -assign("txt_p_dot_val_sw","p.value SW",envir=.dico) -assign("txt_test_dot_val","test.value",envir=.dico) -assign("txt_z_dot_val","Z.value",envir=.dico) -assign("txt_value","value",envir=.dico) -assign("txt_vector_length_zero","vector of length zero",envir=.dico) -assign("txt_kendall_w","Kendall.W",envir=.dico) -assign("txt_synthesis","Synthesis",envir=.dico) -assign("txt_truncated_mean_0_2","Test on truncated mean 0.2",envir=.dico) -assign("txt_cramer_v_square","V.square",envir=.dico) -assign("txt_effect_size_dot","Effect.size",envir=.dico) -assign("txt_gg_p_value","GG.p.value",envir=.dico) -assign("txt_var_explained_dot","Var.explained",envir=.dico) -assign("V.sq","V.squared", envir=.dico) -} + .dico <<- new.env(parent=emptyenv()) + assign("ask_2x2_table" , "Table 2x2?" , envir=.dico) + assign("ask_2x2_table_value" , "Please specify the value for tables 2x2" , envir=.dico) + assign("ask_add_a_value_to_empty_cells" , "Does an empty cell value for polychoric correlations need to be added? To specify the values, choose TRUE, otherwise choose [default]" , envir=.dico) + assign("ask_add_value_to_total" , "do you still want to add a total value?" , envir=.dico) + assign("ask_analysis_by_group" , "Group analysis?" , envir=.dico) + assign("ask_analysis_on_complete_data_or_remove_outliers" , "Will you require analysis on the complete data or on the data for which the influential values have been removed?" , envir=.dico) + assign("ask_analysis_type" , "What analysis do you want to make?" , envir=.dico) + assign("ask_are_frequences_free_parameters" , "is the frequency of the different group a free parameter? " , envir=.dico) + assign("ask_are_there_inversed_items" , "Is there any inverse items?" , envir=.dico) + assign("ask_are_you_ready" , "are you ready?" , envir=.dico) + assign("ask_baseline" , "What is the baseline?" , envir=.dico) + assign("ask_bigger_tables_value" , "Please specify the value for tables larger than 2x2" , envir=.dico) + assign("ask_bootstrap_number_min_500" , "Please specify the number of bootstrap. A minimum of 500 is ideally required. Can take time for N>1000" , envir=.dico) + assign("ask_bootstrap_numbers_1_for_none" , "Please specify the number of bootstrap. To not have bootstrap, choose 1" , envir=.dico) + assign("ask_bootstraps_number" , "Number of bootstraps?" , envir=.dico) + assign("ask_cancel_entered_value_not_num" , "the value you entered is not numerical. Do you want to cancel this analysis?" , envir=.dico) + assign("ask_cauchy_apriori_distribution" , "Please specify the prior distribution of Cauchy" , envir=.dico) + assign("ask_center" , "Center?" , envir=.dico) + assign("ask_center_numeric_variables" , "Do you want to focus the numerical variables? Centrer is generally advised (e.g., Schielzeth, 2010). " , envir=.dico) + assign("ask_chi_squared_type" , "Please specify the type of chi tile you want to achieve." , envir=.dico) + assign("ask_choose_a_variable_with_at_least_two_modalities" , "A categorical variable must have at least 2 different modes. Please choose a variable with at least two modes" , envir=.dico) + assign("ask_chose_analysis" , "Please choose the analysis you want to perform." , envir=.dico) + assign("ask_chose_categorial_ranking_factor" , "Please select the categorical classification factor." , envir=.dico) + assign("ask_chose_cols_corresponding_to_repeated_measures" , "Please choose all the columns corresponding to the modalites of the variables in repetees measures" , envir=.dico) + assign("ask_chose_covariables" , "Please choose the covariates" , envir=.dico) + assign("ask_chose_database" , "Please select the database" , envir=.dico) + assign("ask_chose_defining_groups" , "Please select the group definition" , envir=.dico) + assign("ask_chose_dependant_variable" , "Please select the dependent variable. " , envir=.dico) + assign("ask_chose_first_judge" , "Please select the first judge" , envir=.dico) + assign("ask_chose_independant_group_variables" , "Please choose the variables-s with independent groups" , envir=.dico) + assign("ask_chose_interaction_model_predictors" , "Please choose the predictors to enter into the interaction model. It is necessary to have at least two variables" , envir=.dico) + assign("ask_chose_manifest_variables_at_least_three" , "Please select the obvious variables you want to analyze. You must choose at least 3 variables" , envir=.dico) + assign("ask_chose_ranking_categorial_factor" , "Please select the categorical classification factor." , envir=.dico) + assign("ask_chose_rotation" , "Please choose the type of rotation. Objection is adapted in the humanities" , envir=.dico) + assign("ask_chose_sample_variables" , "Please select the variable(s) defining the workforce" , envir=.dico) + assign("ask_chose_second_judge" , "Look for the second judge" , envir=.dico) + assign("ask_chose_selection_method" , "Please choose the selection method you wish to use" , envir=.dico) + assign("ask_chose_the_working_dir" , "Please select work directory" , envir=.dico) + assign("ask_chose_variables_at_least_five" , "Please select the variables you want to analyze. You must choose at least 5 variables" , envir=.dico) + assign("ask_chose_variables_at_least_three" , "Please select the variables you want to analyze. You must choose at least 3 variables" , envir=.dico) + assign("ask_chose_variable" , "Please choose the variables you want to analyze." , envir=.dico) + assign("ask_chose_variable_x_axis" , "Please select the abscess variable" , envir=.dico) + assign("ask_chose_variable_y_axis" , "Please select the variable ordered" , envir=.dico) + assign("ask_coding_criterion" , "What coding criteria do you want?" , envir=.dico) + assign("ask_col_separation_index" , "When saving your file, what is the column separation index?" , envir=.dico) + assign("ask_complete_or_outliers" , "Do you want to perform analyses on complete data or on data without influential values?" , envir=.dico) + assign("ask_constant_parameters" , "Consistent parameters?" , envir=.dico) + assign("ask_continue" , "Continue?" , envir=.dico) + assign("ask_contrast_must_respect_ortho" , "Contrasts must respect orthogonalite. Do you want to continue?" , envir=.dico) + assign("ask_control_variables" , "Please specify the variable(s) to control" , envir=.dico) + assign("ask_convert_dependant_variable_to_dichotomic" , "do you want to convert the dependent variable into a dichotomous variable?" , envir=.dico) + assign("ask_correction_desired" , "Please specify the type of probability correction you want to achieve" , envir=.dico) + assign("ask_correction_type" , "Type of correction?" , envir=.dico) + assign("ask_correlated_or_orthogonal_factors" , "Is the factors correlated (FALSE) or are they orthogonal (TRUE)?" , envir=.dico) + assign("ask_correlation_matrix_could_not_be_computed" , "The correlation matrix could not be achieved. Do you want to try again?" , envir=.dico) + assign("ask_correlation_type" , "Please choose the type of correlations you want to make. For dichotomous variables, correlations will be tetrachoric correlations" , envir=.dico) + assign("ask_corr_or_partial_correlations" , "Partial corrections or correlations?" , envir=.dico) + assign("ask_could_not_converge_model_verify_correlation_matrix" , "We did not succeed in making the model converge. Please check your correlation matrix and try again with other parameters" , envir=.dico) + assign("ask_could_not_finish_analysis_respecify_parameters" , "We were unable to complete the analysis correctly. Please try to respecify the parameters" , envir=.dico) + assign("ask_covariables" , "Covariable-s?" , envir=.dico) + assign("ask_criterion_for_dichotomy" , "Please specify the criteria on which you want to dichotomize your variable. You can use the mediane or choose a specific threshold. " , envir=.dico) + assign("ask_criterion_for_obs_to_keep" , "Please specify the criteria for any comments you wish to keep/guard." , envir=.dico) + assign("ask_criterion_for_variable" , "What criteria do you want to use for the variable" , envir=.dico) + assign("ask_data" , "DonnĂ©es?" , envir=.dico) + assign("ask_data_format" , "What is the format of your data?" , envir=.dico) + assign("ask_decimal_symbol" , "If some data contain decimals, what is the symbol indicating the decimal?" , envir=.dico) + assign("ask_denominator_variable_or_value" , "Is the denominator a variable or a value? " , envir=.dico) + assign("ask_denominator_variable" , "Please select the variable to the denominator " , envir=.dico) + assign("ask_dependant_variable_with_less_than_three_val_verify_dataset" , "The dependent variable has less than three different values. Check your data or the analysis you are trying to do is not relevant." , envir=.dico) + assign("ask_did_not_specify_nb_factors_repeated_measure_exit" , "You haven't specified the number of factors you can repetee, do you want to quit?" , envir=.dico) + assign("ask_distribution" , "Distribution?" , envir=.dico) + assign("ask_distribution_type" , "What distribution do you want?" , envir=.dico) + assign("ask_empty_cells" , "Old Cells?" , envir=.dico) + assign("ask_enter_different_values" , "Please enter different values" , envir=.dico) + assign("ask_enter_number_of_to_be_removed_variable" , "You must enter the number to know which observation should be deleted." , envir=.dico) + assign("ask_exit_because_of_alpha_on_non_matrix" , "You are trying to do an alpha on something other than a matrix. Do you want to leave this analysis?" , envir=.dico) + assign("ask_exit_no_lower_bound_specified" , "You have not specified the lower limit. Do you want to leave the selection?" , envir=.dico) + assign("ask_exit_no_upper_bound_specified" , "You have not specified the upper limit. Do you want to leave the selection?" , envir=.dico) + assign("ask_exportation_filename" , "What name do you want to assign to the file?" , envir=.dico) + assign("ask_factorial_scores" , "factorial scores?" , envir=.dico) + assign("ask_factors_number_for_hierarchical_structure" , "Please specify the number of factors in the hierarchical structure. " , envir=.dico) + assign("ask_factors_ortho" , "Orthogonalitis of factors?" , envir=.dico) + assign("ask_factors_superior_level" , "Number of factors of the higher level?" , envir=.dico) + assign("ask_family" , "Please specify the family (i.e. form of distribution). " , envir=.dico) + assign("ask_file_format" , "File format?" , envir=.dico) + assign("ask_file_format_to_import" , "What format is your file saved?" , envir=.dico) + assign("ask_first_categorical_set" , "Please select the first categorical factor(s) set" , envir=.dico) + assign("ask_first_variables_set" , "Please select the first set of variables" , envir=.dico) + assign("ask_fixed_covariables" , "fixed covariates?" , envir=.dico) + assign("ask_freq_constance" , "Constance of Frequence?" , envir=.dico) + assign("ask_f_value" , "What value do you want to use?" , envir=.dico) + assign("ask_group_variable" , "Variable [groups]?" , envir=.dico) + assign("ask_headers_in_database" , "Is the name of the variables on the first line of your database? Choose TRUE if so" , envir=.dico) + assign("ask_hierarchical_analysis" , "Does it have to make a hierarchical analysis?" , envir=.dico) + assign("ask_how_many_modalities" , "How many modes" , envir=.dico) + assign("ask_how_standard_error_must_be_estimated" , "How should the standard error be estimated?" , envir=.dico) + assign("ask_how_to_remove" , "How do you want to delete them?" , envir=.dico) + assign("ask_how_to_treat_missing_values" , "Missing values were detected. How do you want to treat them? Keeping all observations can bias the results. " , envir=.dico) + assign("ask_id_variable" , "Please select the variable identifying participants" , envir=.dico) + assign("ask_imitate" , "Imitate?" , envir=.dico) + assign("ask_independant_variable" , "Please select the independent variable. " , envir=.dico) + assign("ask_information_matrix" , "Information Matrix?" , envir=.dico) + assign("ask_integrate_factorial_scores_in_data" , "Do you want factorial scores to be integrated with your data?" , envir=.dico) + assign("ask_inversed_items" , "inverse items?" , envir=.dico) + assign("ask_is_model_correct" , "Is your model correct?" , envir=.dico) + assign("ask_latent_variables_number" , "Please specify the number of latent variables" , envir=.dico) + assign("ask_level" , "Please select level" , envir=.dico) + assign("ask_likelihood" , "Treasure?" , envir=.dico) + assign("ask_linebase_modalities" , "Please specify the mode(s) that will be used for the base line (e.g. 0). The other modes will be grouped in category 1." , envir=.dico) + assign("ask_log_base" , "Please specify the base of the logarithm.To get e, type e" , envir=.dico) + assign("ask_lower_bound" , "Inferior limit?" , envir=.dico) + assign("ask_mcnemar_repeated_measure" , "McNemar test: modalites are not the same for the McNemar test. Is this a factor that is able to repeat?" , envir=.dico) + assign("ask_mediation_type" , "What kind of mediation?" , envir=.dico) + assign("ask_mediator" , "please choose the mediator" , envir=.dico) + assign("ask_minus_left_hand_variables" , "Please select the variable(s) on the left of the symbol *minus*" , envir=.dico) + assign("ask_minus_right_hand_variables" , "Please select the variable(s) on the right of the symbol *minus*." , envir=.dico) + assign("ask_minus_right_operand_variable_or_value" , "Are the values on the right of the symbol *minus* a variable(s) or a value? " , envir=.dico) + assign("ask_missing_values_detected_what_to_do" , "Missing values have been detected. How do you want to treat them?" , envir=.dico) + assign("ask_missing_values_treatment" , "Treatment of missing values?" , envir=.dico) + assign("ask_missing_values_value_na_on_empty" , "If some data is missing, how are they defined? You can leave NA if the cells are empty." , envir=.dico) + assign("ask_missing_value_treatment" , "Number of missing values per variable. How do you want to treat them?" , envir=.dico) + assign("ask_modalities_for_variable" , "What modes do you want to select for the variable" , envir=.dico) + assign("ask_modalities_to_keep" , "Please select the modes you want to keep." , envir=.dico) + assign("ask_name_for_dataset" , "What name do you want to give the data?" , envir=.dico) + assign("ask_name_to_attribute_to" , "What name do you want to assign to" , envir=.dico) + assign("ask_nb_factors_repeated_measure" , "How many factors can be rehearsed?" , envir=.dico) + assign("ask_new_variable_name" , "What name do you want to assign to the new variable? " , envir=.dico) + assign("ask_norm_value" , "What is the standard value?" , envir=.dico) + assign("ask_not_enough_obs_verify_dataset" , "There are not enough observations to make the analysis. Please check your net data to ensure that there are at least three observations per mode of each factor" , envir=.dico) + assign("ask_null_hypothesis_tests_or_bayesian_factors" , "Do you want null hypothesis tests or/and Bayesian factors?" , envir=.dico) + assign("ask_numerator_variable_or_value" , "Is the numerator a variable or a value? " , envir=.dico) + assign("ask_numerator_variable" , "Please select the variable at the numerator " , envir=.dico) + assign("ask_obs_to_remove" , "What observation do you want to remove from analyses? 0=none" , envir=.dico) + assign("ask_other_options" , "Other options?" , envir=.dico) + assign("ask_ponderate_analysis_by_a_sample_var" , "Does the analysis need to be weighted by an effective variable?" , envir=.dico) + assign("ask_positive_val_variable_or_value" , "Are the positive values a variable(s) or a value? " , envir=.dico) + assign("ask_predictor" , "please specify the predictor" , envir=.dico) + assign("ask_press_enter_to_continue" , "Support [enter] to continue" , envir=.dico) + assign("ask_probabilities_for_modalities" , "Please enter the probabilities corresponding to each mode of the variable. " , envir=.dico) + assign("ask_probabilities" , "Probability?" , envir=.dico) + assign("ask_probability_value" , "What value of probability do you want to use?" , envir=.dico) + assign("ask_redefine_analysis_because_modalities_product_is_superior_to_obs" , "The product of the modalites of the variables defining the groups is superior to your observations. You need at least one observation by combination of modes of your variables. Please redefine your analysis" , envir=.dico) + assign("ask_regroup_modalities" , "Do you want to group between the modes?" , envir=.dico) + assign("ask_rename_variables_with_special_char" , "Some variable names contain special characters that can create bugs. Do you want to rename these variables?" , envir=.dico) + assign("ask_results_desired" , "What results do you want?" , envir=.dico) + assign("ask_results_output" , "Outcomes?" , envir=.dico) + assign("ask_sampling_type" , "What type of sampling have you done for your analysis?" , envir=.dico) + assign("ask_save_results_in_external_file" , "Do you want to save results to an external file?" , envir=.dico) + assign("ask_second_categorical_set" , "Please select the second categorical factor(s) set" , envir=.dico) + assign("ask_second_mediator" , "Please specify the second mediator. " , envir=.dico) + assign("ask_second_variables_set" , "Please select the second set of variables" , envir=.dico) + assign("ask_selection_method" , "What method should be used for the selection method?" , envir=.dico) + assign("ask_select_variables_or_modalities_of_repeated_measure_variable" , "Please select the variables OR modalites of the variables a measure(s). " , envir=.dico) + assign("ask_separation_value" , "Please specify the separation value" , envir=.dico) + assign("ask_shorten_long_variables_names" , "Some variables have particularly long names that can generate playback. Do you want to shorten them?" , envir=.dico) + assign("ask_should_intercept_of_latent_variable_be_fixed_to_zero" , "Does the intercept of latent variables have to be fixed to 0?" , envir=.dico) + assign("ask_should_intercept_of_obs_variables_be_fixed_to_zero" , "Does the intercept of observed variables have to be fixed to 0?" , envir=.dico) + assign("ask_simple_or_partial_corr" , "Single or partial corrections?" , envir=.dico) + assign("ask_specify_all_parameters_or_imitate_specific_software" , "Do you want to specify all the parameters [default] or imitate any particular software?" , envir=.dico) + assign("ask_specify_datasheet_to_import" , "Please specify the worksheet you want to import" , envir=.dico) + assign("ask_specify_groups" , "Specify groups?" , envir=.dico) + assign("ask_specify_inverted_item" , "Please specify the inverse items" , envir=.dico) + assign("ask_specify_likelihood" , "Please specify the likelihood. " , envir=.dico) + assign("ask_specify_norm_value" , "Please specify the value of the standard" , envir=.dico) + assign("ask_specify_other_options" , "Specify other options?" , envir=.dico) + assign("ask_specify_sample" , "Specify actual?" , envir=.dico) + assign("ask_specify_sample_variable" , "Specify the true number?" , envir=.dico) + assign("ask_specify_variables_for_ranks" , "Please specify the variables you wish to make the rows of" , envir=.dico) + assign("ask_specify_variables_type" , "Please specify the type(s) of variable(s) you wish to include in the analysis.nYou can choose several (e.g., for mixed anova or ancova)" , envir=.dico) + assign("ask_standard_error" , "Standard Error?" , envir=.dico) + assign("ask_standardization" , "Standardization?" , envir=.dico) + assign("ask_standardization_vl" , "Standardization VL?" , envir=.dico) + assign("ask_standardize_obs_variables_before" , "Does it standardize (i.e. centrer reduce) the variables observed at prelable (TRUE) or not (FALSE)?" , envir=.dico) + assign("ask_statistical_approach" , "Statistical approach?" , envir=.dico) + assign("ask_subgroups" , "You can compose descriptive statistics by sub-group by choosing one or more categorical variables. Do you want to specify the subgroups?" , envir=.dico) + assign("ask_sufficient_matrix_for_afe" , "Is the matrix satisfactory for an EFA?" , envir=.dico) + assign("ask_suppress_this_obs" , "Do you want to delete this observation?" , envir=.dico) + assign("ask_test_hierarchical_structure" , " Do you want to test a hierarchical structure? The omega tests a hierarchical structure and a hierarchical AFE will be realized." , envir=.dico) + assign("ask_time1" , "Please choose time 1." , envir=.dico) + assign("ask_time2" , "Please choose time 2." , envir=.dico) + assign("ask_transform_numerical_to_categorial_variables" , "You must use categorical variables. Do you want to turn numerical variables into categorical variables?" , envir=.dico) + assign("ask_troncature_threshold" , "Please set the threshold of the trunk" , envir=.dico) + assign("ask_t_test_type" , "Please specify the type of test t you want to perform." , envir=.dico) + assign("ask_type_correlation" , "Please specify the type of correlation you want to achieve." , envir=.dico) + assign("ask_upper_bound" , "High light?" , envir=.dico) + assign("ask_value_for_missing_values" , "By what value are the missing values defined?" , envir=.dico) + assign("ask_value_for_operation" , "Please specify the value for your mathematical operation. " , envir=.dico) + assign("ask_value_for_selected_obs" , "Please specify the value on which observations should be selected. " , envir=.dico) + assign("ask_value" , "Get value?" , envir=.dico) + assign("ask_variabels_for_polyc_tetra_mixt_corr" , "Please select the variables for which polychoric/tetrachoric/mixte correlations should be made." , envir=.dico) + assign("ask_variable_at_this_point" , "What variable has this etape" , envir=.dico) + assign("ask_variable_name" , "Name of new variable?" , envir=.dico) + assign("ask_variables_for_description_statistics" , "Please choose the variables for which you wish to obtain descriptive statistics" , envir=.dico) + assign("ask_variables_groups" , "Variable groups?" , envir=.dico) + assign("ask_variables_names" , "Name of variables?" , envir=.dico) + assign("ask_variables_to_abs" , "Please select the variables to make the absolute value " , envir=.dico) + assign("ask_variables_to_add" , "Please select the variables to add." , envir=.dico) + assign("ask_variables_to_exp" , "Please select the variables to which the exponent applies" , envir=.dico) + assign("ask_variables_to_log" , "Please select the variables for which logarithm is required" , envir=.dico) + assign("ask_variables_to_mean" , "Please select the variables to average " , envir=.dico) + assign("ask_variables_to_multiply" , "Please select the variables to multiply. " , envir=.dico) + assign("ask_variables_to_order" , "Please select the variable(s) to sort" , envir=.dico) + assign("ask_variables_type_correlations" , "Please specify the type of variables. Tetra/polychoric correlations will be made on dichotomous/ordinal variables and Bravais-Pearson on continuous variables" , envir=.dico) + assign("ask_variables_types_correlations" , "Please specify the type of variables. Tetra/polychoric correlations will be made on the ordinal and Bravai-Pearson variables on the continuous" , envir=.dico) + assign("ask_variables_used_for_exponential" , "Please select the variables used in the exhibition " , envir=.dico) + assign("ask_variables_used_for_groups" , "Please select the variable(s) defining groups" , envir=.dico) + assign("ask_variable" , "Variable to analyze?" , envir=.dico) + assign("ask_wanted_model" , "Please choose the model you want to analyze with aov.plus" , envir=.dico) + assign("ask_what_do_you_want" , "What do you want?" , envir=.dico) + assign("ask_what_is_your_choice" , "What is your choice?" , envir=.dico) + assign("ask_what_to_print" , "What do you want to show?" , envir=.dico) + assign("ask_which_algorithm" , "What algorithm will you want?" , envir=.dico) + assign("ask_which_analysis_you_looking_for" , "What analysis are you looking for?" , envir=.dico) + assign("ask_which_baseline" , "What is the baseline?" , envir=.dico) + assign("ask_which_constant_parameters" , "What parameters do you want to maintain constant?" , envir=.dico) + assign("ask_which_contrasts_for_variable" , "What contrasts for the variable" , envir=.dico) + assign("ask_which_contrasts" , "What kind of contrast do you want?" , envir=.dico) + assign("ask_which_correction" , "What correction of probability do you want to apply? To not apply a correction, choose +none+" , envir=.dico) + assign("ask_which_data_to_analyse" , "What data do you want to analyze?" , envir=.dico) + assign("ask_which_data_to_export" , "What data do you want to export?" , envir=.dico) + assign("ask_which_estimator" , "What estimator?" , envir=.dico) + assign("ask_which_factors_combination_for_adjust_means" , "What combination of factors do you want to show the adjusted averages?" , envir=.dico) + assign("ask_which_information_matrix_for_standard_error_estimation" , "On which information matrix should the estimation of standard errors be achieved?" , envir=.dico) + assign("ask_which_mathematical_operation" , "Please choose the mathematical operation you wish to achieve" , envir=.dico) + assign("ask_which_operation" , "What operation do you want?" , envir=.dico) + assign("ask_which_options" , "What options?" , envir=.dico) + assign("ask_which_options_to_specify" , "What options do you want to specify?" , envir=.dico) + assign("ask_which_output" , "What format do you want?" , envir=.dico) + assign("ask_which_output_results" , "What results do you want?" , envir=.dico) + assign("ask_which_regression_type" , "What type of regression?" , envir=.dico) + assign("ask_which_results_warning_on_default_output" , "What results do you want? Warning: exits by default cannot be saved. If you want a saver, choose the detail" , envir=.dico) + assign("ask_which_rotation" , "What rotation" , envir=.dico) + assign("ask_which_saturation_criterion" , "What is the saturation criteria you want to use?" , envir=.dico) + assign("ask_which_size_effect" , "What effect size do you want?" , envir=.dico) + assign("ask_which_squared_sum" , "What sum of squares do you want to use?" , envir=.dico) + assign("ask_which_test" , "What test do you want to use?" , envir=.dico) + assign("ask_which_value_for_operation" , "What value do you want for your mathematical operation?" , envir=.dico) + assign("ask_which_variable_identifies_participants" , "What is the variable identifying participants?" , envir=.dico) + assign("ask_you_did_not_chose_a_variable_continue_or_abort" , "You have not chosen a variable. Do you want to continue (ok) or give up (cancel) this analysis?" , envir=.dico) + assign("desc_abs_val_applied_to_var" , "the absolute value has been applied to the variable" , envir=.dico) + assign("desc_accepted_values_are_z_and_grubbs" , "The values accepted for criteria are z and Grubbs " , envir=.dico) + assign("desc_all_tests_description" , "The parametric model returns the classic anova, the nonparametric calculates the Kruskal Wallis test nsi it is a model with independent groups, or a Friedman anova for a model in Measurements repetees.nThe Bayesian model is the equivalent of the model test in the anova by adopting a Bayesian approach,n the robust statistics are anovas on medianes or the truncated averages with or without bootstrap." , envir=.dico) + assign("desc_alpha_increased_with_value_equals_to" , "you multiply the error of 1e espece. The risk of making an error of 1st species is" , envir=.dico) + assign("desc_analysis_aborted" , "The analysis could not be completed" , envir=.dico) + assign("desc_and" , "and" , envir=.dico) + assign("desc_and_variabe" , "and variable" , envir=.dico) + assign("desc_and_variable_y" , " and variable " , envir=.dico) + assign("desc_applied_correction_is" , "the correction applied is the correction of" , envir=.dico) + assign("desc_at_least_10_obs_needed" , "It takes at least 10 observations plus the number of variables to make the analysis. Check your data." , envir=.dico) + assign("desc_at_least_independant_variables_or_repeated_measures" , "It is essential to have at least variables with independent groups or in repete measures" , envir=.dico) + assign("desc_at_least_on_contrast_matrix_incorrect" , "At least one of your contrast matrices is not correct." , envir=.dico) + assign("desc_at_least_one_denom_is_zero" , "At least one of the values in the denominator is a 0. The value returned in this case is infinite - inf" , envir=.dico) + assign("desc_at_least_one_non_numeric" , "at least one variable is not digital" , envir=.dico) + assign("desc_at_least_one_var_is_not_num" , "at least one of the variables is not numerical" , envir=.dico) + assign("desc_authorized_values_for_contrasts" , "The permitted values for contrasts are +none+ for no contrast, +pairwise+ for comparisons 2 to 2 or a list of contrast coefficients" , envir=.dico) + assign("desc_avoid_spaces_and_punctuations" , "Avoid spaces and punctuation signs, except . and _ " , envir=.dico) + assign("desc_bayesian_factors_could_not_be_computed" , "Bayesian factors could not be calculated. " , envir=.dico) + assign("desc_beyond_with_lower_and_upper" , "au-dela (with a lower and higher limit)" , envir=.dico) + assign("desc_biased_results_risk_because_of_low_number_of_obs_or_zero_variance" , "there are less than 3 observations for one of the groups or nthe variance of at least one group is 0. Results are likely to be significantly biased" , envir=.dico) + assign("desc_bootstraps_number_must_be_positive" , "The number of bootstrap must be a positive integer" , envir=.dico) + assign("desc_bootstrap_t_adapt_to_truncated_mean" , "The bootstrap-t method is a bootstrap adapted to the calculation of the truncate mean" , envir=.dico) + assign("desc_cannot_compute_mahalanobis" , "Desole, we cannot calculate the distance of Mahalanobis on your data. The analyses will be carried out on the complete data" , envir=.dico) + assign("desc_cannot_group_variables_because_not_described" , "You cannot have a variable *groups* since all variables must be descripted" , envir=.dico) + assign("desc_cannot_have_both_within_RML_arguments" , "You cannot have both arguments in within and RML" , envir=.dico) + assign("desc_cells_for_mcnemar" , "The cells used to calculate the McNemar are those of the 1st row 2nd column and the 2nd row 1st column" , envir=.dico) + assign("desc_centered_data_schielzeth_recommandations" , "In accordance with the recommendations of Schielzeth 2010, the data were pre-centered" , envir=.dico) + assign("desc_chi_squared_adjustment_on_variable_x" , "chi two adjustment on variable" , envir=.dico) + assign("desc_close_browser_to_come_back" , "Do not forget to close the htmlt window (firexfox, chrome, internet explorer...) to return to the R session" , envir=.dico) + assign("desc_cross_validation_is_not_yet_supported" , "Cross validation is not yet available. " , envir=.dico) + assign("desc_data_saved_in" , "data are saved in" , envir=.dico) + assign("desc_data_succesfully_ordered" , "data have been sorted correctly " , envir=.dico) + assign("desc_descriptive_statistics_on" , "Descriptive statistics on" , envir=.dico) + assign("desc_distribution_is_hypergeometric_when" , "The option *Total fixed effect for rows and columns* when the totals for rows and columns are fixed. The distribution is hypergeometric" , envir=.dico) + assign("desc_each_participant_must_appear_only_once_" , "Each participant must appear once and only once for each combination of the modes" , envir=.dico) + assign("desc_effect_size_by_walker" , "The effect size is calculated from the formula proposed by Walker, 2003" , envir=.dico) + assign("desc_entered_value_not_num" , "value entered is not numerical" , envir=.dico) + assign("desc_exponential_has_been_applied_to_var" , "exponential has been applied to the variable" , envir=.dico) + assign("desc_facotrs_must_be_positive_int_inferior_to_variables_num" , "The number of factors must be a positive integer less than the number of variables" , envir=.dico) + assign("desc_fb_ratio_between_models" , "FB ratio between models" , envir=.dico) + assign("desc_file_is_saved_in" , "file is saved in" , envir=.dico) + assign("desc_flattening_and_asymetry_configurable" , "You can specify truncation and parameters for flattening and asymetry by choosing other options" , envir=.dico) + assign("desc_for_bigger_samples_bootstrap_t_prefered" , "For larger samples, boostrap using method t should be preferred." , envir=.dico) + assign("desc_for_easier_to_work" , "In order for easieR to work properly, Pandoc must be installed at the following URL: https://github.com/jgm/pandoc/releases" , envir=.dico) + assign("desc_graph_thickness_gives_density" , "The thickness of the graph gives the density, allowing to better define the distribution. " , envir=.dico) + assign("desc_has_been_added_to" , "was added to" , envir=.dico) + assign("desc_has_been_added_to_variable" , "is added to variable" , envir=.dico) + assign("desc_has_been_applied_to_variable" , " has been applied to the variable" , envir=.dico) + assign("desc_has_been_put_to_the_power_of" , " has been elevated to power" , envir=.dico) + assign("desc_has_multiplied_variables" , "a multiplies the -les-variables" , envir=.dico) + assign("desc_highest_value" , "Highest Value" , envir=.dico) + assign("desc_how_to_cite_easier" , "To cite easieR in your publication / to quote easieR in you publications use:n Stefaniak, N. (2020). " , envir=.dico) + assign("desc_identical_option_total_sample" , "The total fixed staffing option for columns* is identical to the previous one for columns" , envir=.dico) + assign("desc_identified_outliers" , "Observations considered influential" , envir=.dico) + assign("desc_if_true_covariates_as_fixed" , "If true, exogenous covaria are considered fixed, otherwise they are considered to be aleatory and their parameters are free" , envir=.dico) + assign("desc_if_true_latent_residuals_one" , "If true, the residuals of latent variables are fixed to 1, otherwise the parameters of the latent variable are estimated by setting the first indicator to 1" , envir=.dico) + assign("desc_improve_likelihood_for_each_variable" , "Improving likelihood for each variable" , envir=.dico) + assign("desc_incorrect_model" , "The specified model is incorrect. Check your variables and model" , envir=.dico) + assign("desc_instable_model_high_multicolinearity" , "Multicolinearite is too important. The model is unstable" , envir=.dico) + assign("desc_insufficient_obs" , "The number of observations is insufficient to complete the analyses for this group" , envir=.dico) + assign("desc_insufficient_sample_for_combinations_between" , "The number of combinations between the variable is insufficient " , envir=.dico) + assign("desc_in_that_case_non_parametric_is_classical_chi_squared" , "In this case, the nonparametric test is the classic chi square test" , envir=.dico) + assign("desc_issue_in_hierarchical_regression" , "A problem has been identified in the stages of your hierarchical regression" , envir=.dico) + assign("desc_kmo_could_not_be_computed_verify_matrix" , "The KMO could not be calculated. Check your correlation matrix." , envir=.dico) + assign("desc_kmo_must_strictly_be_more_than_a_half" , "the KMO must be absolutely superior to 0.5" , envir=.dico) + assign("desc_kmo_on_matrix_could_not_be_obtained" , "The KMO on the matrix could not be obtained." , envir=.dico) + assign("desc_kmo_on_matrix_could_not_be_obtained_trying" , "The KMO on the matrix could not be obtained. We try to achieve a smoothing of the correlation matrix" , envir=.dico) + assign("desc_large_format_must_be_numeric_or_integer" , "If your data is in large format, all measurements must be numerical or integer" , envir=.dico) + assign("desc_list_of_objects_still_in_mem" , "List of objects still in memory of R" , envir=.dico) + assign("desc_log_with_base" , "the basic logarithm" , envir=.dico) + assign("desc_manifest_variables_of" , "Manifest Variables of" , envir=.dico) + assign("desc_manual_contrast_need_coeff_matrice" , "If you enter contrasts manually, all variables in the analysis must have their coefficient matrix" , envir=.dico) + assign("desc_matrix_is_singular_mardia_cannot_be_performed" , "The matrix is singular and the Marida test cannot be performed. Only univariate analyses can be carried out" , envir=.dico) + assign("desc_mcnemar_need_2x2_table_yours_are_different" , "The McNemar test involves a 2x2 array. The dimensions of your table are different. " , envir=.dico) + assign("desc_modalities_product_must_correspond_to_cols_selected" , "the output of the modes of each variable must correspond to the number of columns selected. " , envir=.dico) + assign("desc_model_contains_error" , "The model cannot be evaluated. It must contain an error." , envir=.dico) + assign("desc_model_could_not_converge" , "The model could not converge. The parameters have been adapted to allow the model to converge" , envir=.dico) + assign("desc_model_seems_incorrect_could_not_be_created" , "The model seems incorrect and could not be created." , envir=.dico) + assign("desc_most_common_effect_size" , "the most frequent effect size is the partial square - pes.nThe most precise effect size is the generalized square - ges" , envir=.dico) + assign("desc_multicolinearity_risk" , "multicolinearite risk if matrix determinant is less than 0.00001" , envir=.dico) + assign("desc_multiple_ways_to_compute_squares_sum" , "There are several ways to calculate the sum of squares. The default choice of commercial software is a sum of type 3 squares, prioritizing interactions rather than main effects. " , envir=.dico) + assign("desc_must_be_dichotomic" , "modalites. It is incompatible with a logistic regression. It must be dichotomous." , envir=.dico) + assign("desc_nb_factors_must_be_positive_integer" , "The number of factors must be a positive integer less than the number of factors" , envir=.dico) + assign("desc_need_at_least_three_observation_by_combination" , "Some combinations of modes have less than 3 observations. You must have at least 3 observations for each combination" , envir=.dico) + assign("desc_neg_log_impossible" , "it is not possible to calculate logarithms for a base is negative. NA is fired" , envir=.dico) + assign("desc_no_analysis_can_be_performed_given_your_data" , "The variables you selected to perform your analysis do not allow any analysis to be made. Please redefine your analysis" , envir=.dico) + assign("desc_no_data_in_R_memory" , "there are no data in the memory of R, please import the data on which to perform the analysis" , envir=.dico) + assign("desc_non_equal_independant_variable_modalities_occurrence" , "The number of occurrences for each modeite of your independent variable is not the same. Please select a participating identifier" , envir=.dico) + assign("desc_non_numeric_value" , "The input value is not numerical, you must enter a numeric value" , envir=.dico) + assign("desc_non_numeric_variable" , "the variable is not digital" , envir=.dico) + assign("desc_non_param_are_rho_and_tau" , "The nonparametric test corresponds to the Spearman rho and the Kendall tau" , envir=.dico) + assign("desc_non_param_is_wilcoxon_or_mann_withney" , "The non-parametric test is the Wilcoxon test (or Mann-Whitney)" , envir=.dico) + assign("desc_no_obs_for_combination" , "no observations for combination" , envir=.dico) + assign("desc_no_result_saved" , "no result has been saved" , envir=.dico) + assign("desc_norm_must_be_numeric" , "The standard must be a numeric value. " , envir=.dico) + assign("desc_no_saved_analysis_found" , "No backup analysis could be found" , envir=.dico) + assign("desc_number_of_judge_is" , "the number of judges =" , envir=.dico) + assign("desc_number_of_missing_values" , "Number of missing values per variable" , envir=.dico) + assign("desc_number_of_observations_is" , "number of observations =" , envir=.dico) + assign("desc_number_outliers_removed" , "Number of observations withdrawn" , envir=.dico) + assign("desc_obs_with_asterisk_are_outliers" , "The observations marked with an asterisk are considered to be influential at least on a criteria" , envir=.dico) + assign("desc_odd_ratio_cannot_be_computed" , "Or cannot be calculated for tables larger than 2x3 or tables containing 0" , envir=.dico) + assign("desc_only_one_dependant_variable_alllowed" , "There can be only one dependent variable. " , envir=.dico) + assign("desc_only_one_file_format_at_time_EPS_JPG" , "Only one file format for saving figure may be used at a time (you have both EPS and JPG specified). " , envir=.dico) + assign("desc_only_one_file_format_at_time_EPS_PDF" , "Only one file format for saving figure may be used at a time (you have both PDF and EPS specified). " , envir=.dico) + assign("desc_only_one_file_format_at_time_PDF_JPG" , "Only one file format for saving figure may be used at a time (you have both PDF and JPG specified). " , envir=.dico) + assign("desc_only_values_above_diagonal_are_adjusted_for_multiple_comp" , "Only values above the diagonal are adjusted for multiple comparisons" , envir=.dico) + assign("desc_operation_succesful" , "Mathematic operation has gone smoothly." , envir=.dico) + assign("desc_order" , "sort" , envir=.dico) + assign("desc_outliers_identified_on_4_div_n" , "Influential values are identified based on 4/n" , envir=.dico) + assign("desc_outliers_identified_on_mahalanobis" , "Influential values are identified based on the distance of Mahalanobis with a chi threshold at 0.001" , envir=.dico) + assign("desc_outliers_on_4_div_n" , "Influential values are identified on the basis of 4/n" , envir=.dico) + assign("desc_packages_used_for_this_function" , "Packages used for this function" , envir=.dico) + assign("desc_param_is_BP" , "The parametric test is the Bravais-Pearson correlation" , envir=.dico) + assign("desc_param_is_t_test" , "The parametric test is the classic t test" , envir=.dico) + assign("desc_param_test_is_classical_reg_robusts_are_m_estimator" , "The parametric test is the classic regression and robust tests are an estimate on an M estimater as well as a bootstrap." , envir=.dico) + assign("desc_percentile_bootstrap_prefered_for_small_samples" , "the percentile boottrap method must be preferred for small samples" , envir=.dico) + assign("desc_perfectly_correlated_variables_in_matrix_trying_to_solve" , "you try to make a matrix of correlations with perfectly correlated variables. This is a concern for the calculation of Mahalanobis' distance. We are trying to solve the problem." , envir=.dico) + assign("desc_polyc_correlations_failed_rho_used_instead" , "Polychoric correlations have failed. The correlations used are Spearman rho" , envir=.dico) + assign("desc_proba_and_IC_estimated_on_bootstrap" , "Probabilities and ICs are estimated on the basis of a bootrap. The IC is corrected for multiple comparison, unlike the probability reported." , envir=.dico) + assign("desc_probabilities_vector_please_no_fraction" , "Vector of probabilities. Note: do not enter fractions" , envir=.dico) + assign("desc_red_dot_is_mean_error_is_sd" , "The red dot is the average. The error bar is the scale-type" , envir=.dico) + assign("desc_references" , "References for packages used for this analysis" , envir=.dico) + assign("desc_removed_variable" , "deleted variable" , envir=.dico) + assign("desc_removing_outliers_weakens_sample_size" , "Removal of influential values results in too small a number of modalites to complete the analysis" , envir=.dico) + assign("desc_result_succesfully_imported_in" , "Results were correctly imported into" , envir=.dico) + assign("desc_robusts_statistics_could_not_be_computed" , "The robust statistics could not be achieved" , envir=.dico) + assign("desc_robust_statistics_are_alternative_to_the_principal_but_slower" , "The robust statistics are alternative to the main analysis, usually involving bootstraps. These analyses are often slower" , envir=.dico) + assign("desc_saturation_criterion_must_be_between_zero_and_one" , "The saturation criterion must be between 0 and 1." , envir=.dico) + assign("desc_search_here" , "Type your search here" , envir=.dico) + assign("desc_selected_obs_are_in" , "the observations you have selected are in" , envir=.dico) + assign("desc_selection_for_bayesian_factor_does_not_apply_to_complex_models" , "The selection methods for Bayesian factors do not apply for complex models. " , envir=.dico) + assign("desc_should_specify_nb_factors_repeated_measure" , "you need to specify the number of factors you can repetee" , envir=.dico) + assign("desc_single_dependant_variable_allowed_in_paired_t" , "There can be only one dependent variable for tstudents for matched samples" , envir=.dico) + assign("desc_singular_matrix_mahalanobis_on_max_info" , "Your matrix is singular, which is a concern. We are trying to solve the problem. If possible, the distance from Mahalanobis will then be calculated on the maximum information while avoiding the singularite. " , envir=.dico) + assign("desc_some_values_are_not_numeric" , "Not all entered values are numeric. Please enter numeric values only" , envir=.dico) + assign("desc_special_characters_have_been_removed" , "Special accents/characters have been deliberately removed to ensure the portability of easieR on all computers. " , envir=.dico) + assign("desc_specify_f_value" , "You must specify the value of the F. This value must be greater than 1" , envir=.dico) + assign("desc_specify_lower_bound" , "you must specify the lower limit" , envir=.dico) + assign("desc_specify_probability_value" , "You must specify the value of probability. This value shall be between 0 and 1" , envir=.dico) + assign("desc_specify_upper_bound" , "you must specify the upper limit" , envir=.dico) + assign("desc_standardized_saturation_on_correlation_matrix" , "standardized saturations based on the correlation matrix" , envir=.dico) + assign("desc_succesfully_imported" , "data were imported correctly" , envir=.dico) + assign("desc_succesful_operation" , "Operation has been done correctly" , envir=.dico) + assign("desc_tested_model_is" , "the model test is" , envir=.dico) + assign("desc_there_is_no_rotation" , "there is no rotation" , envir=.dico) + assign("desc_the_variable_lower" , "variable" , envir=.dico) + assign("desc_the_variable_upper" , "The variable" , envir=.dico) + assign("desc_this_analysis_will_not_be_performed" , ". This analysis will not be done." , envir=.dico) + assign("desc_this_index_is_prefered_for_most_cases" , " This index is adapted in most situations. The modified M-estimator must be preferred for N<20" , envir=.dico) + assign("desc_this_is_large_format" , "this is the wide format" , envir=.dico) + assign("desc_this_is_long_format" , "this is the long format" , envir=.dico) + assign("desc_times_less" , "times less" , envir=.dico) + assign("desc_times_more" , "times more" , envir=.dico) + assign("desc_to_display_results_use_summary" , "To display the results, please use summary(model.cfa)" , envir=.dico) + assign("desc_total_observations" , "total number of observations" , envir=.dico) + assign("desc_truncature_on_m_estimator_adapts_to_sample" , "The truncation on the M-estimetor adapts to the characteristics of the sample. " , envir=.dico) + assign("desc_two_cols_are_needed" , "For a large-format repeat factor, it takes at least two columns" , envir=.dico) + assign("desc_two_modalities_for_independante_categorial_variable" , "You must use an independent categorical variable with 2 modes" , envir=.dico) + assign("desc_unauthorized_char_replaced" , "Unauthorized characters were used for the name. These characters were replaced by points" , envir=.dico) + assign("desc_unavailable_distal_mediations" , "Distal mediations are not currently available / Distal mediations are not available for now" , envir=.dico) + assign("desc_user_exited_aov_plus" , "you left aov.plus" , envir=.dico) + assign("desc_value_must_be_between_zero_and_one" , "The value must be between 0 and 1" , envir=.dico) + assign("desc_value_must_be_numeric" , "The value must be numerical and between the minimum and maximum of the dependent variable. " , envir=.dico) + assign("desc_variable_added" , "Variable adds" , envir=.dico) + assign("desc_variable_must_be_numeric_and_of_non_null_variance" , "the variable must be digital and have a nonzero variance. " , envir=.dico) + assign("desc_variable_must_be_positive_int" , "the variable must be a positive *integer* integer" , envir=.dico) + assign("desc_variables_are_in" , "selected variables are in" , envir=.dico) + assign("desc_we_could_not_compute_anova_on_medians" , "Desole, we could not calculate the anova on the medianes, possibly due to a large number of ex aequo." , envir=.dico) + assign("desc_we_could_not_compute_robust_anova" , "Desole, we couldn't calculate the robust anova. " , envir=.dico) + assign("desc_working_dir_is_now" , "The work directory is present" , envir=.dico) + assign("desc_you_can_chose_predefined_or_manual_contrasts" , "You can choose predefined contrasts or specify them manually. In the latter case, please choose to specify the contrasts" , envir=.dico) + assign("desc_you_can_still_add" , "You can still add a specific value to the total. Leave 0 if you don't want to add anything" , envir=.dico) + assign("desc_you_can_still_multiply" , "You can still multiply the total by a specific value. Leave 1 if you no longer want to multiply by a new value" , envir=.dico) + assign("desc_you_did_this_operation" , "you have done the following operation:" , envir=.dico) + assign("desc_you_exited_afe" , "you left the AFE" , envir=.dico) + assign("desc_you_have_selected" , "you have selected" , envir=.dico) + assign("desc_you_must_give_obs_number" , "You must enter the observation number" , envir=.dico) + assign("desc_your_dependant_variable_has" , "Your real dependent a" , envir=.dico) + assign("desc_z_must_be_a_number" , "z must be a number" , envir=.dico) + assign("desc_author" , "author: 'Generate automatically by easieR'" , envir=.dico) + assign("desc_title" , "title: 'Results of your analyses'" , envir=.dico) + assign("txt_absolute_value" , "absolute" , envir=.dico) + assign("txt_added_variables_graph" , "Added variable scale" , envir=.dico) + assign("txt_additions" , "adds" , envir=.dico) + assign("txt_additive_effects" , "Additive effects" , envir=.dico) + assign("txt_additive_model_variables" , "Variable additive model" , envir=.dico) + assign("txt_add_of_cols" , "add columns" , envir=.dico) + assign("txt_add_of_specific_value" , "addition of a specific value" , envir=.dico) + assign("txt_adequation_adjustement_indexes" , "Adquest and adjustment indices" , envir=.dico) + assign("txt_adequation_measurement_of_matrix" , "Measurement of the matrix" , envir=.dico) + assign("txt_adequation_measures" , "Adequation measures" , envir=.dico) + assign("txt_adequation_outside_diagonal" , "Adequation based on values outside the diagonal" , envir=.dico) + assign("txt_adjusted_data_loftus_masson" , "Adjusted data (Loftus & Masson, 1994)" , envir=.dico) + assign("txt_adjusted_means_graph" , "Adjusted-Graphic Averages" , envir=.dico) + assign("txt_adjusted_means" , "Adjusted Averages" , envir=.dico) + assign("txt_adjustement_measure" , "Adjustment measures" , envir=.dico) + assign("txt_adjusted_p_dot_value" , "Adjusted p value" , envir=.dico) + assign("txt_agreement" , "Agreement" , envir=.dico) + assign("txt_aic_criterion" , "AIC - Akaike Information criteria" , envir=.dico) + assign("txt_alpha_warning" , "Alpha warning" , envir=.dico) + assign("txt_alternative" , "alternative" , envir=.dico) + assign("txt_analysis_factor_component" , "factor and component analyses" , envir=.dico) + assign("txt_analysis_on" , "analysis on" , envir=.dico) + assign("txt_analysis_on_truncated_means" , "Analysis on truncated means" , envir=.dico) + assign("txt_analysis_on_variable" , "Analysis on the variable" , envir=.dico) + assign("txt_analysis_premature_abortion" , "Premature stop of analysis" , envir=.dico) + assign("txt_ancova_application_conditions" , "Terms of application of the ancova" , envir=.dico) + assign("txt_and_the_number_of_obs" , "and the number of observations =" , envir=.dico) + assign("txt_and_YZ" , "and YZ =" , envir=.dico) + assign("txt_anova_ancova" , "variance and covariance analysis" , envir=.dico) + assign("txt_anova" , "Anova" , envir=.dico) + assign("txt_anova_on" , "anova on" , envir=.dico) + assign("txt_anova_on_modified_huber_estimator" , "Anova on Huber's Modified Localization Estimator" , envir=.dico) + assign("txt_anova_on_truncated_means" , "Anova based on truncated averages" , envir=.dico) + assign("txt_anova_with_welch_correction" , "Anova with Welch correction for heterogene variances" , envir=.dico) + assign("txt_apparied_correlations" , "correlations paired" , envir=.dico) + assign("txt_apriori" , "a priori" , envir=.dico) + assign("txt_autocorrelation" , "Autocorrection" , envir=.dico) + assign("txt_backward" , "Backward" , envir=.dico) + assign("txt_backward_step_descending" , "Backward- not-has-not descending" , envir=.dico) + assign("txt_barlett_test" , "Barlett Test" , envir=.dico) + assign("txt_bayes_factor_10" , "Bayes Factor (10)" , envir=.dico) + assign("txt_bayes_factor" , "BayesFactor" , envir=.dico) + assign("txt_bayesian_approach_hierarchical_models" , "Bayesian approach of hierarchical models" , envir=.dico) + assign("txt_bayesian_factor_by_group" , "Baysian actor by group" , envir=.dico) + assign("txt_bayesian_factor" , "Baysian reactor" , envir=.dico) + assign("txt_bayesian_factor_of_model" , "Model FB" , envir=.dico) + assign("txt_bayesian_factors_10" , "Bayesian reactor 10" , envir=.dico) + assign("txt_bayesian_factors_compute_null_with_bayesian_approach" , "Bayesian factors: calculates the equivalent of the null hypothesis test by adopting a Bayesian approach. " , envir=.dico) + assign("txt_bayesian_factors_for_BP" , "Bayesian forces for Bravais-Pearson correlation" , envir=.dico) + assign("txt_bayesian_factors_for_spearman" , "Bayesian forces for Spearman correlation" , envir=.dico) + assign("txt_bayesian_factors_sequential" , "Sequential Bayesian Factors" , envir=.dico) + assign("txt_bca_bootstrap_on_m_estimator" , "Bootstrap BCa type on the M-estimetor" , envir=.dico) + assign("txt_beta_table" , "Beta table" , envir=.dico) + assign("txt_between" , "between" , envir=.dico) + assign("txt_bidirectionnal" , "Bidirectional" , envir=.dico) + assign("txt_b_m_estimator" , "b (M estimator)" , envir=.dico) + assign("txt_bootstrap_on_BP" , "Bootstrap on the Bravais Pearson correlation" , envir=.dico) + assign("txt_bootstrap_t_method" , "bootstrap-t method" , envir=.dico) + assign("txt_bootstrap_t_method_on_truncated_means" , "Bootstrap using t method on truncated averages" , envir=.dico) + assign("txt_BP_correlation_by_group" , "Bravais-Pearson group correction" , envir=.dico) + assign("txt_breusch_pagan_test" , "Verification of the non-constency of the error variance (Breusch-Pagan test)" , envir=.dico) + assign("txt_cancel" , "cancel" , envir=.dico) + assign("txt_cauchy_prior_width" , "Cauchy Prior With (r)" , envir=.dico) + assign("txt_center_or_center_reduce" , "Center / center reduce" , envir=.dico) + assign("txt_center_reduce" , "center reduce" , envir=.dico) + assign("txt_ceres_graph_linearity" , "Character of Ceres Testing Linearitis" , envir=.dico) + assign("txt_chi_adjustement" , "Adjustment" , envir=.dico) + assign("txt_chi_independance" , "Independence" , envir=.dico) + assign("txt_chi_results_between_var_x" , "Results of chi.two between variable" , envir=.dico) + assign("txt_chi_squared" , "chi two" , envir=.dico) + assign("txt_chi_squared_empirical" , "chi square empirical" , envir=.dico) + assign("txt_chi_squared_likelihood_max" , "chi square of the maximum likelihood" , envir=.dico) + assign("txt_chi_squared_null_model" , "chi square of model null" , envir=.dico) + assign("txt_chi_squared_type" , "Khi type two" , envir=.dico) + assign("txt_coeff_table" , "Table of coefficients" , envir=.dico) + assign("txt_col_correspoding_to_variable" , "Columns corresponding to the variable" , envir=.dico) + assign("txt_col_mean" , "mean columns" , envir=.dico) + assign("txt_cols" , "columns" , envir=.dico) + assign("txt_col_separator" , "Column Separator" , envir=.dico) + assign("txt_cols_in_repeated_measure" , "Columns in Repeated Measures" , envir=.dico) + assign("txt_cols_multiplication" , "column multiplication" , envir=.dico) + assign("txt_comma" , "virgulate" , envir=.dico) + assign("txt_compare_to_baseline" , "Comparison with a base line" , envir=.dico) + assign("txt_compare_two_correlations" , "Comparison of two correlations" , envir=.dico) + assign("txt_comparison_of_two_correlations" , "comparison of the two correlations" , envir=.dico) + assign("txt_comparison_on_truncated_means" , "Comparison based on truncated averages" , envir=.dico) + assign("txt_comparisons_XY" , "Comparison of XY=" , envir=.dico) + assign("txt_comparison_to_norm" , "Comparison with a Standard" , envir=.dico) + assign("txt_comparison_two_by_two" , "Comparison 2 to 2" , envir=.dico) + assign("txt_compile_report" , "generate a report" , envir=.dico) + assign("txt_complementary_results" , "Complementary results (e.g. interaction contrasts and adjusted averages)" , envir=.dico) + assign("txt_complete_dataset" , "Complete data" , envir=.dico) + assign("txt_complete_model" , "Complete Model" , envir=.dico) + assign("txt_complexity" , "complexity" , envir=.dico) + assign("txt_complex_model" , "complex model" , envir=.dico) + assign("txt_confidance_threshold" , "Confidence threshold (1- alpha)" , envir=.dico) + assign("txt_confidence_interval_estimated_by_bootstrap" , "Interval of trust estimates by bootstrap" , envir=.dico) + assign("txt_confidence_interval" , "Confidential Interval" , envir=.dico) + assign("txt_confidence_interval_inferior_limit" , "Lower bound CI" , envir=.dico) + assign("txt_confidence_interval_superior_limit" , "Upper bound CI" , envir=.dico) + assign("txt_confidence_interval_of_saturations_on_bootstrap" , "Interval of confidence of saturations on the basis of bootstrap - may be biased in presence of Heyhood case" , envir=.dico) + assign("txt_confidence_interval_on_bootstrap" , "Trust interval based on bootstrap" , envir=.dico) + assign("txt_confidence_interval_on_standard_error" , "Confidence interval based on standard alpha error" , envir=.dico) + assign("txt_confirmatory_factorial_analysis" , " confirmatory factor analysis" , envir=.dico) + assign("txt_contrast" , "contrast" , envir=.dico) + assign("txt_contrasts" , "contrasts" , envir=.dico) + assign("txt_contrasts_for" , "Contrasts for" , envir=.dico) + assign("txt_contrasts_table_imitating_commercial_softwares" , "Table of contrasts imitating commercial software" , envir=.dico) + assign("txt_contrasts_table" , "Contrast table" , envir=.dico) + assign("txt_control_variables" , "Variable-s to control" , envir=.dico) + assign("txt_correction_for_polyc_corr_must_be_between_zero_and_one" , "The correction for the calculation of polycoric correlations shall be between 0 and 1." , envir=.dico) + assign("txt_correlation_between_scores_and_factors" , "Correlations of scores with factors" , envir=.dico) + assign("txt_correlation_between_var_x" , "Correlation between variable" , envir=.dico) + assign("txt_correlation_is" , "correction" , envir=.dico) + assign("txt_correlation_matrix_determinant" , "Determining the correlation matrix" , envir=.dico) + assign("txt_correlation_matrix_determinant_information" , "Determining the correlation matrix: information" , envir=.dico) + assign("txt_correlations_between_factors" , "correlations between factors" , envir=.dico) + assign("txt_correlations_comparison" , "comparison of correlations" , envir=.dico) + assign("txt_correlations_matrix_afe" , "Correlation matrix used for AFE" , envir=.dico) + assign("txt_covariance_matrix_adjusted" , "Adjusted covariance matrix" , envir=.dico) + assign("txt_covariance_matrix_estimated" , "Estimated covariance matrix" , envir=.dico) + assign("txt_cox_snell_r_2" , "Cox and Snell R^2" , envir=.dico) + assign("txt_cronbach_alpha" , "Cronbach Alpha" , envir=.dico) + assign("txt_cronbach_alpha_on_whole_scale" , "Cronbach Alpha on Scale Totalite" , envir=.dico) + assign("txt_cross_validation" , "Validation crossee" , envir=.dico) + assign("txt_csv_file" , "CSV file" , envir=.dico) + assign("txt_cumulated_explaination_ratio" , "Cumulative share of explanation" , envir=.dico) + assign("txt_cumulated_explained_variance_ratio" , "proportion of variance explained cumulated" , envir=.dico) + assign("txt_dataframe_choice" , "Dataframe selection" , envir=.dico) + assign("txt_data_import_export_save" , "Data - (Import, export, backup)" , envir=.dico) + assign("txt_decimal_separator" , "Separator of decimals" , envir=.dico) + assign("txt_default_outputs" , "Releases by default" , envir=.dico) + assign("txt_delete_observations_with_missing_values" , "Deletion of observations with missing values" , envir=.dico) + assign("txt_denominator" , "Denominator" , envir=.dico) + assign("txt_dependant_variables" , "Variable-dependent-s" , envir=.dico) + assign("txt_dependant_variable" , "dependent variable" , envir=.dico) + assign("txt_descriptive_statistics_by_group" , "Descriptive statistics by group" , envir=.dico) + assign("txt_detailed_corr_analysis" , "Analysis desize (Bravais Pearson/Spearman/tau) for one or few correlations" , envir=.dico) + assign("txt_deviation" , "Deviance" , envir=.dico) + assign("txt_dichotomic_ordinal" , "dichotomics/ordinal" , envir=.dico) + assign("txt_difference" , "Difference" , envir=.dico) + assign("txt_distance_mediation_effect" , "Remote mediation effect" , envir=.dico) + assign("txt_distance_mediator" , "Mediation distance" , envir=.dico) + assign("txt_do_nothing_keep_all_obs" , "Do nothing - Keep all observations" , envir=.dico) + assign("txt_dot" , "point" , envir=.dico) + assign("txt_durbin_watson_test_autocorr" , "Durbin-Watson test - autocorrelations" , envir=.dico) + assign("txt_dw_statistic" , "D-W statistics" , envir=.dico) + assign("txt_dynamic_crossed_table" , "Dynamic Cross Table" , envir=.dico) + assign("txt_effect" , "Effect" , envir=.dico) + assign("txt_equals_to" , "egal a" , envir=.dico) + assign("txt_error" , "error" , envir=.dico) + assign("txt_estimated_parameters_not_standardized" , "Non-standardized Parameters" , envir=.dico) + assign("txt_estimated_parameters" , "Advised parameters" , envir=.dico) + assign("txt_estimated_parameters_standardized" , "Standardized estimated parameters" , envir=.dico) + assign("txt_estimation" , "estimate" , envir=.dico) + assign("txt_excel_file" , " Excel file" , envir=.dico) + assign("txt_exogenous_fixed_variables" , "Variables exogenes fixed [fixed.x=default]" , envir=.dico) + assign("txt_expected" , "Attended" , envir=.dico) + assign("txt_expected_sample" , "Expected effects" , envir=.dico) + assign("txt_experimental_pan_between" , "Pan experimental enters" , envir=.dico) + assign("txt_explaination_ratio" , "Proportion of explanation" , envir=.dico) + assign("txt_explained_variance_ratio" , "proportion of variance explained" , envir=.dico) + assign("txt_explained_variance" , "Variance explained" , envir=.dico) + assign("txt_exponant" , "exposant" , envir=.dico) + assign("txt_exponant_or_root" , "exposant or root" , envir=.dico) + assign("txt_exponential" , "exponential" , envir=.dico) + assign("txt_export_data" , "export data" , envir=.dico) + assign("txt_factorial_analysis" , "factorial analysis" , envir=.dico) + assign("txt_factorial_analysis_using_fa_with_method" , "factorial analysis using the fa function of the psych package with the method" , envir=.dico) + assign("txt_factorial_exploratory_analysis" , "Exploratory factor analysis" , envir=.dico) + assign("txt_factor_name" , "factor name" , envir=.dico) + assign("txt_factors" , "factors. " , envir=.dico) + assign("txt_factors_ortho" , "Orthogonality of factors [orthogonal=FALSE]" , envir=.dico) + assign("txt_factors_to_keep_accord_to_parallel_analysis_is" , "the number of factors to remember according to the parallel analysis is" , envir=.dico) + assign("txt_fiability_analysis" , "Analysis of reliability and agreement" , envir=.dico) + assign("txt_fiability_by_removed_item" , "reliability by item deletes" , envir=.dico) + assign("txt_for_a_detailed_results_description_distal" , "For a detailed description of the results, ?distal.med" , envir=.dico) + assign("txt_for_a_detailed_results_description_mediation" , "For a detailed description of the results, ?mediation" , envir=.dico) + assign("txt_forward_step_ascending" , "Forward - not-a-not ascending" , envir=.dico) + assign("txt_friedman_anova_pairwise_comparison" , "Comparison 2 to 2 for Friedman's ANOVA" , envir=.dico) + assign("txt_f_value" , "F value" , envir=.dico) + assign("txt_get_working_dir" , "get work directory" , envir=.dico) + assign("txt_global_model_estimation" , "Global Model Estimation" , envir=.dico) + assign("txt_graphic_mean_sd" , "Graphic representation - Medium and level-type" , envir=.dico) + assign("txt_graphics" , "Graphics" , envir=.dico) + assign("txt_graphics_informations" , "Information on graphics" , envir=.dico) + assign("txt_group_analysis" , "Group Analysis" , envir=.dico) + assign("txt_groups_analysis" , "group analysis" , envir=.dico) + assign("txt_groups_variables" , "Variable-s groups" , envir=.dico) + assign("txt_grubbs_test" , " Grubbs Test" , envir=.dico) + assign("txt_hierarchical_factorial_analysis" , "Hierarchical factor analysis" , envir=.dico) + assign("txt_hierarchical_model_analysis" , "Hierarchical model analysis" , envir=.dico) + assign("txt_hierarchical_models_complete_model_sig_at_each_step" , "Hierarchical models - significativite of the complete model to each step" , envir=.dico) + assign("txt_hierarchical_models_deviance_table" , "Table of the analysis of the deviance of hierarchical models" , envir=.dico) + assign("txt_hierarchical_models" , "hierarchical models" , envir=.dico) + assign("txt_hierarchical_models_variance_analysis_table" , "Table of variance analysis of hierarchical models" , envir=.dico) + assign("txt_hosmer_lemeshow_r_2" , "Hosmer and Lemeshow R^2" , envir=.dico) + assign("txt_hypergeom_total_sample_fixed_rows_cols" , "hypergeom - Total fixed strength for rows and columns" , envir=.dico) + assign("txt_hypothesis_analysis" , "Analysis - Hypothesis tests" , envir=.dico) + assign("txt_identified_outliers_synthesis" , "Synthesis of the number of observations considered to be influential" , envir=.dico) + assign("txt_identifying_outliers" , "Identification of influential values" , envir=.dico) + assign("txt_id_variable" , "Variable *Identifier*" , envir=.dico) + assign("txt_import_data" , "import data" , envir=.dico) + assign("txt_imput_missing_values" , "Impacting Missing Values" , envir=.dico) + assign("txt_independant_correlations" , "Independent adjustments" , envir=.dico) + assign("txt_independant_group_variables" , "Variables to independent groups" , envir=.dico) + assign("txt_independant_variable" , "Independent variable" , envir=.dico) + assign("txt_indepmulti_fixed_sample_rows_cols" , "indepMulti - Fixed number for columns - variable" , envir=.dico) + assign("txt_indepmulti_total_fixed_rows_cols" , "indepMulti - Total fixed strength for lines - variable" , envir=.dico) + assign("txt_inferior" , "Inner" , envir=.dico) + assign("txt_inferior_or_equal_to" , "inferior or equal" , envir=.dico) + assign("txt_inferior_proba" , "inferior probability" , envir=.dico) + assign("txt_inferior_to" , "under a" , envir=.dico) + assign("txt_inflation_variance_factor" , "Inflation factor of variance" , envir=.dico) + assign("txt_influence_method" , "Influence Measurement" , envir=.dico) + assign("txt_information" , "Information" , envir=.dico) + assign("txt_init_values" , "Departure values" , envir=.dico) + assign("txt_inspect_initial_values" , "Inspect start values" , envir=.dico) + assign("txt_inspect_model_matrices" , "Inspect model matrices" , envir=.dico) + assign("txt_inspect_model_representation" , "Inspect model representation" , envir=.dico) + assign("txt_interaction_effects" , "Interaction effects" , envir=.dico) + assign("txt_interactive_model_variables" , "Interactive models" , envir=.dico) + assign("txt_is_different_from" , "is different from" , envir=.dico) + assign("txt_jointmulti_total_fixed_sample" , "jointMulti - Total fixed staff" , envir=.dico) + assign("txt_judge1" , "Juge 1" , envir=.dico) + assign("txt_judge2" , "Juge 2" , envir=.dico) + assign("txt_kaiser_meyer_olkin_index" , "Kaiser-Meyer-Olkin global index" , envir=.dico) + assign("txt_keep_default_values" , "Keep values by default" , envir=.dico) + assign("txt_kendall_coeff" , "Kendall Match Coefficient" , envir=.dico) + assign("txt_kendall_partial_semipartial_tau" , "Kendall partial/semipartial" , envir=.dico) + assign("txt_kendall_partial_tau" , "Kendall partial rate" , envir=.dico) + assign("txt_kendall_semipartial_tau" , "Kendall Semi-Partial" , envir=.dico) + assign("txt_kendall_tau" , "Kendall Tau" , envir=.dico) + assign("txt_kolmogorov_smirnov_comparing_two_distrib" , "Kolmogorov-Smirnov test comparing two distributions" , envir=.dico) + assign("txt_labeled_outliers" , "Values considered influential" , envir=.dico) + assign("txt_latent_variable_name" , "Latent variable name" , envir=.dico) + assign("txt_less_square_diagonally_pondered" , "mind square weight diagonally" , envir=.dico) + assign("txt_less_square_generalized" , "mind tile generalises" , envir=.dico) + assign("txt_less_square_not_pondered" , "mind unweighted square" , envir=.dico) + assign("txt_less_square_pondered" , "mind square" , envir=.dico) + assign("txt_levene_test_verifying_homogeneity_variances" , "Levene test checking variance homogeneity" , envir=.dico) + assign("txt_likelihood_only_for_estimator" , "True (only for estimator=ML) [likelihood=default]" , envir=.dico) + assign("txt_likelihood_ratio_g_test" , "Ratio of likelihood (G test)" , envir=.dico) + assign("txt_lilliefors_d" , "D de Lilliefors" , envir=.dico) + assign("txt_linearity_graph_between_predictors_and_dependant_variable" , "Character testing linearite between predictors and dependent variable" , envir=.dico) + assign("txt_link_only_for_estimator" , "Link (only for estimator=MML) [link=probit]" , envir=.dico) + assign("txt_list_of_objects_in_mem" , "list of objects in memory" , envir=.dico) + assign("txt_logarithm" , "logarithm" , envir=.dico) + assign("txt_long_or_large_format" , "Long format wide format" , envir=.dico) + assign("txt_lower_bound_rmsea" , "inferior limit of RMSEA" , envir=.dico) + assign("txt_mann_whitney_test" , " Mann-Whitney test - Wilcoxon" , envir=.dico) + assign("txt_mathematical_operations_on_variables" , "Mathematic operations on variables" , envir=.dico) + assign("txt_matrix_type" , "matrix type" , envir=.dico) + assign("txt_max_likelihood_chi_squared_proba_value" , "value of the probability of the chi carre maximum likelihood" , envir=.dico) + assign("txt_max_likelihood" , "maximum likelihood" , envir=.dico) + assign("txt_mcnemar_results_between_var_x" , "Results of McNemar test between variable" , envir=.dico) + assign("txt_mcnemar_test" , "McNemar Test" , envir=.dico) + assign("txt_mcnemar_test_with_continuity_correction" , "McNemar test with continuity correction" , envir=.dico) + assign("txt_mcnemar_test_without_yates_correction" , "McNemar test without continuity correction" , envir=.dico) + assign("txt_mcnemar_test_with_yates_correction" , "McNemar Test with Yates Correction" , envir=.dico) + assign("txt_mean1" , "Average1" , envir=.dico) + assign("txt_mean2" , "Average2" , envir=.dico) + assign("txt_mean_complexity" , "Medium Complexity" , envir=.dico) + assign("txt_mean_complexity_is" , "average complexity is of" , envir=.dico) + assign("txt_means_adjusted_standard_errors" , "adjusted averages and standard errors" , envir=.dico) + assign("txt_means_comparison" , "Comparison of Averages" , envir=.dico) + assign("txt_mean_sd_for_adjusted_data" , "Average and scale-type for adjusted data" , envir=.dico) + assign("txt_mean_sd_for_non_adjusted_data" , "Average and scale-type for unadjusted data" , envir=.dico) + assign("txt_mean_sd" , "Average and scale-type" , envir=.dico) + assign("txt_measured_variable_name" , "Measuring variable name" , envir=.dico) + assign("txt_median" , "Mediane" , envir=.dico) + assign("txt_mediation_effect" , "Mediation Effects" , envir=.dico) + assign("txt_mediator2" , "Mediator 2" , envir=.dico) + assign("txt_mediator" , "Mediator" , envir=.dico) + assign("txt_method_choice" , "Choice of the method" , envir=.dico) + assign("txt_min_correlation_between_scores_and_factors" , "Minimum possible correlation of scores with factors" , envir=.dico) + assign("txt_minus" , "less" , envir=.dico) + assign("txt_missing_values_treatment" , "Treatment of Missing Values" , envir=.dico) + assign("txt_mixt_correlations" , "mixed correlations" , envir=.dico) + assign("txt_modalities_name_for" , "Names of the modes for" , envir=.dico) + assign("txt_modalities_to_regroup" , "Modalites to group" , envir=.dico) + assign("txt_modality" , "modality" , envir=.dico) + assign("txt_model_degrees_of_freedom" , "degrees of model freedom" , envir=.dico) + assign("txt_model_matrix" , "Model Matrix" , envir=.dico) + assign("txt_model_representation" , "Model representation" , envir=.dico) + assign("txt_model_significance" , "Significativite of the global model" , envir=.dico) + assign("txt_multicolinearity_tests" , "Multicolinearite tests" , envir=.dico) + assign("txt_multicolinearity_test" , "Multicolinearite test" , envir=.dico) + assign("txt_multiple_imputation_amelia" , "Multiple imputation - Amelia" , envir=.dico) + assign("txt_multiple_r_square_of_factors_scores" , "R multiple square scores with factors" , envir=.dico) + assign("txt_multiplication" , "multiplication" , envir=.dico) + assign("txt_multivariate_normality" , "Normalite multivarie" , envir=.dico) + assign("txt_nb_variables_measured" , "Number of variables measured" , envir=.dico) + assign("txt_negative_values" , "negative values" , envir=.dico) + assign("txt_new_data_set" , "new data set" , envir=.dico) + assign("txt_new_dir" , "new directory" , envir=.dico) + assign("txt_N_of_XY_corr" , "XY correlation N" , envir=.dico) + assign("txt_N_of_XY_NUM_corr" , "N of XY:TXT" , envir=.dico) + assign("txt_N_of_XZ_corr" , "N of XZ correlation" , envir=.dico) + assign("txt_N_of_XZ_NUM_corr" , "N of XZ:TXT" , envir=.dico) + assign("txt_non_adjusted_data" , "Unadjusted data" , envir=.dico) + assign("txt_non_centered" , "No center" , envir=.dico) + assign("txt_no" , "no" , envir=.dico) + assign("txt_non_parametric_test" , "Nonparametric test" , envir=.dico) + assign("txt_non_param_model" , "Non-parametric model" , envir=.dico) + assign("txt_non_param_test" , "non-parametric test" , envir=.dico) + assign("txt_non_pondered_coeff" , "Kappa coefficient non-weight" , envir=.dico) + assign("txt_non_standardized_residuals" , "Non-standardised residues" , envir=.dico) + assign("txt_null_hypothesis_tests" , "H0 test" , envir=.dico) + assign("txt_null_model_degrees_of_freedom" , "Degrees of null model freedom" , envir=.dico) + assign("txt_numerator" , "Numerator" , envir=.dico) + assign("txt_objective_function_of_model" , "objective model function" , envir=.dico) + assign("txt_objective_function_of_null_model" , "objective null model function" , envir=.dico) + assign("txt_objects_in_mem" , "Memory objects" , envir=.dico) + assign("txt_object_to_remove" , "Objects to be deleted" , envir=.dico) + assign("txt_observed" , "Observations" , envir=.dico) + assign("txt_observed_sample" , "Observed Effects" , envir=.dico) + assign("txt_odd_ratio" , "Odd ratio" , envir=.dico) + assign("txt_order" , "Sort" , envir=.dico) + assign("txt_orthogonals_inverse" , "orthogonal reverses" , envir=.dico) + assign("txt_orthogonals" , "orthogonal" , envir=.dico) + assign("txt_other_correlations" , "Other correlations" , envir=.dico) + assign("txt_other_data" , "other data" , envir=.dico) + assign("txt_outliers" , "Influential observations" , envir=.dico) + assign("txt_outliers_synthesis" , "Synthesis of influential observations" , envir=.dico) + assign("txt_outliers_values" , "Influential values" , envir=.dico) + assign("txt_packages_install" , "Installation of packages" , envir=.dico) + assign("txt_packages_update" , "packages update" , envir=.dico) + assign("txt_packages_verification" , "Verification of packages" , envir=.dico) + assign("txt_parallel_analysis" , "parallel analyses" , envir=.dico) + assign("txt_param_model" , "parametric model" , envir=.dico) + assign("txt_param_tests" , "Parametric tests" , envir=.dico) + assign("txt_param_test" , "parametric test" , envir=.dico) + assign("txt_partial_and_semi_correlations" , "Partial and semi-partial corrections" , envir=.dico) + assign("txt_partial_corr_BP_by_group" , "Partial correction of Bravais-Pearson by group" , envir=.dico) + assign("txt_partial_correlations_matrix" , "Partial Correlations Matrix" , envir=.dico) + assign("txt_partial_rho" , "Rho partiale de Spearman" , envir=.dico) + assign("txt_partial_semi_BP" , "Partial/semi-partial correction of Bravais Pearson" , envir=.dico) + assign("txt_partial_semi_partial_rho" , "Partial/Semipartial Rho" , envir=.dico) + assign("txt_partial_spearman_by_group" , "Partial patch of Spearman by group" , envir=.dico) + assign("txt_participants_id" , "participating identifier" , envir=.dico) + assign("txt_partila_correlations" , "Partial corrections" , envir=.dico) + assign("txt_percentage_col" , "Percentage by column" , envir=.dico) + assign("txt_percentage_row" , "Percentage per line" , envir=.dico) + assign("txt_percentage_total" , "Total percentage" , envir=.dico) + assign("txt_percentile_bootstrap_on_m_estimators" , "Percentile bootstrap on M-estimetor" , envir=.dico) + assign("txt_p_estimation_with_monter_carlo" , "Value estimated by Monte Carlo simulation" , envir=.dico) + assign("txt_plus" , "plus" , envir=.dico) + assign("txt_poisson_total_not_fixed_sample" , "fish - total non-fixed" , envir=.dico) + assign("txt_polyc_correlations" , "polychoric correlations" , envir=.dico) + assign("txt_polynomials" , "polynomials" , envir=.dico) + assign("txt_pondered_kappa" , "Kappa weight coefficient" , envir=.dico) + assign("txt_positive_values" , "positive values" , envir=.dico) + assign("txt_predicted_probabilities" , "Probability" , envir=.dico) + assign("txt_predictor" , "Predictor" , envir=.dico) + assign("txt_principal_analysis" , "Main Analysis" , envir=.dico) + assign("txt_principal_analysis_using_psych_with_algo" , "main component analysis using the [principal] function of the psych package, the algorithm is" , envir=.dico) + assign("txt_principal_component_analysis" , "Main Component Analysis" , envir=.dico) + assign("txt_probabilities" , "probabilities" , envir=.dico) + assign("txt_probability_matrix" , "probability matrix" , envir=.dico) + assign("txt_probability_value" , "probability value" , envir=.dico) + assign("txt_proper_values_index" , "Index of own values" , envir=.dico) + assign("txt_pseudo_r_square_delta" , "Delta du pseudo R carre" , envir=.dico) + assign("txt_p_value_with_monte_carlo" , "Value p by Monte Carlo simulation" , envir=.dico) + assign("txt_ranks_lower" , "ranges" , envir=.dico) + assign("txt_ranks_upper" , "Rangs" , envir=.dico) + assign("txt_references" , "References" , envir=.dico) + assign("txt_remove_object_in_memory" , "Deletion of memory object" , envir=.dico) + assign("txt_replace_by_mean" , "Replace by Average" , envir=.dico) + assign("txt_replace_by_median" , "Replace with media" , envir=.dico) + assign("txt_residual_distribution" , "Distribution of residual" , envir=.dico) + assign("txt_residual_error" , "Residual error" , envir=.dico) + assign("txt_residual" , "residual" , envir=.dico) + assign("txt_residuals_distribution" , "Distribution of survivors" , envir=.dico) + assign("txt_residue" , "Residus" , envir=.dico) + assign("txt_residues_significativity_holm_correction" , "Significativite des residus - probability corrected by applying the Holm method" , envir=.dico) + assign("txt_residue_standardized_adjusted" , "Residues standardises fit" , envir=.dico) + assign("txt_residue_standardized" , "Residue standardized" , envir=.dico) + assign("txt_result" , "Result" , envir=.dico) + assign("txt_rho" , "Rho de Spearman" , envir=.dico) + assign("txt_robust_analysis" , "Sturdy analyses" , envir=.dico) + assign("txt_robusts" , "robust" , envir=.dico) + assign("txt_robusts_statistics" , "Strudy statistics" , envir=.dico) + assign("txt_robust_statistics" , "Strudy statistics - can take time" , envir=.dico) + assign("txt_robusts_tests_with_bootstraps" , "Sturdy test - involving bootstraps" , envir=.dico) + assign("txt_rotation_is_a_rotation" , "rotation is a rotation" , envir=.dico) + assign("txt_sample_size_NUM" , "Size of sample:TXT" , envir=.dico) + assign("txt_saturations_sum_of_squares" , "Sum of squares of saturations" , envir=.dico) + assign("txt_search_for_new_function" , "Search for a new function" , envir=.dico) + assign("txt_second_variables_set" , "Second set of variables" , envir=.dico) + assign("txt_selected_data" , "Give that you just selected" , envir=.dico) + assign("txt_selection_method_akaike" , "Selection method - Akaike information criteria" , envir=.dico) + assign("txt_selection_method_bayesian_factor" , "Selection methods: Bayesian factors" , envir=.dico) + assign("txt_selection_method" , "Selection method" , envir=.dico) + assign("txt_selection_methods" , "Selection methods" , envir=.dico) + assign("txt_selection" , "selection" , envir=.dico) + assign("txt_select_obs" , "Select Observations" , envir=.dico) + assign("txt_select_variables" , "Select variables" , envir=.dico) + assign("txt_semi_BP" , "Semi-partial correction of Bravais Pearson" , envir=.dico) + assign("txt_semicolon" , "point comma" , envir=.dico) + assign("txt_semi_partial_rho" , "Spearman Semi-Partial Rho" , envir=.dico) + assign("txt_sequential_bayesian_factors_robustness_analysis" , "Sequential Bayesian Factors - Robustness Analysis" , envir=.dico) + assign("txt_shapiro_wilk" , "W de Shapiro-Wilk" , envir=.dico) + assign("txt_simple_mediation_effect" , "simple mediation effects" , envir=.dico) + assign("txt_slopes_homogeneity_between_groups_on_dependant_variable" , "Test of homogeneite slopes between groups on the dependent variable" , envir=.dico) + assign("txt_spearman_kendall_corr_by_group" , "Spearman/Kendall group correction" , envir=.dico) + assign("txt_specific_val_multiplication" , "multiplication of a specific value" , envir=.dico) + assign("txt_specify_contrasts" , "specify your contrasts" , envir=.dico) + assign("txt_specify_model" , "Specify the model" , envir=.dico) + assign("txt_specify_working_dir" , "specify work directory" , envir=.dico) + assign("txt_spss_file" , "SPSS file" , envir=.dico) + assign("txt_square" , "square" , envir=.dico) + assign("txt_rectangular" , "rectangular" , envir=.dico) + assign("txt_standardized_parameters" , "Standardized Parameters" , envir=.dico) + assign("txt_statistic" , "statistic" , envir=.dico) + assign("txt_step" , "etape" , envir=.dico) + assign("txt_student_bootstrap_on_truncated_means" , "Student bootstrap on truncated means" , envir=.dico) + assign("txt_student_t_by_group" , "Student t by group" , envir=.dico) + assign("txt_student_t_independant" , "tstudent for independent samples" , envir=.dico) + assign("txt_student_t" , "Student t" , envir=.dico) + assign("txt_student_t_test_norm" , "Student test - comparison to a standard" , envir=.dico) + assign("txt_student_t_test_paired" , "Student test - comparison of two matching samples" , envir=.dico) + assign("txt_substraction" , "subtraction" , envir=.dico) + assign("txt_sufficient_factors" , "sufficient factors" , envir=.dico) + assign("txt_superior_or_equal_to" , "upper or equal a" , envir=.dico) + assign("txt_superior_proba" , "high probability" , envir=.dico) + assign("txt_superior" , "Superior" , envir=.dico) + assign("txt_superior_to" , "upper a" , envir=.dico) + assign("txt_supports_alternative" , "In favour of alternative hypothesis" , envir=.dico) + assign("txt_supports_null" , "In favour of null hypothesis" , envir=.dico) + assign("txt_suppress_all_outliers" , "Deleting all outliers" , envir=.dico) + assign("txt_suppress_outliers_manually" , "Manual Deletion" , envir=.dico) + assign("txt_synthesis_table" , "Summary Table" , envir=.dico) + assign("txt_teaching_material" , "Pedagogical material" , envir=.dico) + assign("txt_tetra_polyc_corr_matrix_or_mixt" , "Tetrachoric/polychoric or mixed correlation matrix" , envir=.dico) + assign("txt_this_tests_if" , "It tests if" , envir=.dico) + assign("txt_threshold" , "Threshold" , envir=.dico) + assign("txt_time_1" , "time 1" , envir=.dico) + assign("txt_time1" , "time1" , envir=.dico) + assign("txt_time_2" , "time 2" , envir=.dico) + assign("txt_time2" , "time2" , envir=.dico) + assign("txt_tolerance" , "Tolerance" , envir=.dico) + assign("txt_total_sample_not_fixed" , "Total non-fixed effect" , envir=.dico) + assign("txt_troncature_num" , "Troncature:TXT" , envir=.dico) + assign("txt_truncated_means" , "Truncated averages" , envir=.dico) + assign("txt_t_test_choice" , "Test selection t" , envir=.dico) + assign("txt_tucker_lewis_fiability_factor" , "Tucker Lewis reliability factor - TLI" , envir=.dico) + assign("txt_two_independant_samples" , "Two independent samples" , envir=.dico) + assign("txt_two_paired_samples" , "Two paired samples" , envir=.dico) + assign("txt_txt_file" , "Txt file" , envir=.dico) + assign("txt_type" , "Type" , envir=.dico) + assign("txt_understanding_alpha_and_power" , "Understanding Alpha and Power" , envir=.dico) + assign("txt_understanding_bayesian_inference" , "Understanding a Bayesian Inference" , envir=.dico) + assign("txt_understanding_central_limit_theorem" , "Understanding the central theorem limit" , envir=.dico) + assign("txt_understanding_confidance_interval" , "Understanding a confidence interval" , envir=.dico) + assign("txt_understanding_corr_2" , "Understanding a correlation 2" , envir=.dico) + assign("txt_understanding_corr" , "Understanding correlation" , envir=.dico) + assign("txt_understanding_heterogenous_variance_effects" , "Understanding the effects of heterogene variances" , envir=.dico) + assign("txt_understanding_likelihood" , "Understanding the maximum likelihood" , envir=.dico) + assign("txt_understanding_negative_positive_predic_power" , "Understanding positive predictive power and negative predictive power" , envir=.dico) + assign("txt_understanding_prev_sens_specificity_2" , "Understanding Prevalence, Sensibility and Specificity 2" , envir=.dico) + assign("txt_understanding_prev_sens_specificity" , "Understanding prevalence, sensitivity and specificity" , envir=.dico) + assign("txt_upper_bound_rmsea" , "the upper limit of the RMSEA" , envir=.dico) + assign("txt_user_exited_easieR" , "You left easieR" , envir=.dico) + assign("txt_values" , "values" , envir=.dico) + assign("txt_value" , "value" , envir=.dico) + assign("txt_variable_descriptive_statistics" , "Descriptive variable statistics" , envir=.dico) + assign("txt_variables_coeff_matrix" , "variable coefficient matrix" , envir=.dico) + assign("txt_variables_contribution_to_model" , "Contribution of variables to the model" , envir=.dico) + assign("txt_variables_from_step" , "Variable of this step" , envir=.dico) + assign("txt_verify_packages_install" , "Check package installation" , envir=.dico) + assign("txt_view_data" , "see data" , envir=.dico) + assign("txt_VIF" , "VIF" , envir=.dico) + assign("txt_warning" , "Warning" , envir=.dico) + assign("txt_wilcoxon_by_group" , "Wilcoxon by group" , envir=.dico) + assign("txt_without_outliers" , "Data without influential value" , envir=.dico) + assign("txt_without_welch_correction" , "without Welch correction" , envir=.dico) + assign("txt_without_yates_correction" , "Without Yates Correction" , envir=.dico) + assign("txt_with_welch_correction" , "with Welch correction" , envir=.dico) + assign("txt_with_yates_correction" , "With Yates Correction" , envir=.dico) + assign("txt_working_dir" , "Work Directory" , envir=.dico) + assign("txt_x_axis_variables" , "Variable-s in abscess" , envir=.dico) + assign("txt_XY_correlation" , "Correlation between XY" , envir=.dico) + assign("txt_XY_NUM_correlation" , "Correlation between XY:TXT" , envir=.dico) + assign("txt_XZ_correlation" , "Correlation between XZ" , envir=.dico) + assign("txt_XZ_NUM_correlation" , "Correlation between XZ:TXT" , envir=.dico) + assign("txt_y_axis_variables" , "Variable-s ordered" , envir=.dico) + assign("txt_yes" , "yes" , envir=.dico) + assign("txt_your_data" , "Your data" , envir=.dico) + assign("txt_YZ_correlation" , "Correlation between YZ" , envir=.dico) + assign("txt_YZ_NUM_correlation" , "Correlation between YZ:TXT" , envir=.dico) + assign("ask_probability_correction" , "Which p adjustment do you want ? If you do not want any p adjustment, choose +none+" , envir=.dico) + assign("ask_contrasts_must_be_ortho" , "The contrasts must be orthogonal. Do you want to continue?" , envir=.dico) + assign("desc_bayesian_factors_chosen_in" , "Baysian factors is choosen in " , envir=.dico) + assign("desc_cross_validation_issues" , "cross validation is encountering some issues" , envir=.dico) + assign("desc_easier_metapackage" , "easieR: An R metapackage. Retrieved from https://github.com/NicolasStefaniak/easieR" , envir=.dico) + assign("desc_first_time_easier" , " If you are using easieR for the first time, please use the function ez.install in order to ensure that easieR will work properly.n If you are using easieR for the first time, please use the ez.install function to ensure that easieR works properly." , envir=.dico) + assign("ask_chose_variables" , "please choose the variable(s)" , envir=.dico) + assign("ask_correlations_type" , "Type of correlations?" , envir=.dico) + assign("ask_dependant_variable_name" , "What is the name of the dependent variable?" , envir=.dico) + assign("ask_factors_number" , "Number of factors?" , envir=.dico) + assign("ask_filename" , "What name do you want to give the file?" , envir=.dico) + assign("ask_independant_variable_name" , "What is the name of the independent variable?" , envir=.dico) + assign("ask_is_long_format_correct" , "Is the structure in a long format of your data correct?" , envir=.dico) + assign("ask_model" , "Model?" , envir=.dico) + assign("ask_ordinal_variables" , "Ordinal Variables?" , envir=.dico) + assign("ask_save_results" , "Save Results?" , envir=.dico) + assign("ask_save" , "Do you want to save?" , envir=.dico) + assign("ask_specify_contrasts" , "Please specify contrasts. " , envir=.dico) + assign("ask_variables" , "What are the variables to select?" , envir=.dico) + assign("ask_variables_type" , "Nature of variables?" , envir=.dico) + assign("ask_what_to_do" , "What do you want to do?" , envir=.dico) + assign("ask_which_analysis" , "What analysis do you want?" , envir=.dico) + assign("desc_all_contrasts_description" , "The a priori contrasts correspond to the contrasts that allow to test hypotheses a priori.nThe contrasts 2 to 2 allow to make all comparisons 2 to 2 by applying or not a correction to probabilities" , envir=.dico) + assign("desc_contrasts_must_be_coeff_matrices_in_list" , "Contracts must be matrix coefficients placed in a list whose name of each level corresponds to a factor" , envir=.dico) + assign("desc_percentage_outliers" , "% of observations considered influential" , envir=.dico) + assign("desc_robusts_statistics_could_not_be_computed_verify_WRS" , "The robust statistics could not be achieved. Check the installation of the WRS package" , envir=.dico) + assign("desc_some_participants_have_missing_values_on_repeated_measures" , "Some participants have missing values on the repeted measurement factors. They will be removed from the analyses." , envir=.dico) + assign("txt_absence_of_difference_between_groups_test_on" , "Test of absence of difference between groups on " , envir=.dico) + assign("txt_anova_on_medians" , "Anova on the media" , envir=.dico) + assign("txt_anova_on_m_estimator" , "ANOVA on M estimator" , envir=.dico) + assign("txt_bayesian_factors" , "Baysian Factors" , envir=.dico) + assign("txt_BP_correlation" , "Bravais-Pearson Correlation" , envir=.dico) + assign("txt_center" , "center" , envir=.dico) + assign("txt_cohen_d" , "D of Cohen" , envir=.dico) + assign("txt_correlations" , "Correlations" , envir=.dico) + assign("txt_correlations_matrix" , "Correlations Matrix" , envir=.dico) + assign("txt_descriptive_statistics_of_interaction_between_x" , "Descriptive statistics of the interaction between" , envir=.dico) + assign("txt_descriptive_statistics" , "Descriptive statistics" , envir=.dico) + assign("txt_empirical_chi_square_proba_value" , "value of the probabilities of empirical chi tile" , envir=.dico) + assign("txt_factor" , "factor." , envir=.dico) + assign("txt_friedman_anova" , "Anova de Friedman" , envir=.dico) + assign("txt_import_results" , "import results" , envir=.dico) + assign("txt_interface_objects_in_memory" , "Interface - objects in memory, clean memory, working directory, language" , envir=.dico) + assign("txt_intraclass_correlation" , "Intraclass correlation" , envir=.dico) + assign("txt_kruskal_wallis_pairwise" , "Kruskal-Wallis Test - Comparison Two to Two" , envir=.dico) + assign("txt_kruskal_wallis_test" , "Kruskal-Wallis Test" , envir=.dico) + assign("txt_latent_variables_intercept" , "Intercept of latent variables [int.lv.free=FALSE]" , envir=.dico) + assign("txt_observed_variables_intercept" , "Intercept of observed variables [int.ov.free=FALSE]" , envir=.dico) + assign("txt_logistic_regressions" , "Logistical Regressions" , envir=.dico) + assign("txt_mauchly_test_sphericity_covariance_matrix" , "Mauchly test testing the sphericite of the covariance matrix" , envir=.dico) + assign("txt_none" , "none" , envir=.dico) + assign("txt_non_param_analysis" , "Nonparametric analysis" , envir=.dico) + assign("txt_normality_tests" , "Standardity test" , envir=.dico) + assign("txt_pairwise_comparisons" , "Comparations 2 to 2" , envir=.dico) + assign("txt_pairwise" , "pairwise" , envir=.dico) + assign("txt_partial_corr_BP" , "Partial correction of Bravais-Pearson" , envir=.dico) + assign("txt_preprocess_sort_select_operations" , "Pretreatments (tri, selection, mathematical operations, Missing value processing)" , envir=.dico) + assign("txt_press_enter_to_continue" , "Press [enter] to continue" , envir=.dico) + assign("txt_regressions" , "regressions" , envir=.dico) + assign("txt_repeated_measures" , "Measures repeats" , envir=.dico) + assign("txt_sample_size" , "size of sample" , envir=.dico) + assign("txt_test_model" , "Test model" , envir=.dico) + assign("txt_variables" , "variables" , envir=.dico) + assign("txt_variable" , "variable" , envir=.dico) + assign("desc_corr_group_analysis_spec" , "If you want to perform the analysis for different subsamples based on a categorical criterion (i.e.; perform a group analysis) \n choose yes. In this case, the analysis is done on the complete sample and on the subsamples. \n If you want the analysis for the complete sample only, choose no. The group analysis does not apply to robust statistics." , envir=.dico) + assign("desc_outliers_removal_implications" , "Delete all outliers removes all values beyond p(chi.two)< 0.001. Delete one observation at a time makes it possible to make a detailed analysis of each observation considered to be influential from the most extreme value. The procedure stops when no more observations are considered influential" , envir=.dico) + assign("txt_bilateral" , "Bilateral" , envir=.dico) + assign("desc_no_compatible_object_in_mem_for_aov" , "there is no object compatible with aov.plus in the memory of R. You must make an analysis of variance to the prerequisite" , envir=.dico) + assign("desc_this_function_means_and_sd_adjusted_interaction_effect_possible" , "This function provides the adjusted averages and standard errors as well as the corresponding graph. With the post hoc choice on interactions, you can test the interaction effects 2 a 2 and the simple effects. " , envir=.dico) + assign("txt_anova_plus" , "Anova plus" , envir=.dico) + assign("desc_center_and_center_reduce_explaination" , "Center allows you to have a zero average by keeping the chart-type. Centrer reduce corresponds to the formula of z. The average is 0 and the standard scale is 1. The lower probability corresponds to the probability of having a lower or equal z. The higher probability corresponds to the probability of having a higher or equal z" , envir=.dico) + assign("desc_proba_sum_is_not_one_or_not_enough_proba" , "The sum of probabilities is different from 1 or the number of probabilities does not correspond to the number of modes of the variable. Please enter a valid probability vector" , envir=.dico) + assign("desc_if_non_fixed_sample_poisson_law" , "If the total number is not fixed, it is hypothesized that the observations occur according to a fish law. Distribution on the levels of a factor occurs with a fixed probability. Distribution is a fish distribution" , envir=.dico) + assign("desc_distribution_is_joint_multinomial" , "The option *Fixed total effect* must be chosen if the null hypothesis is made that the distribution in each of the cells in the table is fixed. Distribution is a multinomial distribution attached" , envir=.dico) + assign("desc_distribution_is_independant_multinomial" , "The fixed total number option for lines* must be chosen if the number of staff for each line is the same, as if you want to ensure a matching between groups. Distribution is an independent multinomial distribution" , envir=.dico) + assign("desc_corr_detailed_analysis" , "the size analysis allows to have descriptive statistics, normalite tests, the cloud of points, \n robust statistics, all correlation coefficients. \n the correlation matrix allows to control the error of 1e species and is adapted for a large number of correlations \n the correlation comparison allows to compare 2 dependent or independent correlations \n The choice + other correlations + allows to have the tetrachoric and polychoric correlation" , envir=.dico) + assign("desc_corr_values_must_be_between_min_1_and_1" , "The correlation values must be between -1 and 1/n and the numbers must be positive integers" , envir=.dico) + assign("desc_you_can_choose_contrasts_you_want" , "You can choose the contrasts you want. Nevertheless, the rules concerning the application of contrasts must be respected. Contrasts can be specified manually. In this case, please select the contrasts" , envir=.dico) + assign("desc_square_matrix_rectangular_matrix" , "A square matrix is a matrix with all Correlations 2 to 2. A rectangular matrix is a matrix in which a first set of variables is correlated with a second set of variables." , envir=.dico) + assign("desc_complete_dataset_vs_identification_outliers_vs_without_outliers" , "the complete data represent the classical analysis on all usable data, the identification of the influential values allows to identify the observations which are considered statistically to influence the results. data analysis without influential values performs analysis after removal of influential values. This option stores in the memory of R a new database of data without influential value in an object bearing the name *nettoyees*" , envir=.dico) + assign("desc_welcome_in_easieR" , "Welcome in easieR - For more information, please visit:https://theeasierproject.wordpress.com/" , envir=.dico) + assign("ask_variables_type_for_anova" , "Please specify the type(s) of variable(s) you want to include in the analysis.\nYou can choose several (e.g., for mixed annova or ancova)" , envir=.dico) + assign("ask_correction_anova_contrasts" , "Correction?" , envir=.dico) + assign("txt_independant_groups" , "Independent groups" , envir=.dico) + assign("txt_covariables" , "Covariates" , envir=.dico) + assign("txt_cfa_information_default" , "information [information=default]" , envir=.dico) + assign("txt_cfa_continuity_correction_zero_keep_margins_default" , "correction of continuity [zero.keep.margins=default]" , envir=.dico) + assign("txt_cfa_estimator_ml_default" , "estimator [estimator=ml]" , envir=.dico) + assign("txt_cfa_groups_null_default" , "groups [group=NULL]" , envir=.dico) + assign("txt_cfa_test_standard_default" , "test" , envir=.dico) + assign("txt_cfa_standard_error_default" , "standard error" , envir=.dico) + assign("txt_cfa_observed_variabes_standardization_true_default" , "standardization of observed variables" , envir=.dico) + assign("txt_cfa_latent_variables_indicators_estimates_true_default" , "Estimation of indicators of latent variables [std.lv=FALSE]" , envir=.dico) + assign("desc_wls_corresponds_to_adf_plus_explaination_other_estimators" , "[WLS] corresponds to [ADF]. Estimators with extensions [M],[MV],[MVSF],[R] are robust versions of classic estimators [MV],[WLS], [DWLS], [ULS]" , envir=.dico) + assign("ask_observed_variables_intercept_zero" , "Intercept VO=0?" , envir=.dico) + assign("ask_latent_variables_intercept_zero" , "Intercept VL=0?" , envir=.dico) + assign("ask_how_to_treat_exaequo_rank" , "How do you want to treat ex-aequo? The method *warning* is the average between ex aequo (the most usual), *first* assigns the first ranking ex aequo to the first value in the data, *last* to the last, *min* assigns the minimum value to all ex aequo and *max* the maximum value. " , envir=.dico) + assign("desc_for_ordinal_and_dicho_varible_prefer_min_res" , "For the ordinal and dichomic variables, choose the method of minimum residus - minres - or least weighting squares - wls. For continuous variables, the maximum likelihood if normalite is respected - ml" , envir=.dico) + assign("desc_saturation_criterion_show_only_above_threshold" , "The saturation criterion allows the results table to show only saturation above the fixed threshold" , envir=.dico) + assign("desc_to_find_new_analysis_search_in_english" , "To find a new analysis, it is necessary to do your search in English. You can use several words in the search. A html page containing all the packages referring to the search analysis will open." , envir=.dico) + assign("txt_division" , "division" , envir=.dico) + assign("desc_if_you_select_both_operations_value_will_be_added_to_chose_cols" , "If you select both options at the same time, the specified value will be added to all the selected columns and then the selected columns will be added. To add a specific value to the total, select the column addition option only." , envir=.dico) + assign("desc_if_you_select_both_operations_value_will_be_multiplied_to_chose_cols" , "If you select both options at the same time, the specified value will be multiplied to all the selected columns and then the selected columns will be multiplied among them. To multiply a specific value in total, please select the column multipication option only." , envir=.dico) + assign("ask_chose_values_on_left_of_minus_symbol" , "Please select the values to the left of the symbol *minus*. If several variables are selected, the rules of the matrix calculation are applied. " , envir=.dico) + assign("desc_one_or_same_number_cols_on_both_sides_only" , "There shall be only one column or the number of columns to the right of the symbol *less* shall be equal to the number of columns to the left of the symbol *less*" , envir=.dico) + assign("ask_specify_exponant_value" , "Please specify the value of the exhibitor. NOTE: For roots, the exponent is the inverse value. For example, the square root is equal to 1/2, the cubic root 1/3..." , envir=.dico) + assign("desc_expression_must_be_correct_example" , "The expression must be correct. You can use the variables name directly the operators are +,-,*,/,^,(,). A correct expression would be:" , envir=.dico) + assign("ask_chose_relation_between_vars_regressions_log" , "Please choose the type(s) of relationships between variables. Additive effects take the form of y=X1+X2 while interaction effects take the form of Y=X1+X2+X1:X2" , envir=.dico) + assign("ask_variables_order_for_max_likelihood" , "The order of entry of the variables is important for the calculation of the maximum likelihood. Please specify the order of entry of variables" , envir=.dico) + assign("ask_integrate_probabilities_to_dataset" , "Do you want to integrate probabilities into your database?" , envir=.dico) + assign("ask_specify_other_options_regressions" , "Do you want to specify other options? You can select several. The selection methods allow you to select the best model based on statistical criteria. Hierarchical models allow to compare several models. Cross validations make it possible to check if a model is not dependent on the data. This option is to be used with selection methods. The group analysis makes it possible to achieve the same regression for subgroups. Influence measurements are the other measures usually used to identify influential values. " , envir=.dico) + assign("desc_possible_apply_multiple_selection_criterion" , "It is possible to apply several selection criteria simultaneously, involving or not several variables. Please specify the number of variables you want to apply one or more selection criteria. Please choose the variables on which you should apply a selection" , envir=.dico) + assign("desc_skew_and_kurtosis_between_1_and_3" , "Type of skew and kurtosis, shall be between 1 and 3:TXT" , envir=.dico) + assign("desc_with_two_equal_means_ratio_must_be_5_percent" , "With two equal averages, or almost equal, the error rate must be 5%. Gradually modify the gap between the scratch-types and see how the alpha error rate will be changed" , envir=.dico) + assign("desc_bilateral_superior_inferior_test_t" , "Bilateral analysis tests the existence of a difference. Superior choice test if average is strictly superior \n The lower choice tests the existence of a strictly inferior difference" , envir=.dico) + assign("txt_numeric_variables" , "Numeric variables" , envir=.dico) + assign("txt_select_language" , "Choose language" , envir=.dico) + assign("txt_dot_adjusted" , ".adjusted" , envir=.dico) + assign("txt_bca_inferior_limit" , "Bca lim inf" , envir=.dico) + assign("txt_bca_inferior_limit" , "Bca.lim.inf" , envir=.dico) + assign("txt_bca_superior_limit" , " Bca.lim.sup" , envir=.dico) + assign("txt_bca_superior_limit" , "Bca lim sup" , envir=.dico) + assign("txt_bca_superior_limit" , "Bca.lim.sup" , envir=.dico) + assign("txt_centered_dot_reduced" , "centered.reduced" , envir=.dico) + assign("txt_chi_dot_squared" , "chi.2" , envir=.dico) + assign("txt_chi_dot_squared_model" , "chi.2.model" , envir=.dico) + assign("txt_chi_dot_squared" , "chi.squared" , envir=.dico) + assign("txt_chi_dot_squared" , "chi.two" , envir=.dico) + assign("txt_chi_dot_squared_adjustment" , "chi.two adjustment" , envir=.dico) + assign("txt_pairwise_comparison" , "pairwaise comparison" , envir=.dico) + assign("txt_continuous" , "continuous" , envir=.dico) + assign("txt_greenhouse_geisser_huynn_feldt_correction" , "Correction : Greenhouse-Geisser & Hyunh-Feldt" , envir=.dico) + assign("txt_df" , "df" , envir=.dico) + assign("txt_df1" , "df1" , envir=.dico) + assign("txt_df_parenthesis_1" , "Df(1)" , envir=.dico) + assign("txt_df2" , "df2" , envir=.dico) + assign("txt_df_parenthesis_2" , "Df(2)" , envir=.dico) + assign("txt_df_denom" , "df.denom" , envir=.dico) + assign("txt_df_parenthesis_denom" , "Df (dnom)" , envir=.dico) + assign("txt_df_effect" , "df.effet" , envir=.dico) + assign("txt_df_num" , "df.num" , envir=.dico) + assign("txt_df_parenthesis_num" , "Df (num)" , envir=.dico) + assign("txt_df_predictor" , "df predictor" , envir=.dico) + assign("txt_df_residual" , "df.resid" , envir=.dico) + assign("txt_df_residuals" , "df.residuals" , envir=.dico) + assign("txt_delta_r_squared" , "Delta R.two" , envir=.dico) + assign("txt_error" , "Error" , envir=.dico) + assign("txt_error_BP" , "Error.BP" , envir=.dico) + assign("txt_error_spearman" , "Error.Spearman" , envir=.dico) + assign("txt_error_dot_standard_short" , "error.st" , envir=.dico) + assign("txt_error_dot_standard" , "error.standard" , envir=.dico) + assign("txt_error_dot_standard" , "Error.standard" , envir=.dico) + assign("txt_space" , "space" , envir=.dico) + assign("txt_estimator" , "estimator" , envir=.dico) + assign("txt_global_model_estimate" , "Global model estimation" , envir=.dico) + assign("txt_hf_p_value" , "HF.p.value" , envir=.dico) + assign("txt_ci_inferior" , "CI Inf" , envir=.dico) + assign("txt_ci_inferior_limit" , "CI lim inf" , envir=.dico) + assign("txt_ci_superior_limit" , "CI lim sup" , envir=.dico) + assign("txt_ci_superior" , "CI Sup" , envir=.dico) + assign("txt_large" , "large" , envir=.dico) + assign("txt_large_half" , "large - 0.5" , envir=.dico) + assign("txt_inferior_limit" , "lim.inf" , envir=.dico) + assign("txt_ci_inferior_limit_dot" , "lim.inf.CI" , envir=.dico) + assign("txt_ci_inferior_limit_dot" , "Lim.inf.CI" , envir=.dico) + assign("txt_ci_superior_limit" , "lim.sup" , envir=.dico) + assign("txt_ci_superior_limit_dot" , "lim.sup.CI" , envir=.dico) + assign("txt_ci_superior_limit_dot" , "Lim.sup.CI" , envir=.dico) + assign("txt_r_squared_matrix" , "matrix r.two" , envir=.dico) + assign("txt_truncated_m" , "M.truncated" , envir=.dico) + assign("txt_multiplied_by" , "multiplied.by" , envir=.dico) + assign("txt_dot_cleaned" , ".cleaned" , envir=.dico) + assign("txt_cleaned" , "cleaned" , envir=.dico) + assign("txt_bootstrap_dot_number" , "Number.bootstraps" , envir=.dico) + assign("txt_odd_ratio_dot" , "Odd.ratio" , envir=.dico) + assign("desc_install_bad_packages" , "Package.mal.installes" , envir=.dico) + assign("desc_install_correct_packages" , "packages.installes.correctement" , envir=.dico) + assign("txt_critical_p_corrected" , "crit.p.corrected" , envir=.dico) + assign("txt_percentile_inferior_limit_dot" , "Percentile.lim.inf" , envir=.dico) + assign("txt_percentile_superior_limit_dot" , "Percentile.lim.sup" , envir=.dico) + assign("txt_percentage_removed_obs" , "Percentage.obs.removed" , envir=.dico) + assign("txt_percent_removed_obs" , "Percent.obs.removed" , envir=.dico) + assign("txt_r_dot_square" , "r.square" , envir=.dico) + assign("txt_r_square" , "R square" , envir=.dico) + assign("txt_r_dot_square" , "R.square" , envir=.dico) + assign("txt_r_dot_two" , "r.two" , envir=.dico) + assign("txt_r_dot_two" , "R.two" , envir=.dico) + assign("txt_r_dot_two_adjusted" , "R.two.aj" , envir=.dico) + assign("txt_log_regression_dot" , "Regressions.logistic" , envir=.dico) + assign("txt_multiple_regressions_dot" , "regressions.multiples" , envir=.dico) + assign("txt_multiple_regressions_dot" , "Regressions.multiples" , envir=.dico) + assign("txt_rho_dot_square" , "rho.two" , envir=.dico) + assign("txt_critical_dot_threshold" , "critic.threshold" , envir=.dico) + assign("txt_critical_dot_threshold" , "Critic.threshold" , envir=.dico) + assign("txt_spearman_df" , "Spearman.df" , envir=.dico) + assign("txt_specificity" , "specifity" , envir=.dico) + assign("txt_ultrawide" , "ultra wide" , envir=.dico) + assign("txt_ultrawide" , "ultrawide" , envir=.dico) + assign("txt_ultrawide_val" , "ultra wide - 0.707" , envir=.dico) + assign("txt_absolute_dot_val" , "value.absolute." , envir=.dico) + assign("txt_contrast_dot_val" , "Value.contrast" , envir=.dico) + assign("txt_critical_dot_val" , "Value.critical" , envir=.dico) + assign("txt_p_dot_val" , "p.value" , envir=.dico) + assign("txt_p_dot_val_lilliefors" , "p.value Llfrs" , envir=.dico) + assign("txt_p_dot_val_sw" , "p.value SW" , envir=.dico) + assign("txt_test_dot_val" , "test.value" , envir=.dico) + assign("txt_z_dot_val" , "Z.value" , envir=.dico) + assign("txt_value" , "value" , envir=.dico) + assign("txt_vector_length_zero" , "vector of length zero" , envir=.dico) + assign("txt_kendall_w" , "Kendall.W" , envir=.dico) + assign("txt_synthesis" , "Synthesis" , envir=.dico) + assign("txt_truncated_mean_0_2" , "Test on truncated mean 0.2" , envir=.dico) + assign("txt_cramer_v_square" , "V.square" , envir=.dico) + assign("txt_effect_size_dot" , "Effect.size" , envir=.dico) + assign("txt_gg_p_value" , "GG.p.value" , envir=.dico) + assign("txt_var_explained_dot" , "Var.explained" , envir=.dico) + assign("txt_V_sq_" , "V.squared" , envir=.dico) +} \ No newline at end of file diff --git a/R/lang_fr_FR.R b/R/lang_fr_FR.R index 04ec674..6b47ca3 100644 --- a/R/lang_fr_FR.R +++ b/R/lang_fr_FR.R @@ -1,1136 +1,1132 @@ load_fr_FR <- function() { -.dico <<- new.env(parent = emptyenv()) -assign("ask_2x2_table", "tableau 2x2 ?",envir=.dico) -assign("ask_2x2_table_value", "Veuillez preciser la valeur pour les tableaux 2x2",envir=.dico) -assign("ask_add_a_value_to_empty_cells", "Faut-il ajouter une valeur aux cellules vides pour les correlations polychorique ? Pour specifier les valeurs,choisissez TRUE, sinon choisissez [default]",envir=.dico) -assign("ask_add_value_to_total", "voulez-vous encore ajouter une valeur au total ?",envir=.dico) -assign("ask_analysis_by_group", "Analyse par groupe?",envir=.dico) -assign("ask_analysis_on_complete_data_or_remove_outliers", "Desirez-vous l'analyse sur les donnees completes ou sur les donnees pour lesquelles les valeurs influentes ont ete enlevees ?",envir=.dico) -assign("ask_analysis_type", "Quelle analyse voulez-vous realiser?",envir=.dico) -assign("ask_are_frequences_free_parameters", "est-ce que les frequences des differents group est un parametre libre ? ",envir=.dico) -assign("ask_are_there_inversed_items", "Y a-t-il des items inverses ?",envir=.dico) -assign("ask_are_you_ready", "etes-vous pret?",envir=.dico) -assign("ask_baseline", "Quelle est la ligne de base?",envir=.dico) -assign("ask_bigger_tables_value", "Veuillez preciser la valeur pour les tableaux plus grand que 2x2",envir=.dico) -assign("ask_bootstrap_number_min_500", "veuillez preciser le nombre de bootstrap. Un minimum de 500 est idealement requis. Peut prendre du temps pour N>1000",envir=.dico) -assign("ask_bootstrap_numbers_1_for_none", "Veuillez preciser le nombre de bootstrap. Pour ne pas avoir de bootstrap, choisir 1",envir=.dico) -assign("ask_bootstraps_number", "Nombre de bootstrap ?",envir=.dico) -assign("ask_cancel_entered_value_not_num", "la valeur que vous avez entree n'est pas numerique.Voulez-vous annuler cette analyse ?",envir=.dico) -assign("ask_cauchy_apriori_distribution", "Veuillez preciser la distribution a priori de Cauchy",envir=.dico) -assign("ask_center", "Centrer?",envir=.dico) -assign("ask_center_numeric_variables", "Voulez-vous centrer les variables numeriques ? Centrer est generalement conseille (e.g., Schielzeth, 2010).",envir=.dico) -assign("ask_chi_squared_type", "Veuillez preciser le type de chi carre que vous souhaitez realiser.",envir=.dico) -assign("ask_choose_a_variable_with_at_least_two_modalities", "Une variable categorielle doit avoir au moins 2 modalites differentes. Veuillez choisir une variable avec au moins deux modalites",envir=.dico) -assign("ask_chose_analysis", "Veuillez choisir l'analyse que vous desirez realiser.",envir=.dico) -assign("ask_chose_categorial_ranking_factor", "Veuillez choisissez le facteur de classement categoriel.",envir=.dico) -assign("ask_chose_cols_corresponding_to_repeated_measures", "Veuillez choisir l'ensemble des colonnes correspondant aux modalites des variables en mesures repetees",envir=.dico) -assign("ask_chose_covariables", "Veuillez choisir la ou les covariables",envir=.dico) -assign("ask_chose_database", "Veuillez choisir la base de donnees",envir=.dico) -assign("ask_chose_defining_groups", "Veuillez choisir la definissant les groupes",envir=.dico) -assign("ask_chose_dependant_variable", "Veuillez choisir la variable dependante.",envir=.dico) -assign("ask_chose_first_judge", "Veuillez choisir le premier juge",envir=.dico) -assign("ask_chose_independant_group_variables", "Veuillez choisir les variable-s a groupes independants",envir=.dico) -assign("ask_chose_interaction_model_predictors", "Veuillez choisir les predicteurs a entrer dans le modele d'interaction. Il est necessaire d'avoir au moins deux variables",envir=.dico) -assign("ask_chose_manifest_variables_at_least_three", "Veuillez choisir les variables manifestes que vous desirez analyser. Vous devez choisir au moins 3 variables",envir=.dico) -assign("ask_chose_ranking_categorial_factor", "Veuillez choisir le facteur de classement categoriel.",envir=.dico) -assign("ask_chose_rotation", "Veuillez choisir le type de rotation. Oblimin est adapte en sciences humaines",envir=.dico) -assign("ask_chose_sample_variables", "Veuillez choisir la ou les variables definissant les effectifs",envir=.dico) -assign("ask_chose_second_judge", "Veuilez choisir le second juge",envir=.dico) -assign("ask_chose_selection_method", "Veuillez choisir la methode de selection que vous souhaitez utiliser",envir=.dico) -assign("ask_chose_the_working_dir", "Veuillez choisir le repertoire de travail",envir=.dico) -assign("ask_chose_variables_at_least_five", "Veuillez choisir les variables que vous desirez analyser. Vous devez choisir au moins 5 variables",envir=.dico) -assign("ask_chose_variables_at_least_three", "Veuillez choisir les variables que vous desirez analyser. Vous devez choisir au moins 3 variables",envir=.dico) -assign("ask_chose_variable", "Veuillez choisir les variables que vous desirez analyser.",envir=.dico) -assign("ask_chose_variable_x_axis", "Veuillez choisir la variable en abcisse",envir=.dico) -assign("ask_chose_variable_y_axis", "Veuillez choisir la variable en ordonnee",envir=.dico) -assign("ask_coding_criterion", "Quel critere de codage voulez-vous ?",envir=.dico) -assign("ask_col_separation_index", "Lors de l'enregistrement de votre fichier, quel est l'indice de separation des colonnes ?",envir=.dico) -assign("ask_complete_or_outliers", "Voulez-vous realiser les analyses sur les donnees completes ou sur les donnees sans les valeurs influentes ?",envir=.dico) -assign("ask_constant_parameters", "Parametres constants ?",envir=.dico) -assign("ask_continue", "Continuer ?",envir=.dico) -assign("ask_contrast_must_respect_ortho", "Les contrastes doivent respecter l orthogonalite. Voulez-vous continuer ?",envir=.dico) -assign("ask_control_variables", "Veuillez preciser la ou les variables a controler",envir=.dico) -assign("ask_convert_dependant_variable_to_dichotomic", "voulez-vous convertir la variable dependante en une variable dichotomique, ?",envir=.dico) -assign("ask_correction_desired", "Veuillez preciser le type de correction de la probabilite que vous desirez realiser",envir=.dico) -assign("ask_correction_type", "Type de correction ?",envir=.dico) -assign("ask_correlated_or_orthogonal_factors", "Est-ce que les facteurs sont correles (FALSE) ou sont-ils orthogonaux (TRUE)?",envir=.dico) -assign("ask_correlation_matrix_could_not_be_computed", "La matrice de correlation n'a pu etre realisee. Voulez-vous reessayer ?",envir=.dico) -assign("ask_correlation_type", "Veuillez choisir le type de correlations que vous desirez realiser. Pour les variables dichotomiques, les correlations seront des correlations tetrachoriques",envir=.dico) -assign("ask_corr_or_partial_correlations", "Correlations ou correlations partielles?",envir=.dico) -assign("ask_could_not_converge_model_verify_correlation_matrix", "Nous n'avons pas reussi a faire converger le modele. Veuillez verifier votre matrice de correlations et reessayer avec d'autres parametres",envir=.dico) -assign("ask_could_not_finish_analysis_respecify_parameters", "Nous n'avons pas pu terminer correctement l'analyse. Veuillez tenter de respecifier les parametres",envir=.dico) -assign("ask_covariables", "Covariable-s ?",envir=.dico) -assign("ask_criterion_for_dichotomy", "Veuillez specifier le critere sur lequel vous souhaitez dichotomiser votre variable.Vous pouvez utiliser la mediane ou choisir un seuil specifique.",envir=.dico) -assign("ask_criterion_for_obs_to_keep", "Veuillez specifier les criteres des observations que vous desirez conserver/garder.",envir=.dico) -assign("ask_criterion_for_variable", "Quel critere voulez-vous utiliser pour la variable",envir=.dico) -assign("ask_data", "Donnees ?",envir=.dico) -assign("ask_data_format", "Quel est le format de vos donnees?",envir=.dico) -assign("ask_decimal_symbol", "Si certaines donnees contiennent des decimales, quel est le symbole indiquant la decimale ?",envir=.dico) -assign("ask_denominator_variable_or_value", "Le denominateur est-il une variable ou une valeur ? ",envir=.dico) -assign("ask_denominator_variable", "Veuillez selectionner la variable au denominateur ",envir=.dico) -assign("ask_dependant_variable_with_less_than_three_val_verify_dataset", "La variable dependante a moins de trois valeurs differentes. Verifiez vos donnees ou l'analyse que vous tentez de realiser n'est pas pertinente.",envir=.dico) -assign("ask_did_not_specify_nb_factors_repeated_measure_exit", "Vous n avez pas precise le nombre de facteurs en mesure repetee, voulez-vous quitte ?",envir=.dico) -assign("ask_distribution", "Distribution ?",envir=.dico) -assign("ask_distribution_type", "Quelle distribution voulez-vous ?",envir=.dico) -assign("ask_empty_cells", "Cellules vides ?",envir=.dico) -assign("ask_enter_different_values", "Veuillez entrer les differentes valeurs",envir=.dico) -assign("ask_enter_number_of_to_be_removed_variable", "Vous devez entrer le numero permettant de savoir quelle observation doit etre supprimee.",envir=.dico) -assign("ask_exit_because_of_alpha_on_non_matrix", "Vous essayez de faire un alpha sur autre chose qu'un matrice. Voulez-vous sortir de cette analyse?",envir=.dico) -assign("ask_exit_no_lower_bound_specified", "Vous n'avez pas precise la limite inferieure. Voulez-vous quitter la selection ?",envir=.dico) -assign("ask_exit_no_upper_bound_specified", "Vous n'avez pas precise la limite superieure. Voulez-vous quitter la selection ?",envir=.dico) -assign("ask_exportation_filename", "Quel nom voulez-vous attribuer au fichier ?",envir=.dico) -assign("ask_factorial_scores", "Scores factoriels?",envir=.dico) -assign("ask_factors_number_for_hierarchical_structure", "Veuillez preciser le nombre de facteurs de la structure hierarchique.",envir=.dico) -assign("ask_factors_ortho", "Orthogonalite des facteurs ?",envir=.dico) -assign("ask_factors_superior_level", "Nombre de facteurs du niveau superieur ?",envir=.dico) -assign("ask_family", "Veuillez preciser la famille (i.e. forme de la distribution).",envir=.dico) -assign("ask_file_format", "Format du fichier?",envir=.dico) -assign("ask_file_format_to_import", "Dans quel format est enregistre votre fichier ?",envir=.dico) -assign("ask_first_categorical_set", "Veuillez choisir le premier set de facteur(s) categoriel(s)",envir=.dico) -assign("ask_first_variables_set", "Veuillez choisir le premier jeu de variables",envir=.dico) -assign("ask_fixed_covariables", "Covariables fixees ?",envir=.dico) -assign("ask_freq_constance", "Constance de la frequence ?",envir=.dico) -assign("ask_f_value", "Quelle valeur du F voulez-vous utiliser ?",envir=.dico) -assign("ask_group_variable", "Variable [groupes] ?",envir=.dico) -assign("ask_headers_in_database", "Est-ce que le nom des variables est sur la premiere ligne de votre base de donnees ? Choisir TRUE si c'est le cas",envir=.dico) -assign("ask_hierarchical_analysis", "Faut-il realiser une analyse hierarchique ?",envir=.dico) -assign("ask_how_many_modalities", "Combien de modalites",envir=.dico) -assign("ask_how_standard_error_must_be_estimated", "Comment l'erreur standard doit-elle etre estimee ?",envir=.dico) -assign("ask_how_to_remove", "Comment voulez-vous les supprimer?",envir=.dico) -assign("ask_how_to_treat_missing_values", "Des valeurs manquantes ont ete detectees. Comment voulez-vous les traiter ? Garder l'ensemble des observations peut biaiser les resultats.",envir=.dico) -assign("ask_id_variable", "Veuillez choisir la variable identifiant les participants",envir=.dico) -assign("ask_imitate", "Imiter ?",envir=.dico) -assign("ask_independant_variable", "Veuillez choisir la variable independante.",envir=.dico) -assign("ask_information_matrix", "Matrice d'information ?",envir=.dico) -assign("ask_integrate_factorial_scores_in_data", "Voulez-vous que les scores factoriels soient integres a vos donnees ?",envir=.dico) -assign("ask_inversed_items", "items inverses?",envir=.dico) -assign("ask_is_model_correct", "Est-ce que votre modele est correct ?",envir=.dico) -assign("ask_latent_variables_number", "Veuillez preciser le nombre de variables latentes",envir=.dico) -assign("ask_level", "Veuillez choisir le niveau",envir=.dico) -assign("ask_likelihood", "Vraisemblance ?",envir=.dico) -assign("ask_linebase_modalities", "Veuillez specifier la/les modalite(s) qui serviront pour la ligne de base (e.g. 0). Les autres modalites seront regroupes dans la categorie 1.",envir=.dico) -assign("ask_log_base", "Veuillez preciser la base du logarithme.Pour obtenir e, tapez e",envir=.dico) -assign("ask_lower_bound", "Limite inferieure?",envir=.dico) -assign("ask_mcnemar_repeated_measure", "Test de McNemar : les modalites ne sont pas les memes pour le test de McNemar. Est-ce bien un facteur en mesure repetee ?",envir=.dico) -assign("ask_mediation_type", "Quel type de mediation ?",envir=.dico) -assign("ask_mediator", "veuillez choisir le mediateur",envir=.dico) -assign("ask_minus_left_hand_variables", "Veuillez selectionner la -les- variable(s) a gauche du symbole *moins*",envir=.dico) -assign("ask_minus_right_hand_variables", "Veuillez selectionner la -les- variable(s) a droite du symbole *moins*.",envir=.dico) -assign("ask_minus_right_operand_variable_or_value", "Les valeurs a droite du symbole *moins* sont-elles une/des variable(s) ou une valeur ? ",envir=.dico) -assign("ask_missing_values_detected_what_to_do", "Des valeurs manquantes ont ete detectees. Comment voulez-vous les traiter ?",envir=.dico) -assign("ask_missing_values_treatment", "Traitement des valeurs manquantes ?",envir=.dico) -assign("ask_missing_values_value_na_on_empty", "Si certaines donnees sont manquantes, comment sont-elles definies ? Vous pouvez laisser NA si les cellules sont vides",envir=.dico) -assign("ask_missing_value_treatment", "Nombre de valeurs manquantes par variable. Comment voulez-vous les traiter ?",envir=.dico) -assign("ask_modalities_for_variable", "Quelles modalites voulez-vous selectionner pour la variable",envir=.dico) -assign("ask_modalities_to_keep", "Veuillez selectionner les modalites que vous desirez conserver.",envir=.dico) -assign("ask_name_for_dataset", "Quel nom voulez-vous donner aux donnees ?",envir=.dico) -assign("ask_name_to_attribute_to", "Quel nom voulez-vous attribuer a",envir=.dico) -assign("ask_nb_factors_repeated_measure", "Combien de facteurs en mesure repetee ?",envir=.dico) -assign("ask_new_variable_name", "Quel nom voulez-vous attribuer a la nouvelle variable ? ",envir=.dico) -assign("ask_norm_value", "Quelle est la valeur de la norme ?",envir=.dico) -assign("ask_not_enough_obs_verify_dataset", "Il n'y a pas assez d'observations pour realiser l'analyse. Veuillez verifier vos donnees net vous assurer qu'il y a au moins trois observations par modalite de chaque facteur",envir=.dico) -assign("ask_null_hypothesis_tests_or_bayesian_factors", "Voulez-vous les tests d'hypothees nuls ou/et les facteurs bayesiens ?",envir=.dico) -assign("ask_numerator_variable_or_value", "Le numerateur est-il une variable ou une valeur ?",envir=.dico) -assign("ask_numerator_variable", "Veuillez selectionner la variable au numerateur ",envir=.dico) -assign("ask_obs_to_remove", "Quelle observation souhaitez-vous retirer des analyses ? '0'=aucune",envir=.dico) -assign("ask_other_options", "Autres options?",envir=.dico) -assign("ask_ponderate_analysis_by_a_sample_var", "Faut-il ponderer l'analyse par une variable effectif ?",envir=.dico) -assign("ask_positive_val_variable_or_value", "Les valeurs positives sont-elles une/des variable(s) ou une valeur ? ",envir=.dico) -assign("ask_predictor", "veuillez preciser le predicteur",envir=.dico) -assign("ask_press_enter_to_continue", "Appuyez [entree] pour continuer",envir=.dico) -assign("ask_probabilities_for_modalities", "Veuillez entrer les probabilites correspondant a chaque modalite de la variable.",envir=.dico) -assign("ask_probabilities", "Probabilites ?",envir=.dico) -assign("ask_probability_value", "Quelle valeur de la probabilite voulez-vous utiliser ?",envir=.dico) -assign("ask_redefine_analysis_because_modalities_product_is_superior_to_obs", "Le produit des modalites des variables definissant les groupes est superieur au nombre de vos observations. Il faut au moins une observation par combinaison de modalites de vos variables. Veuillez redefinir votre analyse",envir=.dico) -assign("ask_regroup_modalities", "Voulez-vous faire des regroupements entre les modalites ?",envir=.dico) -assign("ask_rename_variables_with_special_char", "Certaines noms de variables contiennent des caracteres speciaux pouvant creer des bugs. Voulez-vous renommer ces variables ?",envir=.dico) -assign("ask_results_desired", "Quels resultats voulez-vous obtenir ?",envir=.dico) -assign("ask_results_output", "Sorties de resultats ?",envir=.dico) -assign("ask_sampling_type", "Quel type d'echantillonnage avez-vous realise pour votre analyse ?",envir=.dico) -assign("ask_save_results_in_external_file", "Desirez-vous sauvegarder les resultats dans un fichier externe ?",envir=.dico) -assign("ask_second_categorical_set", "Veuillez choisir le second set de facteur(s) categoriel(s)",envir=.dico) -assign("ask_second_mediator", "veuillez preciser le second mediateur.",envir=.dico) -assign("ask_second_variables_set", "Veuillez choisir le second jeu de variables",envir=.dico) -assign("ask_selection_method", "Quel methode faut-il appliquer pour la methode de selection ?",envir=.dico) -assign("ask_select_variables_or_modalities_of_repeated_measure_variable", "Veuillez selectionner les variables OU les modalites de la (des) variables a mesure(s) repetee(s).",envir=.dico) -assign("ask_separation_value", "Veuillez preciser la valeur de separation",envir=.dico) -assign("ask_shorten_long_variables_names", "Certaines variables ont des noms particulierement longs pouvant gener la lecture. Voulez-vous les raccourcir?",envir=.dico) -assign("ask_should_intercept_of_latent_variable_be_fixed_to_zero", "Est-ce que l'intercept des variables latentes doit etre fixe a 0 ?",envir=.dico) -assign("ask_should_intercept_of_obs_variables_be_fixed_to_zero", "Faut-il fixer l'intercept des variables observees a 0 ?",envir=.dico) -assign("ask_simple_or_partial_corr", "Correlations simples ou partielles?",envir=.dico) -assign("ask_specify_all_parameters_or_imitate_specific_software", "Voulez-vous specifier tous les parametres [default] ou imiter un logiciel particulier ?",envir=.dico) -assign("ask_specify_datasheet_to_import", "Veuillez specifier la feuille de calcul que vous souhaitez importer",envir=.dico) -assign("ask_specify_groups", "Specifier groupes ?",envir=.dico) -assign("ask_specify_inverted_item", "Veuillez preciser les items inverses",envir=.dico) -assign("ask_specify_likelihood", "Veuillez preciser la vraisemblance.",envir=.dico) -assign("ask_specify_norm_value", "Veuillez specifier la valeur de la norme",envir=.dico) -assign("ask_specify_other_options", "Specifier les autres options?",envir=.dico) -assign("ask_specify_sample", "Specifier effectifs ?",envir=.dico) -assign("ask_specify_sample_variable", "Specifier la vriable effectifs ?",envir=.dico) -assign("ask_specify_variables_for_ranks", "Veuillez preciser les variables dont vous souhaiter faire les rangs",envir=.dico) -assign("ask_specify_variables_type", "Veuillez preciser le(s) type(s) de variable(s) que vous souhaitez inclure dans l'analyse.nVous pouvez en choisir plusieurs (e.g., pour anova mixte ou des ancova",envir=.dico) -assign("ask_standard_error", "Erreur standard ?",envir=.dico) -assign("ask_standardization", "Standardisation ?",envir=.dico) -assign("ask_standardization_vl", "Standardisation VL?",envir=.dico) -assign("ask_standardize_obs_variables_before", "Faut-il standardise (i.e. centrer reduire) les variables observees au prelable (TRUE) ou non (FALSE) ?",envir=.dico) -assign("ask_statistical_approach", "Approche statistique ?",envir=.dico) -assign("ask_subgroups", "Vous pouvez decomposer les statistiques descriptives par sous-groupe en choisissant une ou plusieurs variables categorielles. Voulez-vous specifier les sous-groupes ?",envir=.dico) -assign("ask_sufficient_matrix_for_afe", "La matrice est-elle satisfaisante pour une AFE ?",envir=.dico) -assign("ask_suppress_this_obs", "Voulez-vous supprimer cette observation ?",envir=.dico) -assign("ask_test_hierarchical_structure", "Desirez-vous tester une structure hierarchique ? L'omega teste une structure hierarchique et une AFE hierarchique seront realisees.",envir=.dico) -assign("ask_time1", "Veuillez choisir le temps 1.",envir=.dico) -assign("ask_time2", "Veuillez choisir le temps 2.",envir=.dico) -assign("ask_transform_numerical_to_categorial_variables", "Vous devez utiliser des variables categorielles. Voulez-vous transformer les variables numeriques en variables categorielles ?",envir=.dico) -assign("ask_troncature_threshold", "Veuillez fixer le seuil de la troncature",envir=.dico) -assign("ask_t_test_type", "Veuillez preciser le type de test t que vous souhaitez realiser.",envir=.dico) -assign("ask_type_correlation", "Veuillez preciser le type de correlation que vous souhaitez realiser.",envir=.dico) -assign("ask_upper_bound", "Limite superieure?",envir=.dico) -assign("ask_value_for_missing_values", "Par quelle valeur sont definies les valeurs manquantes ?",envir=.dico) -assign("ask_value_for_operation", "Veuillez specifier la valeur pour realiser votre operation mathematique.",envir=.dico) -assign("ask_value_for_selected_obs", "Veuillez preciser la valeur sur laquelle les observations doivent etre selectionnees.",envir=.dico) -assign("ask_value", "Precisez la valeur?",envir=.dico) -assign("ask_variabels_for_polyc_tetra_mixt_corr", "Veuillez choisir les variables dont il faut realiser les correlations polychorique/tetrachorique/mixte.",envir=.dico) -assign("ask_variable_at_this_point", "Quelle variable a cette etape",envir=.dico) -assign("ask_variable_name", "Nom de la nouvelle variable ?",envir=.dico) -assign("ask_variables_for_description_statistics", "veuillez choisir les variables pour lesquelles vous desirez obtenir les statistiques descriptives",envir=.dico) -assign("ask_variables_groups", "Variable(s) groupes ?",envir=.dico) -assign("ask_variables_names", "Nom de variables?",envir=.dico) -assign("ask_variables_to_abs", "Veuillez selectionner les variables dont il faut faire la valeur absolue ",envir=.dico) -assign("ask_variables_to_add", "Veuillez selectionner les variables a additionner.",envir=.dico) -assign("ask_variables_to_exp", "Veuillez selectionner les variables auxquelles s'applique l'exposant ",envir=.dico) -assign("ask_variables_to_log", "Veuillez selectionner les variables dont il faut faire le logarithme ",envir=.dico) -assign("ask_variables_to_mean", "Veuillez selectionner les variables a moyenner ",envir=.dico) -assign("ask_variables_to_multiply", "Veuillez selectionner les variables a multiplier. ",envir=.dico) -assign("ask_variables_to_order", "Veuillez selectionner la (les) variable(s) a trier",envir=.dico) -assign("ask_variables_type_correlations", "Veuillez preciser le type de variables. Des correlations tetra/polychoriques seront realisees sur les variables dichotomiques/ordinales et Bravais-Pearson sur les variables continues",envir=.dico) -assign("ask_variables_types_correlations", "Veuillez preciser le type de variables. Des correlations tetra/polychoriques seront realisees sur les variables ordinales et Bravais-Pearson sur les variables continues",envir=.dico) -assign("ask_variables_used_for_exponential", "Veuillez selectionner les variables servant a l'exponentiel ",envir=.dico) -assign("ask_variables_used_for_groups", "Veuillez choisir la ou les variables definissant les groupes",envir=.dico) -assign("ask_variable", "Variable a analyser ?",envir=.dico) -assign("ask_wanted_model", "Veuillez choisir le modele que vous desirez analyser avec aov.plus",envir=.dico) -assign("ask_what_do_you_want", "Que voulez-vous ?",envir=.dico) -assign("ask_what_is_your_choice", "Quel est votre choix ?",envir=.dico) -assign("ask_what_to_print", "Que voulez-vous afficher ?",envir=.dico) -assign("ask_which_algorithm", "Quel algorithme desirez-vous?",envir=.dico) -assign("ask_which_analysis_you_looking_for", "Quelle analyse recherchez vous ?",envir=.dico) -assign("ask_which_baseline", "Quelle est la ligne de base ?",envir=.dico) -assign("ask_which_constant_parameters", "Quels sont les parametres que vous desirez maintenir constants ?",envir=.dico) -assign("ask_which_contrasts_for_variable", "Quels contrastes pour la variable",envir=.dico) -assign("ask_which_contrasts", "Quel types de contraste voulez-vous ?",envir=.dico) -assign("ask_which_correction", "Quelle correction de la probabilite voulez-vous appliquer ? Pour ne pas appliquer de correction, choisir +none+",envir=.dico) -assign("ask_which_data_to_analyse", "Quelles donnees voulez-vous analyser?",envir=.dico) -assign("ask_which_data_to_export", "Quelles donnees voulez-vous exporter ?",envir=.dico) -assign("ask_which_estimator", "Quelles estimateur ?",envir=.dico) -assign("ask_which_factors_combination_for_adjust_means", "Pour quelle combinaison de facteurs desirez-vous afficher les moyennes ajustees ?",envir=.dico) -assign("ask_which_information_matrix_for_standard_error_estimation", "Sur quelle matrice d'information doit se realiser l'estimation des erreurs standards ?",envir=.dico) -assign("ask_which_mathematical_operation", "Veuillez choisir l'operation mathematique que vous desirez realiser ",envir=.dico) -assign("ask_which_operation", "Quelle operation voulez-vous?",envir=.dico) -assign("ask_which_options", "Quelles options ?",envir=.dico) -assign("ask_which_options_to_specify", "Quelles options voulez-vous specifier ?",envir=.dico) -assign("ask_which_output", "Quel format souhaitez-vous ?",envir=.dico) -assign("ask_which_output_results", "Quelles sorties de resultats souhaitez-vous ?",envir=.dico) -assign("ask_which_regression_type", "Quel type de regression ?",envir=.dico) -assign("ask_which_results_warning_on_default_output", "Quels resultats souhaitez-vous ? Attention : les sorties par defaut ne peuvent etre sauvegrdees. Si vous voulez une sauvarde, choisissez le detail",envir=.dico) -assign("ask_which_rotation", "Quelle rotation",envir=.dico) -assign("ask_which_saturation_criterion", "Quel est le critere de saturation que vous voulez utiliser ?",envir=.dico) -assign("ask_which_size_effect", "Quelle taille d effet voulez-vous ?",envir=.dico) -assign("ask_which_squared_sum", "Quelle somme des carres voulez-vous utiliser ?",envir=.dico) -assign("ask_which_test", "Quel test voulez-vous utiliser ?",envir=.dico) -assign("ask_which_value_for_operation", "Quelle valeur voulez-vous pour votre operation mathematique ?",envir=.dico) -assign("ask_which_variable_identifies_participants", "Quelle est la variable identifiant les participants ?",envir=.dico) -assign("ask_you_did_not_chose_a_variable_continue_or_abort", "Vous n avez pas choisi de variable. Voulez-vous continuer (ok) ou abandonner (annuler) cette analyse ?",envir=.dico) -assign("desc_abs_val_applied_to_var", "la valeur absolue a ete applique a la variable",envir=.dico) -assign("desc_accepted_values_are_z_and_grubbs", "Les valeurs admises pour critere sont z et Grubbs ",envir=.dico) -assign("desc_all_tests_description", "le modele parametrique renvoie l'anova classique,le non parametrique calcule le test de Kruskal Wallis nsi c'est un modele a groupes independants, ou une anova de Friedman pour un modele en Mesures repetees.nLe modele bayesien est l'equivalent du modele teste dans l'anova en adoptant une approche bayesienne,nles statistiques robustes sont des anovas sur des medianes ou les moyennes tronquees avec ou sans bootstrap.",envir=.dico) -assign("desc_alpha_increased_with_value_equals_to", "vous multipliez l'erreur de 1e espece. Le risque de commettre une erreur de 1e espece est de",envir=.dico) -assign("desc_analysis_aborted", "L'analyse n'a pas pu aboutir",envir=.dico) -assign("desc_and", "et",envir=.dico) -assign("desc_and_variabe", "et la variable",envir=.dico) -assign("desc_and_variable_y", "et la variable ",envir=.dico) -assign("desc_applied_correction_is", "la correction appliquee est la correction de",envir=.dico) -assign("desc_at_least_10_obs_needed", "Il faut au moins 10 observations plus le nombre de variables pour realiser l'analyse. Verifiez vos donnees.",envir=.dico) -assign("desc_at_least_independant_variables_or_repeated_measures", "Il est indispensable d'avoir au minimum des variables a groupes independants ou en mesures repetees",envir=.dico) -assign("desc_at_least_on_contrast_matrix_incorrect", "Au moins une de vos matrices de contrastes n'est pas correcte.",envir=.dico) -assign("desc_at_least_one_denom_is_zero", "Au moins une des valeurs au denominateur est un 0. La valeur renvoyee dans ce cas est infinie - inf",envir=.dico) -assign("desc_at_least_one_non_numeric", "au moins une variable n'est pas numerique",envir=.dico) -assign("desc_at_least_one_var_is_not_num", "au moins une des variables n'est pas numerique",envir=.dico) -assign("desc_authorized_values_for_contrasts", "Les valeurs autorisees pour les contrastes sont +none+ pour aucun contraste, +pairwise+ pour les comparaisons 2 a 2 ou une liste de coefficients de contrastes",envir=.dico) -assign("desc_avoid_spaces_and_punctuations", "Evitez les espaces ainsi que les signes de ponctuations, a l'exception . et _ ",envir=.dico) -assign("desc_bayesian_factors_could_not_be_computed", "Les facteurs bayesiens n'ont pas pu etre calcules.",envir=.dico) -assign("desc_beyond_with_lower_and_upper", "au-dela (avec une limite inferieure et superieure",envir=.dico) -assign("desc_biased_results_risk_because_of_low_number_of_obs_or_zero_variance", "il y a moins de 3 observations pour un des groupes ou \nla variance d'au moins un groupe vaut 0. Les resultats risquent d'etre considerablement biaises",envir=.dico) -assign("desc_bootstraps_number_must_be_positive", "Le nombre de bootstrap doit etre un nombre entier positif",envir=.dico) -assign("desc_bootstrap_t_adapt_to_truncated_mean", "Le bootstrap-t method est un bootstrap adapte au calcul de la moyenne tronquee",envir=.dico) -assign("desc_cannot_compute_mahalanobis", "Desole, nous ne pouvons pas calculer la distance de Mahalanobis sur vos donnees. Les analyses seront resalisees sur les donnees completes",envir=.dico) -assign("desc_cannot_group_variables_because_not_described", "Vous ne pouvez pas avoir de variable *groupes* etant donne que toutes les variables doivent etre decrites",envir=.dico) -assign("desc_cannot_have_both_within_RML_arguments", "Vous ne pouvez pas avoir a la fois des arguments dans within et RML",envir=.dico) -assign("desc_cells_for_mcnemar", "Les cellules utilisees pour le calcul du McNemar sont celles de la 1e ligne 2e colonne et de la 2e ligne 1e colonne",envir=.dico) -assign("desc_centered_data_schielzeth_recommandations", "En accord avec les recommandations de Schielzeth 2010, les donnees ont ete prealablement centrees",envir=.dico) -assign("desc_chi_squared_adjustment_on_variable_x", "chi deux d'ajustement sur la variable",envir=.dico) -assign("desc_close_browser_to_come_back", "Ne pas oublier de fermer la fenetre htmlt (firexfox, chrome, internet explorer...) pour revenir Ă  la session R",envir=.dico) -assign("desc_cross_validation_is_not_yet_supported", "La validation croisee n'est pas encore disponible.",envir=.dico) -assign("desc_data_saved_in", "les donnees sont sauvegardees dans",envir=.dico) -assign("desc_data_succesfully_ordered", "les donnees ont ete triees correctement ",envir=.dico) -assign("desc_descriptive_statistics_on", "Statistiques descriptives sur",envir=.dico) -assign("desc_distribution_is_hypergeometric_when", "L'option *Effectif total fixe pour les lignes et les colonnes* lorsque les totaux pour les lignes et les colonnes sont fixes.La distribution est hypergeometrique",envir=.dico) -assign("desc_each_participant_must_appear_only_once_", "Chaque participant doit apparaĂ®tre une et une seule fois pour chaque combinaison des modalites",envir=.dico) -assign("desc_effect_size_by_walker", "La taille d'effet est calculee a partir de la formule proposee par Walker, 2003",envir=.dico) -assign("desc_entered_value_not_num", "la valeur entree n'est pas numerique",envir=.dico) -assign("desc_exponential_has_been_applied_to_var", "l'exponentiel a ete applique a la variable",envir=.dico) -assign("desc_facotrs_must_be_positive_int_inferior_to_variables_num", "Le nombre de facteur doit etre un entier positif inferieur au nombre de variables",envir=.dico) -assign("desc_fb_ratio_between_models", "Rapport des FB entre les modeles",envir=.dico) -assign("desc_file_is_saved_in", "le fichier est sauvegarde dans",envir=.dico) -assign("desc_flattening_and_asymetry_configurable", "Vous pouvez specifier la troncature et les parametres pour l'aplatissement et l'asymetrie en choisissant autres options",envir=.dico) -assign("desc_for_bigger_samples_bootstrap_t_prefered", "Pour des echantillons plus importants, les boostrap utilisant la methode t doit etre preferee.",envir=.dico) -assign("desc_for_easier_to_work", "Pour que easieR fonctionne correctement, il faut installer Pandoc disponible Ă  l'url suivant : https://github.com/jgm/pandoc/releases",envir=.dico) -assign("desc_graph_thickness_gives_density", "L'epaisseur du graphique donne la densite, permettant de mieux cerner la distribution.",envir=.dico) -assign("desc_has_been_added_to", "a ete ajoutee a",envir=.dico) -assign("desc_has_been_added_to_variable", "a ete ajoutee a la variable",envir=.dico) -assign("desc_has_been_applied_to_variable", "a ete applique a la variable",envir=.dico) -assign("desc_has_been_put_to_the_power_of", "a ete elevee a la puissance",envir=.dico) -assign("desc_has_multiplied_variables", "a multiplie la -les- variable-s",envir=.dico) -assign("desc_highest_value", "Valeur la plus elevee",envir=.dico) -assign("desc_how_to_cite_easier", "Pour citer easieR dans vos publication / to cite easieR in you publications use :\n Stefaniak, N. (2020). ",envir=.dico) -assign("desc_identical_option_total_sample", "L'option Effectif total fixe pour les colonnes* est identique a la precedente pour les colonnes",envir=.dico) -assign("desc_identified_outliers", "Observations considerees comme influentes",envir=.dico) -assign("desc_if_true_covariates_as_fixed", "Si vrai, on considere les covaries exogenes comme fixes, sinon on les considere comme aleatoires et leurs parametres sont libres",envir=.dico) -assign("desc_if_true_latent_residuals_one", "Si vrai, les residus des variables latentes sont fixes a 1, sinon les parametres de la variable latente sont estimes en fixant le premier indicateur a 1",envir=.dico) -assign("desc_improve_likelihood_for_each_variable", "Amelioration de la vraisemblance pour chaque variable",envir=.dico) -assign("desc_incorrect_model", "Le modele specifie est incorrect. Verifiez vos variables et votre modele",envir=.dico) -assign("desc_instable_model_high_multicolinearity", "La multicolinearite est trop importante. Le modele est instable",envir=.dico) -assign("desc_insufficient_obs", "Le nombre d'observations est insuffisant pour mener a bien les analyses pour ce groupe",envir=.dico) -assign("desc_insufficient_sample_for_combinations_between", "Les effectifs sont insuffisants pour le nombre de combinaisons entre la variable ",envir=.dico) -assign("desc_in_that_case_non_parametric_is_classical_chi_squared", "Dans ce cas, le test non parametrique est le test de chi carre classique",envir=.dico) -assign("desc_issue_in_hierarchical_regression", "Un probleme a ete identifie dans les etapes de votre regression hierarchique",envir=.dico) -assign("desc_kmo_could_not_be_computed_verify_matrix", "Le KMO n'a pas pu etre calcule. Verifiez votre matrice de correlation.",envir=.dico) -assign("desc_kmo_must_strictly_be_more_than_a_half", "le KMO doit absolument etre superieur a 0.5",envir=.dico) -assign("desc_kmo_on_matrix_could_not_be_obtained", "Le KMO sur la matrice n'a pu etre obtenu.",envir=.dico) -assign("desc_kmo_on_matrix_could_not_be_obtained_trying", "Le KMO sur la matrice n'a pu etre obtenu. Nous tentons de realiser un lissage de la matrice de correlation",envir=.dico) -assign("desc_large_format_must_be_numeric_or_integer", "Si vos donnees sont en format large, les mesures doivent toutes etre numeriques ou des entiers (integer)",envir=.dico) -assign("desc_list_of_objects_still_in_mem", "Liste des objects encore en memoire de R",envir=.dico) -assign("desc_log_with_base", "le logarithme de base",envir=.dico) -assign("desc_manifest_variables_of", "Variables manifestes de",envir=.dico) -assign("desc_manual_contrast_need_coeff_matrice", "Si vous entrez des contrastes manuellement, toutes les variables de l'analyse doivent avoir leur matrice de coefficients",envir=.dico) -assign("desc_matrix_is_singular_mardia_cannot_be_performed", "La matrice est singuliere et le test de Mardia ne peut etre realise. Seules les analyses univariees peuvent etre realisees",envir=.dico) -assign("desc_mcnemar_need_2x2_table_yours_are_different", "Le test de McNemar implique un tableau 2x2. Les dimensions de votre tableau sont differentes.",envir=.dico) -assign("desc_modalities_product_must_correspond_to_cols_selected", "le produit des modalites de chacune des variables doit correspondre au nombre de colonnes selectionnees.",envir=.dico) -assign("desc_model_contains_error", "Le modele ne peut etre evalue. Il doit contenir une erreur",envir=.dico) -assign("desc_model_could_not_converge", "Le modele n'a pas pu converger. Les parametres ont ete adaptes pour permettre au modele de converger",envir=.dico) -assign("desc_model_seems_incorrect_could_not_be_created", "Le modele semble incorrect et n'a pas pu etre cree.",envir=.dico) -assign("desc_most_common_effect_size", "la taille d'effet la plus frequente est le eta carre partiel - pes.\nLa taille d'effet la plus precise est le eta carre generalise - ges",envir=.dico) -assign("desc_multicolinearity_risk", "risque de multicolinearite si le determinant de la matrice est inferieur a 0.00001",envir=.dico) -assign("desc_multiple_ways_to_compute_squares_sum", "Il existe plusieurs maniere de calculer la somme des carres. Le choix par defaut des logiciels commerciaux est une somme des carres\nde type 3, mettant la priorite sur les interactions plutot que sur les effets principaux.",envir=.dico) -assign("desc_must_be_dichotomic", "modalites. Elle est incompatible avec une regression logistique. Elle doit etre dichotomique",envir=.dico) -assign("desc_nb_factors_must_be_positive_integer", "Le nombre de facteur doit etre un entier positif inferieur au nombre de facteurs",envir=.dico) -assign("desc_need_at_least_three_observation_by_combination", "Certaines combinaisons des modalites ont moins de 3 observations. Vous devez avoir au moins 3 observations pour chaque combinaison",envir=.dico) -assign("desc_neg_log_impossible", "il n'est pas possible de calculer des logarithmes pour une base est negative. NA est renvoye",envir=.dico) -assign("desc_no_analysis_can_be_performed_given_your_data", "Les variables que vous avez choisies pour realiser votre analyse ne permettent de faire aucune analyse. Veuillez redefinir votre analyse",envir=.dico) -assign("desc_no_data_in_R_memory", "il n'y a pas de donnees dans la memoire de R, veuillez importer les donnnees sur lesquelles realiser l'analyse",envir=.dico) -assign("desc_non_equal_independant_variable_modalities_occurrence", "Le nombre d'occurrence pour chaque modalite de votre variable independante n'est pas identique. Veuillez choisir un identifiant participant",envir=.dico) -assign("desc_non_numeric_value", "La valeur entree n'est pas numerique, vous devez entrer une valeur numerique",envir=.dico) -assign("desc_non_numeric_variable", "la variable n est pas numerique",envir=.dico) -assign("desc_non_param_are_rho_and_tau", "Le test non parametrique correspond au rho de Spearman et au tau de Kendall",envir=.dico) -assign("desc_non_param_is_wilcoxon_or_mann_withney", "Le test non parametrique est le test de Wilcoxon (ou Mann-Whitney)",envir=.dico) -assign("desc_no_obs_for_combination", "pas d'observations pour la combinaison",envir=.dico) -assign("desc_no_result_saved", "aucun resultat n'a ete sauvegarde",envir=.dico) -assign("desc_norm_must_be_numeric", "La norme doit etre une valeur numerique.",envir=.dico) -assign("desc_no_saved_analysis_found", "Aucune analyse sauvegardee n'a pu etre trouvee",envir=.dico) -assign("desc_number_of_judge_is", "le nombre de juge",envir=.dico) -assign("desc_number_of_missing_values", "Nombre de valeurs manquantes par variable",envir=.dico) -assign("desc_number_of_observations_is", "le nombre d'observations",envir=.dico) -assign("desc_number_outliers_removed", "Nombre d'observations retirees",envir=.dico) -assign("desc_obs_with_asterisk_are_outliers", "Les observations marquees d'un asterisque sont considerees comme influentes au moins sur un critere",envir=.dico) -assign("desc_odd_ratio_cannot_be_computed", "On ne peut pas calculer les OR pour des tableaux plus grands que 2x3 ou des tableaux contenant des 0",envir=.dico) -assign("desc_only_one_dependant_variable_alllowed", "Il ne peut y avoir qu'une seule variable dependante.",envir=.dico) -assign("desc_only_one_file_format_at_time_EPS_JPG", "Only one file format for saving figure may be used at a time (you have both EPS and JPG specified).",envir=.dico) -assign("desc_only_one_file_format_at_time_EPS_PDF", "Only one file format for saving figure may be used at a time (you have both PDF and EPS specified).",envir=.dico) -assign("desc_only_one_file_format_at_time_PDF_JPG", "Only one file format for saving figure may be used at a time (you have both PDF and JPG specified).",envir=.dico) -assign("desc_only_values_above_diagonal_are_adjusted_for_multiple_comp", "Seules les valeurs au-dessus de la diagonales sont ajustees pour comparaisons multiples",envir=.dico) -assign("desc_operation_succesful", "L'operation mathematique s'est deroulee correctement.",envir=.dico) -assign("desc_order", "de tri",envir=.dico) -assign("desc_outliers_identified_on_4_div_n", "les valeurs influentes sont identifiees sur la base de 4/n",envir=.dico) -assign("desc_outliers_identified_on_mahalanobis", "les valeurs influentes sont identifiees sur la base de la distance de Mahalanobis avec un seuil du chi a 0.001",envir=.dico) -assign("desc_outliers_on_4_div_n", "les valeurs influentes sont identifiees sur la base de 4/n",envir=.dico) -assign("desc_packages_used_for_this_function", "Packages utilises pour cette fonction",envir=.dico) -assign("desc_param_is_BP", "Le test parametrique est la correlation de Bravais-Pearson",envir=.dico) -assign("desc_param_is_t_test", "Le test parametrique est le test t classique",envir=.dico) -assign("desc_param_test_is_classical_reg_robusts_are_m_estimator", "Le test parametrique est la regression classique et les tests robustes sont une estimation sur un M estimeur ainsi qu'un bootstrap.",envir=.dico) -assign("desc_percentile_bootstrap_prefered_for_small_samples", "la methode du percentile bootstrap doit etre preferee pour les petits echantillons",envir=.dico) -assign("desc_perfectly_correlated_variables_in_matrix_trying_to_solve", "vous tenter de faire une matrice de correlations avec des variables parfaitement correlees. Cela pose souci pour le calcul de la distance de Mahalanobis. Nous tentons de resoudre le souci",envir=.dico) -assign("desc_polyc_correlations_failed_rho_used_instead", "Les correlations polychoriques ont echoue. Les correlations utilisees sont des rho de Spearman",envir=.dico) -assign("desc_proba_and_IC_estimated_on_bootstrap", "Les probabilites et les IC sont estimes sur la base d'un bootsrap. L'IC est corrige pour comparaison multiple, contrairement a la probabilite reportee.",envir=.dico) -assign("desc_probabilities_vector_please_no_fraction", "Vecteur des probabilites. Attention : ne pas entrer des fractions",envir=.dico) -assign("desc_red_dot_is_mean_error_is_sd", "Le point rouge est la moyenne. La barre d'erreur est l'ecart-type",envir=.dico) -assign("desc_references", "References des packages utilises pour cette analyse",envir=.dico) -assign("desc_removed_variable", "variable supprimee",envir=.dico) -assign("desc_removing_outliers_weakens_sample_size", "La suppression des valeurs influentes entraĂ®ne un effectif trop faible sur certaines modalites pour mener a bien l'analyse",envir=.dico) -assign("desc_result_succesfully_imported_in", "Les resultats ont ete correctement importes dans",envir=.dico) -assign("desc_robusts_statistics_could_not_be_computed", "Les statistiques robustes n'ont pas pu etre realisees",envir=.dico) -assign("desc_robust_statistics_are_alternative_to_the_principal_but_slower", "Les statistiques robustes sont des analyses alternatives a l'analyse principale, impliquant le plus souvent des bootstraps. Ces analyses sont souvent plus lentes",envir=.dico) -assign("desc_saturation_criterion_must_be_between_zero_and_one", "Le critere de saturation doit etre compris entre 0 et 1.",envir=.dico) -assign("desc_search_here", "Tapez votre recherche ici",envir=.dico) -assign("desc_selected_obs_are_in", "les observations que vous avez selectionnees sont dans",envir=.dico) -assign("desc_selection_for_bayesian_factor_does_not_apply_to_complex_models", "Les methodes de selection pour les facteurs bayesiens ne s'appliquent pas pour des modeles complexes.",envir=.dico) -assign("desc_should_specify_nb_factors_repeated_measure", "vous devez specifier le nombre de facteurs en mesure repetee",envir=.dico) -assign("desc_single_dependant_variable_allowed_in_paired_t", "Il ne peut y avoir qu'une seule variable dependante pour les t de student pour echantillons apparies",envir=.dico) -assign("desc_singular_matrix_mahalanobis_on_max_info", "Votre matrice est singuliere, ce qui pose souci. Nous tentons de de resoudre le souci. Si possible, la distance de Mahalanobis sera alors calculee sur le maximum d'information tout en evitant la singularite.",envir=.dico) -assign("desc_some_values_are_not_numeric", "Toutes les valeurs entrees ne sont pas numerique. Veuillez entrer des valeurs numeriques uniquement",envir=.dico) -assign("desc_special_characters_have_been_removed", "Les accents / caracteres speciaux ont volontairement ete supprimes pour assurer la portabilite de easieR sur tous les ordinateurs.",envir=.dico) -assign("desc_specify_f_value", "Vous devez specifier la valeur du F. Cette valeur doit etre superieure a 1",envir=.dico) -assign("desc_specify_lower_bound", "vous devez preciser la limite inferieure",envir=.dico) -assign("desc_specify_probability_value", "Vous devez specifier la valeur de la probabilite. Cette valeur doit etre entre 0 et 1",envir=.dico) -assign("desc_specify_upper_bound", "vous devez preciser la limite superieure",envir=.dico) -assign("desc_standardized_saturation_on_correlation_matrix", "saturations standardisees basees sur la matrice de correlations",envir=.dico) -assign("desc_succesfully_imported", "les donnees ont ete importees correctement",envir=.dico) -assign("desc_succesful_operation", "L'operation a ete realisee correctement",envir=.dico) -assign("desc_tested_model_is", "le modele teste est",envir=.dico) -assign("desc_there_is_no_rotation", "il n'y a pas de rotation",envir=.dico) -assign("desc_the_variable_lower", "la variable",envir=.dico) -assign("desc_the_variable_upper", "La variable",envir=.dico) -assign("desc_this_analysis_will_not_be_performed", ". Cette analyse ne sera pas realisee.",envir=.dico) -assign("desc_this_index_is_prefered_for_most_cases", "Cet indice est adapte dans la plupart des situations. Le M-estimator modifie doit etre prefere pour N<20",envir=.dico) -assign("desc_this_is_large_format", "ceci est le format large",envir=.dico) -assign("desc_this_is_long_format", "ceci est le format long",envir=.dico) -assign("desc_times_less", "fois moins",envir=.dico) -assign("desc_times_more", "fois plus",envir=.dico) -assign("desc_to_display_results_use_summary", "Pour afficher les resultats, veuillez utiliser summary(modele.cfa)",envir=.dico) -assign("desc_total_observations", "nombre total d'observations",envir=.dico) -assign("desc_truncature_on_m_estimator_adapts_to_sample", "La troncature sur le M-estimator s'adapte en fonction des caracteristiques de l'echantillon.",envir=.dico) -assign("desc_two_cols_are_needed", "Pour un facteur en mesures repetees en format large, il faut au moins deux colonnes",envir=.dico) -assign("desc_two_modalities_for_independante_categorial_variable", "Vous devez utiliser une variable independante categorielle a 2 modalites",envir=.dico) -assign("desc_unauthorized_char_replaced", "Des caracteres non autorises ont ete utilises pour le nom. Ces caracteres ont ete remplaces par des points",envir=.dico) -assign("desc_unavailable_distal_mediations", "Les mediations distales ne sont pas disponibles pour le moment / Distal mediations are not available for now",envir=.dico) -assign("desc_user_exited_aov_plus", "vous avez quitte aov.plus",envir=.dico) -assign("desc_value_must_be_between_zero_and_one", "La valeur doit etre comprise entre 0 et 1",envir=.dico) -assign("desc_value_must_be_numeric", "La valeur doit etre numerique et comprise entre le minimum et le maximum de la variable dependante.",envir=.dico) -assign("desc_variable_added", "Variable ajoutee",envir=.dico) -assign("desc_variable_must_be_numeric_and_of_non_null_variance", "la variable doit etre numerique et avoir une variance non nulle.",envir=.dico) -assign("desc_variable_must_be_positive_int", "la variable doit etre un entier *integer* positif",envir=.dico) -assign("desc_variables_are_in", "les variables selectionnees sont dans",envir=.dico) -assign("desc_we_could_not_compute_anova_on_medians", "Desole, nous n'avons pas pu calcule l'anova sur les medianes, possiblement en raison d'un nombre important d'ex aequo.",envir=.dico) -assign("desc_we_could_not_compute_robust_anova", "Desole, nous n'avons pas pu calcule l'anova robuste.",envir=.dico) -assign("desc_working_dir_is_now", "Le repertoire de travail est a present",envir=.dico) -assign("desc_you_can_chose_predefined_or_manual_contrasts", "Vous pouvez choisir les contrastes predefinis ou les specifier manuellement. Dans ce dernier cas, veuillez choisir specifier les contrastes",envir=.dico) -assign("desc_you_can_still_add", "Vous pouvez encore ajouter une valeur specifique au total. Laissez 0 si vous ne souhaitez rien ajouter",envir=.dico) -assign("desc_you_can_still_multiply", "Vous pouvez encore multiplier le total par une valeur specifique. Laissez 1 si vous ne souhaitez plus multiplier par une nouvelle valeur",envir=.dico) -assign("desc_you_did_this_operation", "vous avez realise l'operation suivante :",envir=.dico) -assign("desc_you_exited_afe", "vous avez quitte l'AFE",envir=.dico) -assign("desc_you_have_selected", "vous avez selectionne",envir=.dico) -assign("desc_you_must_give_obs_number", "Vous devez entrer le numero de l'observation",envir=.dico) -assign("desc_your_dependant_variable_has", "Votre veriable dependante a",envir=.dico) -assign("desc_z_must_be_a_number", "z doit etre un nombre",envir=.dico) -assign("desc_author", "author: 'Genere automatiquement par easieR'",envir=.dico) -assign("desc_title", "title: 'Resultats de vos analyses'",envir=.dico) -assign("txt_absolute_value", "valeur absolue",envir=.dico) -assign("txt_added_variables_graph", "Graphe des variables ajoutees",envir=.dico) -assign("txt_additions", "additions",envir=.dico) -assign("txt_additive_effects", "Effets additifs",envir=.dico) -assign("txt_additive_model_variables", "Variables modele additif",envir=.dico) -assign("txt_add_of_cols", "addition de colonnes",envir=.dico) -assign("txt_add_of_specific_value", "addition d'une valeur specifique",envir=.dico) -assign("txt_adequation_adjustement_indexes", "Indices d'adequation et d'ajustement",envir=.dico) -assign("txt_adequation_measurement_of_matrix", "Mesure d'adequation de la matrice",envir=.dico) -assign("txt_adequation_measures", "Mesures d'adequation",envir=.dico) -assign("txt_adequation_outside_diagonal", "Adequation basee sur les valeurs en dehors de la diagonale",envir=.dico) -assign("txt_adjusted_data_loftus_masson", "Donnees ajustees (Loftus & Masson, 1994)",envir=.dico) -assign("txt_adjusted_means_graph", "Moyennes ajustee-Graphique",envir=.dico) -assign("txt_adjusted_means", "Moyennes ajustee",envir=.dico) -assign("txt_adjustement_measure", "Mesure d'ajustement",envir=.dico) -assign("txt_adjusted_p_dot_value", "Valeur P corrigĂ©e",envir=.dico) -assign("txt_agreement", "Accord",envir=.dico) -assign("txt_aic_criterion", "AIC - Akaike Information criterion",envir=.dico) -assign("txt_alpha_warning", "Avertissement alpha",envir=.dico) -assign("txt_alternative", "alternative",envir=.dico) -assign("txt_analysis_factor_component", "analyses de facteurs et de composantes",envir=.dico) -assign("txt_analysis_on", "analyse sur",envir=.dico) -assign("txt_analysis_on_truncated_means", "Analyse sur les moyennes tronquees",envir=.dico) -assign("txt_analysis_on_variable", "Analyse sur la variable",envir=.dico) -assign("txt_analysis_premature_abortion", "Arret premature de l'analyse",envir=.dico) -assign("txt_ancova_application_conditions", "Conditions d'application de l'ancova",envir=.dico) -assign("txt_and_the_number_of_obs", "et le nombre d'observations",envir=.dico) -assign("txt_and_YZ", "et YZ",envir=.dico) -assign("txt_anova_ancova", "analyse de variance et covariance",envir=.dico) -assign("txt_anova", "Anova",envir=.dico) -assign("txt_anova_on", "anova sur",envir=.dico) -assign("txt_anova_on_modified_huber_estimator", "Anova sur l'estimateur modifie de localisation de Huber",envir=.dico) -assign("txt_anova_on_truncated_means", "Anova basee sur les moyennes tronquees",envir=.dico) -assign("txt_anova_with_welch_correction", "Anova avec correction de Welch pour variances heterogenes",envir=.dico) -assign("txt_apparied_correlations", "Correlations appariees",envir=.dico) -assign("txt_apriori", "a priori",envir=.dico) -assign("txt_autocorrelation", "Autocorrelation",envir=.dico) -assign("txt_backward", "Backward",envir=.dico) -assign("txt_backward_step_descending", "Backward- pas-a-pas descendant",envir=.dico) -assign("txt_barlett_test", "Test de Barlett",envir=.dico) -assign("txt_bayes_factor_10", "Bayes Factor (10)",envir=.dico) -assign("txt_bayes_factor", "BayesFactor",envir=.dico) -assign("txt_bayesian_approach_hierarchical_models", "Approche bayesienne des modeles hierarchique",envir=.dico) -assign("txt_bayesian_factor_by_group", "Facteur bayesien par groupe",envir=.dico) -assign("txt_bayesian_factor", "Facteur bayesien",envir=.dico) -assign("txt_bayesian_factor_of_model", "FB du modele",envir=.dico) -assign("txt_bayesian_factors_10", "Facteur bayesiens 10",envir=.dico) -assign("txt_bayesian_factors_compute_null_with_bayesian_approach", "Facteurs bayesiens : calcule l'equivalent du test d'hypothese nulle en adoptant une approche bayesienne.",envir=.dico) -assign("txt_bayesian_factors_for_BP", "Facteurs Bayesiens pour la correlation de Bravais-Pearson",envir=.dico) -assign("txt_bayesian_factors_for_spearman", "Facteurs Bayesiens pour la correlation de Spearman",envir=.dico) -assign("txt_bayesian_factors_sequential", "Facteurs bayesiens sequentiels",envir=.dico) -assign("txt_bca_bootstrap_on_m_estimator", "Bootstrap de type BCa sur le M-estimator",envir=.dico) -assign("txt_beta_table", "table des betas",envir=.dico) -assign("txt_between", "entre",envir=.dico) -assign("txt_bidirectionnal", "Bidirectionnel",envir=.dico) -assign("txt_b_m_estimator", "b (M estimator)",envir=.dico) -assign("txt_bootstrap_on_BP", "Bootstrap sur la correlation de Bravais Pearson",envir=.dico) -assign("txt_bootstrap_t_method", "bootstrap-t method",envir=.dico) -assign("txt_bootstrap_t_method_on_truncated_means", "Bootstrap utilisant la methode t sur les moyennes tronquees",envir=.dico) -assign("txt_BP_correlation_by_group", "Correlation de Bravais-Pearson par groupe",envir=.dico) -assign("txt_breusch_pagan_test", "Verification de la non-constance de la variance d'erreur (test de Breusch-Pagan)",envir=.dico) -assign("txt_cancel", "annuler",envir=.dico) -assign("txt_cauchy_prior_width", "Cauchy Prior Width (r)",envir=.dico) -assign("txt_center_or_center_reduce", "Centrer / centrer reduire",envir=.dico) -assign("txt_center_reduce", "centrer reduire",envir=.dico) -assign("txt_ceres_graph_linearity", "Graphique de Ceres testant la linearite",envir=.dico) -assign("txt_chi_adjustement", "Ajustement",envir=.dico) -assign("txt_chi_independance", "Independance",envir=.dico) -assign("txt_chi_results_between_var_x", "Resultats du chi.deux entre la variable",envir=.dico) -assign("txt_chi_squared", "chi deux",envir=.dico) -assign("txt_chi_squared_empirical", "chi carre empirique",envir=.dico) -assign("txt_chi_squared_likelihood_max", "chi carre du maximum de vraisemblance",envir=.dico) -assign("txt_chi_squared_null_model", "chi carre du modele null",envir=.dico) -assign("txt_chi_squared_type", "Type de khi deux",envir=.dico) -assign("#txt_choice", "choix",envir=.dico) -assign("txt_coeff_table", "Table des coefficients",envir=.dico) -assign("txt_col_correspoding_to_variable", "Colonnes correspondant Ă  la variable",envir=.dico) -assign("txt_col_mean", "moyenne de colonnes",envir=.dico) -assign("txt_cols", "colonnes",envir=.dico) -assign("txt_col_separator", "Separateur de colonnes",envir=.dico) -assign("txt_cols_in_repeated_measure", "Colonnes en mesures repetees",envir=.dico) -assign("txt_cols_multiplication", "multiplication de colonnes",envir=.dico) -assign("txt_comma", "virgule",envir=.dico) -assign("txt_compare_to_baseline", "comparaison a une ligne de base",envir=.dico) -assign("txt_compare_two_correlations", "Comparaison de deux correlations",envir=.dico) -assign("txt_comparison_of_two_correlations", "comparaison des deux correlations",envir=.dico) -assign("txt_comparison_on_truncated_means", "Comparaison basee sur les moyennes tronquees",envir=.dico) -assign("txt_comparisons_XY", "comparaison des correlations XY=",envir=.dico) -assign("txt_comparison_to_norm", "Comparaison a une norme",envir=.dico) -assign("txt_comparison_two_by_two", "Comparaison 2 a 2",envir=.dico) -assign("txt_compile_report", "generer un rapport",envir=.dico) -assign("txt_complementary_results", "Resultats complementaires (e.g. contrastes d'interaction et moyennes ajustees)",envir=.dico) -assign("txt_complete_dataset", "Donnees completes",envir=.dico) -assign("txt_complete_model", "Modele complet",envir=.dico) -assign("txt_complexity", "complexite",envir=.dico) -assign("txt_complex_model", "modele complexe",envir=.dico) -assign("txt_confidance_threshold", "Seuil de confiance (1- alpha)",envir=.dico) -assign("txt_confidence_interval_estimated_by_bootstrap", "Intervalle de confiance estime par bootstrap",envir=.dico) -assign("txt_confidence_interval", "Intervalle de confiance",envir=.dico) -assign("txt_confidence_interval_inferior_limit", "Lim.inf",envir=.dico) -assign("txt_confidence_interval_superior_limit", "Lim.sup",envir=.dico) -assign("txt_confidence_interval_of_saturations_on_bootstrap", "Intervalle de confiance des saturations sur la base du bootstrap - peut etre biaise en presence de Heyhood case",envir=.dico) -assign("txt_confidence_interval_on_bootstrap", "Intervalle de confiance base sur le bootstrap",envir=.dico) -assign("txt_confidence_interval_on_standard_error", "Intervalle de confiance base sur l'erreur standard de l'alpha",envir=.dico) -assign("txt_confirmatory_factorial_analysis", "Analyse factorielle confirmatoire",envir=.dico) -assign("txt_contrast", "contraste",envir=.dico) -assign("txt_contrasts", "contrastes",envir=.dico) -assign("txt_contrasts_for", "Contrastes pour",envir=.dico) -assign("txt_contrasts_table_imitating_commercial_softwares", "Table des contrastes imitant les logiciels commerciaux",envir=.dico) -assign("txt_contrasts_table", "Table des contrastes",envir=.dico) -assign("txt_control_variables", "Variable-s a controler",envir=.dico) -assign("txt_correction_for_polyc_corr_must_be_between_zero_and_one", "La correction pour le calcul de correlations polycoriques doit etre comprise entre 0 et 1.",envir=.dico) -assign("txt_correlation_between_scores_and_factors", "Correlations des scores avec les facteurs",envir=.dico) -assign("txt_correlation_between_var_x", "Correlation entre la variable",envir=.dico) -assign("txt_correlation_is", "correlation de",envir=.dico) -assign("txt_correlation_matrix_determinant", "Determinant de la matrice de correlation",envir=.dico) -assign("txt_correlation_matrix_determinant_information", "Determinant de la matrice de correlations : information",envir=.dico) -assign("txt_correlations_between_factors", "correlations entre facteurs",envir=.dico) -assign("txt_correlations_comparison", "comparaison de correlations",envir=.dico) -assign("txt_correlations_matrix_afe", "Matrice de correlation utilisee pour AFE",envir=.dico) -assign("txt_covariance_matrix_adjusted", "Matrice de covariance ajustee",envir=.dico) -assign("txt_covariance_matrix_estimated", "Matrice de covariance estimee",envir=.dico) -assign("txt_cox_snell_r_2", "Cox and Snell R^2",envir=.dico) -assign("txt_cronbach_alpha", "Alpha de Cronbach",envir=.dico) -assign("txt_cronbach_alpha_on_whole_scale", "Alpha de Cronbach sur la totalite de l'echelle",envir=.dico) -assign("txt_cross_validation", "Validation croisee",envir=.dico) -assign("txt_csv_file", "Fichier CSV",envir=.dico) -assign("txt_cumulated_explaination_ratio", "Proportion cumulee de l'explication",envir=.dico) -assign("txt_cumulated_explained_variance_ratio", "proportion de variance expliquee cumulee",envir=.dico) -assign("txt_dataframe_choice", "Choix du dataframe",envir=.dico) -assign("txt_data_import_export_save", "Donnees - (Importation, exportation, sauvegarde)",envir=.dico) -assign("txt_decimal_separator", "Separateur de decimales",envir=.dico) -assign("txt_default_outputs", "Sorties par defaut",envir=.dico) -assign("txt_delete_observations_with_missing_values", "Suppression des observations avec valeurs manquantes",envir=.dico) -assign("txt_denominator", "Denominateur",envir=.dico) -assign("txt_dependant_variables", "Variable-s dependante-s",envir=.dico) -assign("txt_dependant_variable", "Variable dependante",envir=.dico) -assign("txt_descriptive_statistics_by_group", "statistiques descriptives par groupe",envir=.dico) -assign("txt_detailed_corr_analysis", "Analyse detaillee (Bravais Pearson/Spearman/tau) pour une ou peu de correlations",envir=.dico) -assign("txt_deviation", "Deviance",envir=.dico) -assign("txt_dichotomic_ordinal", "dichotomiques/ordinales",envir=.dico) -assign("txt_difference", "Difference",envir=.dico) -assign("txt_distance_mediation_effect", "Effet de mediation distante",envir=.dico) -assign("txt_distance_mediator", "Mediation a distance",envir=.dico) -assign("txt_do_nothing_keep_all_obs", "Ne rien faire - Garder l'ensemble des observations",envir=.dico) -assign("txt_dot", "point",envir=.dico) -assign("txt_durbin_watson_test_autocorr", "Test de Durbin-Watson - autocorrelations",envir=.dico) -assign("txt_dw_statistic", "statistique de D-W",envir=.dico) -assign("txt_dynamic_crossed_table", "Tableau croise dynamique",envir=.dico) -assign("txt_effect", "Effet",envir=.dico) -assign("txt_equals_to", "egal a",envir=.dico) -assign("txt_error", "erreur",envir=.dico) -assign("txt_estimated_parameters_not_standardized", "Parametres estimes non standardises",envir=.dico) -assign("txt_estimated_parameters", "Parametres estimes",envir=.dico) -assign("txt_estimated_parameters_standardized", "Parametres estimes standardises",envir=.dico) -assign("txt_estimation", "estimation",envir=.dico) -assign("txt_excel_file", "Fichier Excel",envir=.dico) -assign("txt_exogenous_fixed_variables", "Variables exogenes fixees [fixed.x=default]",envir=.dico) -assign("txt_expected", "Attendus",envir=.dico) -assign("txt_expected_sample", "Effectifs attendus",envir=.dico) -assign("txt_experimental_pan_between", "Pan experimental entre",envir=.dico) -assign("txt_explaination_ratio", "Proportion de l'explication",envir=.dico) -assign("txt_explained_variance_ratio", "proportion de variance expliquee",envir=.dico) -assign("txt_explained_variance", "Variance expliquee",envir=.dico) -assign("txt_exponant", "exposant",envir=.dico) -assign("txt_exponant_or_root", "exposant ou racine",envir=.dico) -assign("txt_exponential", "exponentiel",envir=.dico) -assign("txt_export_data", "exporter des donnees",envir=.dico) -assign("txt_factorial_analysis", "Analyse factorielle",envir=.dico) -assign("txt_factorial_analysis_using_fa_with_method", "analyse factorielle en utilisant la fonction fa du package psych avec la methode",envir=.dico) -assign("txt_factorial_exploratory_analysis", "Analyse factorielle exploratoire",envir=.dico) -assign("txt_factor_name", "Nom du facteur",envir=.dico) -assign("txt_factors", "facteurs.",envir=.dico) -assign("txt_factors_ortho", "Orthogonalite des facteurs [orthogonal=FALSE]",envir=.dico) -assign("txt_factors_to_keep_accord_to_parallel_analysis_is", "le nombre de facteurs a retenir selon l'analyse en parallele est de",envir=.dico) -assign("txt_fiability_analysis", "analyse de fiabilite et d accord",envir=.dico) -assign("txt_fiability_by_removed_item", "fiabilite par item supprime",envir=.dico) -assign("txt_for_a_detailed_results_description_distal", "Pour une description detaillee des resultats, ?distal.med",envir=.dico) -assign("txt_for_a_detailed_results_description_mediation", "Pour une description detaillee des resultats, ?mediation",envir=.dico) -assign("txt_forward_step_ascending", "Forward - pas-a-pas ascendant",envir=.dico) -assign("txt_friedman_anova_pairwise_comparison", "Comparaison 2 a 2 pour ANOVA de Friedman",envir=.dico) -assign("txt_f_value", "valeur du F",envir=.dico) -assign("txt_get_working_dir", "obtenir le repertoire de travail",envir=.dico) -assign("txt_global_model_estimation", "Estimation du modele global",envir=.dico) -assign("txt_graphic_mean_sd", "Representation graphique - Moyenne et ecart-type",envir=.dico) -assign("txt_graphics", "Graphiques",envir=.dico) -assign("txt_graphics_informations", "Informations sur les graphiques",envir=.dico) -assign("txt_group_analysis", "Analyse par groupe",envir=.dico) -assign("txt_groups_analysis", "analyse par groupes",envir=.dico) -assign("txt_groups_variables", "Variable-s groupes",envir=.dico) -assign("txt_grubbs_test", "Test de Grubbs",envir=.dico) -assign("txt_hierarchical_factorial_analysis", "Analyse factorielle hierarchique",envir=.dico) -assign("txt_hierarchical_model_analysis", "Analyse hierarchique des modeles ",envir=.dico) -assign("txt_hierarchical_models_complete_model_sig_at_each_step", "Modeles hierarchique - significativite du modele complet a chaque etape",envir=.dico) -assign("txt_hierarchical_models_deviance_table", "Table de l'analyse de la deviance des modeles hierarchiques",envir=.dico) -assign("txt_hierarchical_models", "Modeles hierarchiques",envir=.dico) -assign("txt_hierarchical_models_variance_analysis_table", "Table de l'analyse de variance des modeles hierarchiques",envir=.dico) -assign("txt_hosmer_lemeshow_r_2", "Hosmer and Lemeshow R^2",envir=.dico) -assign("txt_hypergeom_total_sample_fixed_rows_cols", "hypergeom - Effectif total fixe pour les lignes et les colonnes",envir=.dico) -assign("txt_hypothesis_analysis", "Analyses - Tests d'hypothese",envir=.dico) -assign("txt_identified_outliers_synthesis", "Synthese du nombre d'observations considerees comme influentes",envir=.dico) -assign("txt_identifying_outliers", "Identification des valeurs influentes",envir=.dico) -assign("txt_id_variable", "Variable *Identifiant*",envir=.dico) -assign("txt_import_data", "importer des donnees",envir=.dico) -assign("txt_imput_missing_values", "Imputation de valeurs manquantes",envir=.dico) -assign("txt_independant_correlations", "Correlations independantes",envir=.dico) -assign("txt_independant_group_variables", "Variables a groupes independants",envir=.dico) -assign("txt_independant_variable", "Variable independante",envir=.dico) -assign("txt_indepmulti_fixed_sample_rows_cols", "indepMulti - Effectif fixe pour les colonnes - variable",envir=.dico)# RENAME TO COLS only -assign("txt_indepmulti_total_fixed_rows_cols", "indepMulti - Effectif total fixe pour les lignes - variable",envir=.dico) # RENAME TO RAWS only -assign("txt_inferior", "Inferieur",envir=.dico) -assign("txt_inferior_or_equal_to", "inferieur ou egal a",envir=.dico) -assign("txt_inferior_proba", "probabilite inferieure",envir=.dico) -assign("txt_inferior_to", "inferieur a",envir=.dico) -assign("txt_inflation_variance_factor", "Facteur d'inflation de la variance",envir=.dico) -assign("txt_influence_method", "Mesure d influence",envir=.dico) -assign("txt_information", "Information",envir=.dico) -assign("txt_init_values", "Valeurs de depart",envir=.dico) -assign("txt_inspect_initial_values", "Inspecter les valeurs de depart",envir=.dico) -assign("txt_inspect_model_matrices", "Inspecter les matrices du modele",envir=.dico) -assign("txt_inspect_model_representation", "Inspecter la representation du modele",envir=.dico) -assign("txt_interaction_effects", "Effets d'interaction",envir=.dico) -assign("txt_interactive_model_variables", "Variables modele interactif",envir=.dico) -assign("txt_is_different_from", "est different de",envir=.dico) -assign("txt_jointmulti_total_fixed_sample", "jointMulti - Effectif total fixe",envir=.dico) -assign("txt_judge1", "Juge 1",envir=.dico) -assign("txt_judge2", "Juge 2",envir=.dico) -assign("txt_kaiser_meyer_olkin_index", "Indice de Kaiser-Meyer-Olkin global",envir=.dico) -assign("txt_keep_default_values", "Garder les valeurs par defaut",envir=.dico) -assign("txt_kendall_coeff", "Coefficient de concordance de Kendall",envir=.dico) -assign("txt_kendall_partial_semipartial_tau", "Tau partiel/semi-partiel de Kendall",envir=.dico) -assign("txt_kendall_partial_tau", "Tau partiel de Kendall",envir=.dico) -assign("txt_kendall_semipartial_tau", "Tau semi-partiel de Kendall",envir=.dico) -assign("txt_kendall_tau", "Tau de Kendall",envir=.dico) -assign("txt_kolmogorov_smirnov_comparing_two_distrib", "Test de Kolmogorov-Smirnov comparant deux distributions",envir=.dico) -assign("txt_labeled_outliers", "Valeurs considerees comme influentes",envir=.dico) -assign("txt_latent_variable_name", "Nom de la variable latente",envir=.dico) -assign("txt_less_square_diagonally_pondered", "moindre carre pondere diagonalement",envir=.dico) -assign("txt_less_square_generalized", "moindre carre generalises",envir=.dico) -assign("txt_less_square_not_pondered", "moindre carre non pondere",envir=.dico) -assign("txt_less_square_pondered", "moindre carre pondere",envir=.dico) -assign("txt_levene_test_verifying_homogeneity_variances", "Test de Levene verifiant l'homogeneite des variances",envir=.dico) -assign("txt_likelihood_only_for_estimator", "Vraisemblance (seulement pour estimator=ML) [likelihood=default]",envir=.dico) -assign("txt_likelihood_ratio_g_test", "Rapport de vraisemblance (G test)",envir=.dico) -assign("txt_lilliefors_d", "D de Lilliefors",envir=.dico) -assign("txt_linearity_graph_between_predictors_and_dependant_variable", "Graphique testant la linearite entre les predicteurs et la variable dependante",envir=.dico) -assign("txt_link_only_for_estimator", "Lien (seulement pour estimator=MML) [link=probit]",envir=.dico) -assign("txt_list_of_objects_in_mem", "liste des objets en memoire",envir=.dico) -assign("txt_logarithm", "logarithme",envir=.dico) -assign("txt_long_or_large_format", "Format large au format long",envir=.dico) -assign("txt_lower_bound_rmsea", "limite inferieure du RMSEA",envir=.dico) -assign("txt_mann_whitney_test", "test de Mann-Whitney - Wilcoxon",envir=.dico) -assign("txt_mathematical_operations_on_variables", "Operations mathematiques sur des variables",envir=.dico) -assign("txt_matrix_type", "type de matrice",envir=.dico) -assign("txt_max_likelihood_chi_squared_proba_value", "valeur de la probabilite du chi carre du maximum de vraisemblance",envir=.dico) -assign("txt_max_likelihood", "maximum de vraisemblance",envir=.dico) -assign("txt_mcnemar_results_between_var_x", "Resultats du test de McNemar entre la variable",envir=.dico) -assign("txt_mcnemar_test", "Test de McNemar",envir=.dico) -assign("txt_mcnemar_test_with_continuity_correction", "Test de McNemar avec correction de continuite",envir=.dico) -assign("txt_mcnemar_test_without_yates_correction", "Test de McNemar sans correction de continuite",envir=.dico) -assign("txt_mcnemar_test_with_yates_correction", "Test de McNemar avec correction de Yates",envir=.dico) -assign("txt_mean1", "Moyenne1",envir=.dico) -assign("txt_mean2", "Moyenne2",envir=.dico) -assign("txt_mean_complexity", "Complexite moyenne",envir=.dico) -assign("txt_mean_complexity_is", "la complexite moyenne est de",envir=.dico) -assign("txt_means_adjusted_standard_errors", "moyennes et erreurs-types ajustees",envir=.dico) -assign("txt_means_comparison", "Comparaison de moyennes",envir=.dico) -assign("txt_mean_sd_for_adjusted_data", "Moyenne et ecart-type pour les donnees ajustees",envir=.dico) -assign("txt_mean_sd_for_non_adjusted_data", "Moyenne et ecart-type pour les donnees non ajustees",envir=.dico) -assign("txt_mean_sd", "Moyenne et ecart-type",envir=.dico) -assign("txt_measured_variable_name", "Nom de la variable mesuree",envir=.dico) -assign("txt_median", "Mediane",envir=.dico) -assign("txt_mediation_effect", "Effets de mediation",envir=.dico) -assign("txt_mediator2", "Mediateur 2",envir=.dico) -assign("txt_mediator", "Mediateur",envir=.dico) -assign("txt_method_choice", "Choix de la methode",envir=.dico) -assign("txt_min_correlation_between_scores_and_factors", "Correlation minimale possible des scores avec les facteurs",envir=.dico) -assign("txt_minus", "moins",envir=.dico) -assign("txt_missing_values_treatment", "Traitement des valeurs manquantes",envir=.dico) -assign("txt_mixt_correlations", "correlations mixtes",envir=.dico) -assign("txt_modalities_name_for", "Noms des modalites pour",envir=.dico) -assign("txt_modalities_to_regroup", "Modalites a regrouper",envir=.dico) -assign("txt_modality", "modalite",envir=.dico) -assign("txt_model_degrees_of_freedom", "degres de liberte du modele",envir=.dico) -assign("txt_model_matrix", "Matrices du modeles",envir=.dico) -assign("txt_model_representation", "Representation du modele",envir=.dico) -assign("txt_model_significance", "Significativite du modele global",envir=.dico) -assign("txt_multicolinearity_tests", "Tests de multicolinearite",envir=.dico) -assign("txt_multicolinearity_test", "Test de multicolinearite",envir=.dico) -assign("txt_multiple_imputation_amelia", "Multiple imputation - Amelia",envir=.dico) -assign("txt_multiple_r_square_of_factors_scores", "R carre multiple des scores avec les facteurs",envir=.dico) -assign("txt_multiplication", "multiplication",envir=.dico) -assign("txt_multivariate_normality", "Normalite multivariee",envir=.dico) -assign("txt_nb_variables_measured", "Nombre de variables mesurees",envir=.dico) -assign("txt_negative_values", "Valeurs negatives",envir=.dico) -assign("txt_new_data_set", "nouveau set de donnees",envir=.dico) -assign("txt_new_dir", "nouveau repertoire",envir=.dico) -assign("txt_N_of_XY_corr", "N de la correlation XY",envir=.dico) -assign("txt_N_of_XY_NUM_corr", "N de la correlation XY:TXT",envir=.dico) -assign("txt_N_of_XZ_corr", "N de la correlation XZ",envir=.dico) -assign("txt_N_of_XZ_NUM_corr", "N de la correlation XZ:TXT",envir=.dico) -assign("txt_non_adjusted_data", "Donnees non ajustees",envir=.dico) -assign("txt_non_centered", "Non centre",envir=.dico) -assign("txt_no", "non",envir=.dico) -assign("txt_non_parametric_test", "Test non parametrique",envir=.dico) -assign("txt_non_param_model", "Modele non parametrique",envir=.dico) -assign("txt_non_param_test", "test non parametrique",envir=.dico) -assign("txt_non_pondered_coeff", "Coefficient kappa non pondere",envir=.dico) -assign("txt_non_standardized_residuals", "Residus non standardises",envir=.dico) -assign("txt_null_hypothesis_tests", "Tests de H0",envir=.dico) -assign("txt_null_model_degrees_of_freedom", "Degres de liberte du modele null",envir=.dico) -assign("txt_numerator", "Numerateur",envir=.dico) -assign("txt_objective_function_of_model", "fonction objective du modele",envir=.dico) -assign("txt_objective_function_of_null_model", "fonction objective du modele null",envir=.dico) -assign("txt_objects_in_mem", "Objets en memoire",envir=.dico) -assign("txt_object_to_remove", "Objets a supprimer",envir=.dico) -assign("txt_observed", "Observes",envir=.dico) -assign("txt_observed_sample", "Effectifs Observes",envir=.dico) -assign("txt_odd_ratio", "Odd ratio",envir=.dico) -assign("txt_order", "Trier",envir=.dico) -assign("txt_orthogonals_inverse", "orthogonaux inverses",envir=.dico) -assign("txt_orthogonals", "orthogonaux",envir=.dico) -assign("txt_other_correlations", "Autres correlations",envir=.dico) -assign("txt_other_data", "autres donnees",envir=.dico) -assign("txt_outliers", "observations influentes",envir=.dico) -assign("txt_outliers_synthesis", "Synthese des observations influentes",envir=.dico) -assign("txt_outliers_values", "Valeurs influentes",envir=.dico) -assign("txt_packages_install", "Installation des packages",envir=.dico) -assign("txt_packages_update", "mise a jour des packages",envir=.dico) -assign("txt_packages_verification", "Verification des packages",envir=.dico) -assign("txt_parallel_analysis", "analyses paralleles",envir=.dico) -assign("txt_param_model", "Modele parametrique",envir=.dico) -assign("txt_param_tests", "Test parametrique",envir=.dico) -assign("txt_param_test", "test parametrique",envir=.dico) -assign("txt_partial_and_semi_correlations", "Correlations partielle et semi partielle",envir=.dico) -assign("txt_partial_corr_BP_by_group", "Correlation partielle de Bravais-Pearson par groupe",envir=.dico) -assign("txt_partial_correlations_matrix", "Matrice de Correlations partielles",envir=.dico) -assign("txt_partial_rho", "Rho partiel de Spearman",envir=.dico) -assign("txt_partial_semi_BP", "Correlation partielle/semi-partielle de Bravais Pearson",envir=.dico) -assign("txt_partial_semi_partial_rho", "Rho partiel/semi partiel de Spearman",envir=.dico) -assign("txt_partial_spearman_by_group", "Correlation partielle de Spearman par groupe",envir=.dico) -assign("txt_participants_id", "Identifiant participant",envir=.dico) -assign("txt_partila_correlations", "Correlations partielles",envir=.dico) -assign("txt_percentage_col", "Pourcentage par colonne",envir=.dico) -assign("txt_percentage_row", "Pourcentage par ligne",envir=.dico) -assign("txt_percentage_total", "Pourcentage total",envir=.dico) -assign("txt_percentile_bootstrap_on_m_estimators", "Percentile bootstrap sur les M-estimator",envir=.dico) -assign("txt_p_estimation_with_monter_carlo", "Valeur estimee de p par simulation de Monte Carlo",envir=.dico) -assign("txt_plus", "plus",envir=.dico) -assign("txt_poisson_total_not_fixed_sample", "poisson - Effectif total non fixe",envir=.dico) -assign("txt_polyc_correlations", "correlations polychoriques",envir=.dico) -assign("txt_polynomials", "polynomiaux",envir=.dico) -assign("txt_pondered_kappa", "Coefficient kappa pondere",envir=.dico) -assign("txt_positive_values", "Valeurs positives",envir=.dico) -assign("txt_predicted_probabilities", "Probabilites predites",envir=.dico) -assign("txt_predictor", "Predicteur",envir=.dico) -assign("txt_principal_analysis", "Analyse principale",envir=.dico) -assign("txt_principal_analysis_using_psych_with_algo", "analyse en composante principale en utilisant la fonction [principal] du package psych, l'algorithme est",envir=.dico) -assign("txt_principal_component_analysis", "Analyse en composante principale",envir=.dico) -assign("txt_probabilities", "probabilites",envir=.dico) -assign("txt_probability_matrix", "matrice des probabilites",envir=.dico) -assign("txt_probability_value", "valeur de la probabilite",envir=.dico) -assign("txt_proper_values_index", "Indice des valeurs propres",envir=.dico) -assign("txt_pseudo_r_square_delta", "Delta du pseudo R carre",envir=.dico) -assign("txt_p_value_with_monte_carlo", "Valeur p par simulation de Monte Carlo",envir=.dico) -assign("txt_ranks_lower", "rangs",envir=.dico) -assign("txt_ranks_upper", "Rangs",envir=.dico) -assign("txt_references", "References",envir=.dico) -assign("txt_remove_object_in_memory", "Suppression d objet en memoire",envir=.dico) -assign("txt_replace_by_mean", "Remplacer par la moyenne",envir=.dico) -assign("txt_replace_by_median", "Remplacer par la mediane",envir=.dico) -assign("txt_residual_distribution", "Distribution du residu",envir=.dico) -assign("txt_residual_error", "Erreur residuelle",envir=.dico) -assign("txt_residual", "residu",envir=.dico) -assign("txt_residuals_distribution", "Distribution des residus",envir=.dico) -assign("txt_residue", "Residus",envir=.dico) -assign("txt_residues_significativity_holm_correction", "Significativite des residus - probabilite corrigee en appliquant la methode de Holm",envir=.dico) -assign("txt_residue_standardized_adjusted", "Residus standardises ajustes",envir=.dico) -assign("txt_residue_standardized", "Residus standardises",envir=.dico) -assign("txt_result", "Resultat",envir=.dico) -assign("txt_rho", "Rho de Spearman",envir=.dico) -assign("txt_robust_analysis", "Analyses robustes",envir=.dico) -assign("txt_robusts", "robustes",envir=.dico) -assign("txt_robusts_statistics", "Statistiques robustes",envir=.dico) -assign("txt_robust_statistics", "Statistiques robustes - peut prendre du temps",envir=.dico) -assign("txt_robusts_tests_with_bootstraps", "Test robustes - impliquant des bootstraps",envir=.dico) -assign("txt_rotation_is_a_rotation", "la rotation est un rotation",envir=.dico) -assign("txt_sample_size_NUM", "Taille de l'echantillon:TXT",envir=.dico) -assign("txt_saturations_sum_of_squares", "Sommes des carres des saturations",envir=.dico) -assign("txt_search_for_new_function", "rechercher une nouvelle fonction",envir=.dico) -assign("txt_second_variables_set", "Second jeu de variables",envir=.dico) -assign("txt_selected_data", "donnees que vous venez de selectionner",envir=.dico) -assign("txt_selection_method_akaike", "Methode de selection - criteres d'information d'Akaike",envir=.dico) -assign("txt_selection_method_bayesian_factor", "Methodes de selection : facteurs bayesiens",envir=.dico) -assign("txt_selection_method", "Methode de selection",envir=.dico) -assign("txt_selection_methods", "Methodes de selection",envir=.dico) -assign("txt_selection", "selection",envir=.dico) -assign("txt_select_obs", "Selectionner des observations",envir=.dico) -assign("txt_select_variables", "Selectionner des variables",envir=.dico) -assign("txt_semi_BP", "Correlation semi-partielle de Bravais Pearson",envir=.dico) -assign("txt_semicolon", "point virgule",envir=.dico) -assign("txt_semi_partial_rho", "Rho semi-partiel de Spearman",envir=.dico) -assign("txt_sequential_bayesian_factors_robustness_analysis", "Facteurs bayesiens sequentiels - Analyse de robustesse",envir=.dico) -assign("txt_shapiro_wilk", "W de Shapiro-Wilk",envir=.dico) -assign("txt_simple_mediation_effect", "Effets de mediation simple",envir=.dico) -assign("txt_slopes_homogeneity_between_groups_on_dependant_variable", "Test de l'homogeneite des pentes entre les groupes sur la variable dependante",envir=.dico) -assign("txt_spearman_kendall_corr_by_group", "Correlation de Spearman/Kendall par groupe",envir=.dico) -assign("txt_specific_val_multiplication", "multiplication d'une valeur specifique",envir=.dico) -assign("txt_specify_contrasts", "specifier vos contrastes",envir=.dico) -assign("txt_specify_model", "Specifier le modele",envir=.dico) -assign("txt_specify_working_dir", "specifier le repertoire de travail",envir=.dico) -assign("txt_spss_file", "fichier SPSS",envir=.dico) -assign("txt_square", "carree",envir=.dico) -assign("txt_rectangular", "rectangulaire",envir=.dico) -assign("txt_standardized_parameters", "Parametres standardises",envir=.dico) -assign("txt_statistic", "statistique",envir=.dico) -assign("txt_step", "etape",envir=.dico) -assign("txt_student_bootstrap_on_truncated_means", "bootstrap studentise sur les moyennes tronquees",envir=.dico) -assign("txt_student_t_by_group", "t de Student par groupe",envir=.dico) -assign("txt_student_t_independant", "t de student pour echantillons independants",envir=.dico) -assign("txt_student_t", "t de Student",envir=.dico) -assign("txt_student_t_test_norm", "Test de Student - comparaison a une norme",envir=.dico) -assign("txt_student_t_test_paired", "Test de Student - comparaison de deux echantillons apparies",envir=.dico) -assign("txt_substraction", "soustraction",envir=.dico) -assign("txt_sufficient_factors", "facteurs suffise(nt)",envir=.dico) -assign("txt_superior_or_equal_to", "superieur ou egal a",envir=.dico) -assign("txt_superior_proba", "probabilite superieure",envir=.dico) -assign("txt_superior", "Superieur",envir=.dico) -assign("txt_superior_to", "superieur a",envir=.dico) -assign("txt_supports_alternative", "En faveur de l'hypothese alternative",envir=.dico) -assign("txt_supports_null", "En faveur de l'hypothese nulle",envir=.dico) -assign("txt_suppress_all_outliers", "Suppression de l'ensemble des outliers",envir=.dico) -assign("txt_suppress_outliers_manually", "Suppression manuelle",envir=.dico) -assign("txt_synthesis_table", "Tableau de synthese",envir=.dico) -assign("txt_teaching_material", "Materiel pedagogique",envir=.dico) -assign("txt_tetra_polyc_corr_matrix_or_mixt", "Matrice de correlation tetrachorique/polychorique ou mixte",envir=.dico) -assign("txt_this_tests_if", "Cela teste si",envir=.dico) -assign("txt_threshold", "Seuil",envir=.dico) -assign("txt_time_1", "temps 1",envir=.dico) -assign("txt_time1", "temps1",envir=.dico) -assign("txt_time_2", "temps 2",envir=.dico) -assign("txt_time2", "temps2",envir=.dico) -assign("txt_tolerance", "Tolerance",envir=.dico) -assign("txt_total_sample_not_fixed", "Effectif total non fixe",envir=.dico) -assign("txt_troncature_num", "Troncature:TXT",envir=.dico) -assign("txt_truncated_means", "moyennes tronquees",envir=.dico) -assign("txt_t_test_choice", "Choix du test t",envir=.dico) -assign("txt_tucker_lewis_fiability_factor", "facteur de fiabilite de Tucker Lewis - TLI",envir=.dico) -assign("txt_two_independant_samples", "Deux echantillons independants",envir=.dico) -assign("txt_two_paired_samples", "Deux echantillons apparies",envir=.dico) -assign("txt_txt_file", "Fichier txt",envir=.dico) -assign("txt_type", "Type",envir=.dico) -assign("txt_understanding_alpha_and_power", "Comprendre alpha et la puissance",envir=.dico) -assign("txt_understanding_bayesian_inference", "Comprendre une inference bayesienne",envir=.dico) -assign("txt_understanding_central_limit_theorem", "Comprendre le theorem central limit",envir=.dico) -assign("txt_understanding_confidance_interval", "Comprendre un intervalle de confiance",envir=.dico) -assign("txt_understanding_corr_2", "Comprendre une correlation 2",envir=.dico) -assign("txt_understanding_corr", "Comprendre la correlation",envir=.dico) -assign("txt_understanding_heterogenous_variance_effects", "Comprendre les effets de variances heterogenes",envir=.dico) -assign("txt_understanding_likelihood", "Comprendre le maximum de vraisemblance",envir=.dico) -assign("txt_understanding_negative_positive_predic_power", "Comprendre le pouvoir predictif positif et le pouvoir predictif negatif",envir=.dico) -assign("txt_understanding_prev_sens_specificity_2", "Comprendre la prevalence, la sensibilite et la specificite 2",envir=.dico) -assign("txt_understanding_prev_sens_specificity", "Comprendre la prevalence, la sensibilite et la specificite",envir=.dico) -assign("txt_upper_bound_rmsea", "limite superieure du RMSEA",envir=.dico) -assign("txt_user_exited_easieR", "Vous avez quitte easieR",envir=.dico) -assign("txt_values", "valeurs",envir=.dico) -assign("txt_value", "valeur",envir=.dico) -assign("txt_variable_descriptive_statistics", "Statistiques descriptives de la variable",envir=.dico) -assign("txt_variables_coeff_matrix", "Matrice de coefficients variables",envir=.dico) -assign("txt_variables_contribution_to_model", "Contribution des variables au modele",envir=.dico) -assign("txt_variables_from_step", "Variable(s) de cette etape",envir=.dico) -assign("txt_verify_packages_install", "Verifier l installation des packages",envir=.dico) -assign("txt_view_data", "voir des donnees",envir=.dico) -assign("txt_VIF","FIV",envir=.dico) -assign("txt_warning", "Avertissement",envir=.dico) -assign("txt_wilcoxon_by_group", "Wilcoxon par groupe",envir=.dico) -assign("txt_without_outliers", "Donnees sans valeur influente",envir=.dico) -assign("txt_without_welch_correction", "sans correction de Welch",envir=.dico) -assign("txt_without_yates_correction", "Sans correction de Yates",envir=.dico) -assign("txt_with_welch_correction", "avec correction de Welch",envir=.dico) -assign("txt_with_yates_correction", "Avec correction de Yates",envir=.dico) -assign("txt_working_dir", "Repertoire de travail",envir=.dico) -assign("txt_x_axis_variables", "Variable-s en abcisse",envir=.dico) -assign("txt_XY_correlation", "Correlation entre XY",envir=.dico) -assign("txt_XY_NUM_correlation", "Correlation entre XY:TXT",envir=.dico) -assign("txt_XZ_correlation", "Correlation entre XZ",envir=.dico) -assign("txt_XZ_NUM_correlation", "Correlation entre XZ:TXT",envir=.dico) -assign("txt_y_axis_variables", "Variable-s en ordonnee",envir=.dico) -assign("txt_yes", "oui",envir=.dico) -assign("txt_your_data", "Vos donnees",envir=.dico) -assign("txt_YZ_correlation", "Correlation entre YZ",envir=.dico) -assign("txt_YZ_NUM_correlation", "Correlation entre YZ:TXT",envir=.dico) -assign("ask_probability_correction", "Which p adjustment do you want ? If you do not want any p adjust, choose +none+",envir=.dico) -assign("ask_contrasts_must_be_ortho", "The contrasts must be orthogonal. Do you want to continue ?",envir=.dico) -assign("desc_bayesian_factors_chosen_in", "Facteurs bayesiens is choosen in ",envir=.dico) -assign("desc_cross_validation_issues", "cross validation is encountering some issues",envir=.dico) -assign("desc_easier_metapackage", "easieR: An R metapackage. Retrieved from https://github.com/NicolasStefaniak/easieR",envir=.dico) -assign("desc_first_time_easier", "If you are using easieR for the first time, please use the function ez.install in order to ensure that easieR will work properly.n Si vous utilisez easieR pour la 1e fois, veuillez utiliser la fonction ez.install pour vous assurer de bon fonctionnement de easieR.",envir=.dico) -assign("ask_chose_variables", "veuillez choisir la ou les variables ",envir=.dico) -assign("ask_correlations_type", "Type de correlations ?",envir=.dico) -assign("ask_dependant_variable_name", "Quel est le nom de la variable dependante?",envir=.dico) -assign("ask_factors_number", "Nombre de facteurs ?",envir=.dico) -assign("ask_filename", "Quel nom voulez-vous donner au fichier?",envir=.dico) -assign("ask_independant_variable_name", "Quel est le nom de la variable independante?",envir=.dico) -assign("ask_is_long_format_correct", "Est-ce que la structure dans un format long de vos donnees est correcte ?",envir=.dico) -assign("ask_model", "Modele ?",envir=.dico) -assign("ask_ordinal_variables", "Variables ordinales ?",envir=.dico) -assign("ask_save_results", "Enregistrer les resultats ?",envir=.dico) -assign("ask_save", "Voulez-vous sauvegarder ?",envir=.dico) -assign("ask_specify_contrasts", "Veuillez spĂ©cifier les contrastes.",envir=.dico) -assign("ask_variables", "Quelles sont les variables a selectionner ?",envir=.dico) -assign("ask_variables_type", "Nature des variables ?",envir=.dico) -assign("ask_what_to_do", "Que voulez-vous faire ?",envir=.dico) -assign("ask_which_analysis", "Quelle analyse voulez-vous?",envir=.dico) -assign("desc_all_contrasts_description", "Les contrastes a priori correspondent aux contrastes qui permettent de tester des hypotheses a priori.\nLes contrastes 2 a 2 permettent de faire toutes les comparaisons 2 a 2 en appliquant ou non une correction a la probabilite",envir=.dico) -assign("desc_contrasts_must_be_coeff_matrices_in_list", "Les contrates doivent etre des matrices de coefficients placees dans une list dont le nom de chaque niveau correspond a un facteur",envir=.dico) -assign("desc_percentage_outliers", "% d'observations considerees comme influentes",envir=.dico) -assign("desc_robusts_statistics_could_not_be_computed_verify_WRS", "Les statistiques robustes n'ont pas pu etre realisees. Verifiez l'installation du package WRS",envir=.dico) -assign("desc_some_participants_have_missing_values_on_repeated_measures", "Certains participants ont des valeurs manquantes sur les facteurs en mesures repetees. Ils vont etre supprimes des analyses",envir=.dico) -assign("txt_absence_of_difference_between_groups_test_on", "Test de l'absence de difference entre les groupes sur ",envir=.dico) -assign("txt_anova_on_medians", "Anova sur les medianes",envir=.dico) -assign("txt_anova_on_m_estimator", "ANOVA sur M estimator",envir=.dico) -assign("txt_bayesian_factors", "Facteurs bayesiens",envir=.dico) -assign("txt_BP_correlation", "Correlation de Bravais-Pearson",envir=.dico) -assign("txt_center", "centrer",envir=.dico) -assign("txt_cohen_d", "D de Cohen",envir=.dico) -assign("txt_correlations", "Correlations",envir=.dico) -assign("txt_correlations_matrix", "Matrice de correlations",envir=.dico) -assign("txt_descriptive_statistics_of_interaction_between_x", "Statistiques descriptives de l'interaction entre",envir=.dico) -assign("txt_descriptive_statistics", "Statistiques descriptives",envir=.dico) -assign("txt_empirical_chi_square_proba_value", "valeur de la probabilite du chi carre empirique",envir=.dico) -assign("txt_factor", "facteur.",envir=.dico) -assign("txt_friedman_anova", "Anova de Friedman",envir=.dico) -assign("txt_import_results", "importer des resultats",envir=.dico) -assign("txt_interface_objects_in_memory", "Interface - objets en memoire, nettoyer la memoire, repertoire de travail, langue",envir=.dico) -assign("txt_intraclass_correlation", "Correlation intra-classe",envir=.dico) -assign("txt_kruskal_wallis_pairwise", "Test de Kruskal-Wallis - Comparaison deux a deux",envir=.dico) -assign("txt_kruskal_wallis_test", "Test de Kruskal-Wallis",envir=.dico) -assign("txt_latent_variables_intercept", "Intercept des variables latentes [int.lv.free=FALSE]",envir=.dico) -assign("txt_observed_variables_intercept", "Intercept des variables observees [int.ov.free=FALSE]",envir=.dico) -assign("txt_logistic_regressions", "Regressions logistiques",envir=.dico) -assign("txt_mauchly_test_sphericity_covariance_matrix", "Test de Mauchly testant la sphericite de la matrice de covariance",envir=.dico) -assign("txt_none", "aucun",envir=.dico) -assign("txt_non_param_analysis", "Analyse non parametrique",envir=.dico) -assign("txt_normality_tests", "Tests de normalite",envir=.dico) -assign("txt_pairwise_comparisons", "Comparaisons 2 a 2",envir=.dico) -assign("txt_pairwise", "pairwise",envir=.dico) -assign("txt_partial_corr_BP", "Correlation partielle de Bravais-Pearson",envir=.dico) -assign("txt_preprocess_sort_select_operations", "Pretraitements (tri, selection, operations mathematiques, Traitement des valeurs manquantes)",envir=.dico) -assign("txt_press_enter_to_continue", "Appuyez sur [entree] pour continuer",envir=.dico) -assign("txt_regressions", "regressions",envir=.dico) -assign("txt_repeated_measures", "Mesures repetees",envir=.dico) -assign("txt_sample_size", "taille de l'echantillon",envir=.dico) -assign("txt_test_model", "Modele teste",envir=.dico) -assign("txt_variables", "variables",envir=.dico) -assign("txt_variable", "variable",envir=.dico) -assign("desc_corr_group_analysis_spec", "Si vous souhaitez realiser l'analyse pour differents sous-echantillons en fonction d'un critere categoriel (i.e; realiser une analyse par groupe) \n choisissez oui. Dans ce cas, l'analyse est realisee sur l'echantillon complet et sur les sous-echantillons. \n Si vous desirez l'analyse pour l'echantillon complet uniquement, chosissez non. \n l'analyse par groupe ne s'appliquent pas aux statistiques robustes.",envir=.dico) -assign("desc_outliers_removal_implications", "Supprimer l'ensemble des outliers supprime l'ensemble des valeurs au-delĂ  p(chi.deux)< 0.001. Supprimer une observation Ă  la fois permet de faire une analyse detaillee de chaque observation consideree comme influente en partant de la valeur la plus extreme. La procedure s'arrete quand plus aucune observation n'est consideree comme influente",envir=.dico) -assign("txt_bilateral", "Bilateral",envir=.dico) -assign("desc_no_compatible_object_in_mem_for_aov", "il n'y a pas d'objet compatible avec aov.plus dans la memoire de R. Vous devez realiser une analyse de variance au prealable",envir=.dico) -assign("desc_this_function_means_and_sd_adjusted_interaction_effect_possible", "Cette fonction permet de fournir les moyennes et erreurs-types ajustees ainsi que le graphique correspondant. Avec le choix post hoc sur les interactions, vous pouvez tester les effets d'interaction 2 a 2 et les effet simples.",envir=.dico) -assign("txt_anova_plus", "Anova plus",envir=.dico) -assign("desc_center_and_center_reduce_explaination", "Centrer permet d'avoir une moyenne a zero en maintenant l'ecart-type. Centrer reduire correspond a la formule du z. La moyenne est de 0 et l'ecart-type vaut 1. La probabilite inferieure correspond a la probabilite d'avoir un z inferieur ou egal au z. La probabilite superieure correspond a la probabilite d'avoir un z superieur ou egal au z",envir=.dico) -assign("desc_proba_sum_is_not_one_or_not_enough_proba", "La somme des probabilites est differente de 1 ou le nombre de probabilites ne correspond pas au nombre de modalites de la variable. Veuillez entrer un vecteur de probabilites valide",envir=.dico) -assign("desc_if_non_fixed_sample_poisson_law", "Si l'effectif total est non fixe, on fait l'hypothese que les observations surviennent en respectant une loi de poisson. La repartition sur les niveaux d'un facteur surviennent avec une probabilite fixe. La distribution est une distribution poisson",envir=.dico) -assign("desc_distribution_is_joint_multinomial", "L'option *Effectif total fixe* doit etre choisi si on fait l'hypohese nulle que la repartition dans chacune des cellules du tableau est fixee. La distribution est une distribution multinomiale jointe",envir=.dico) -assign("desc_distribution_is_independant_multinomial", "L'option Effectif total fixe pour les lignes* doit etre choisi si les effectifs pour chaque ligne est identique, comme lorsqu'on veut s'assurer d'un appariement entre groupes. La distribution est une distribution multinomiale independante",envir=.dico) -assign("desc_corr_detailed_analysis", "l'analyse detaillee permet d'avoir les statistiques descriptives, les tests de normalite, le nuage de points, \n des statistiques robustes, l'ensemble des coefficients de correlations. \n la matrice de correlation permet de contrĂ´ler l'erreur de 1e espece et est adaptee pour un grand nombre de correlations \n la comparaison de correlations permet de comparer 2 correlations dependantes ou independantes \n Le choix + autre correlations + permet d'avoir les correlation tetrachoriques et polychoriques",envir=.dico) -assign("desc_corr_values_must_be_between_min_1_and_1", "Les valeurs des correlations doivent etre comprises entre -1 et 1/n et les effectifs doivent etre des entiers positifs",envir=.dico) -assign("desc_you_can_choose_contrasts_you_want", "Vous pouvez choisir les contrastes que vous souhaitez. Neanmoins les regles concernant l'application des contrastes doivent etre respectees. Les contrastes peuvent etre specifies manuellement. Dans ce cas, veuillez choisir specifier les contrastes",envir=.dico) -assign("desc_square_matrix_rectangular_matrix", "Une matrice carree est une matrice avec toutes les Correlations 2 a 2. Une matrice rectangulaire est une matrice dans laquelle un premier ensemble de variables est mis en correlations avec un second jeu de variables",envir=.dico) -assign("desc_complete_dataset_vs_identification_outliers_vs_without_outliers", "les donnees completes representent l'analyse classique sur toutes les donnees utilisables, l'identification des valeurs influentes permet d'identifier les observations qui sont considerees statistiquement comme influencant les resultats. les analyses sur les donnees sans les valeurs influentes realise l'analyse apres suppression des valeurs influentes. Cette option stocke dans la memoire de R une nouvelle base de donnees sans valeur influente dans un objet portant le nom *nettoyees*",envir=.dico) -assign("desc_welcome_in_easieR", "Welcome in easieR - For more information, please visit :https://theeasierproject.wordpress.com/",envir=.dico) -assign("ask_variables_type_for_anova", "Veuillez preciser le(s) type(s) de variable(s) que vous souhaitez inclure dans l'analyse.\nVous pouvez en choisir plusieurs (e.g., pour anova mixte ou des ancova",envir=.dico) -assign("ask_correction_anova_contrasts", "Correction ?",envir=.dico) -assign("txt_independant_groups", "Groupes independants",envir=.dico) -assign("txt_covariables", "Covariables",envir=.dico) -assign("txt_cfa_information_default", "information [information=default]",envir=.dico) -assign("txt_cfa_continuity_correction_zero_keep_margins_default", "correction de continuite [zero.keep.margins=default]",envir=.dico) -assign("txt_cfa_estimator_ml_default", "estimateur [estimator=ml])",envir=.dico) -assign("txt_cfa_groups_null_default", "groupes [group=NULL]",envir=.dico) -assign("txt_cfa_test_standard_default", "test [test=standard]",envir=.dico) -assign("txt_cfa_standard_error_default", "erreur standard [se=standard]",envir=.dico) -assign("txt_cfa_observed_variabes_standardization_true_default", "standardisation des variables observees [std.ov=T]",envir=.dico) -assign("txt_cfa_latent_variables_indicators_estimates_true_default", "Estimation des indicateurs des variables latentes [std.lv=FALSE]",envir=.dico) -assign("desc_wls_corresponds_to_adf_plus_explaination_other_estimators", "[WLS] correspond a [ADF]. Les estimateurs avec les extensions [M],[MV],[MVSF],[R] sont des versions robustes des estimateurs classiques [MV],[WLS], [DWLS], [ULS]",envir=.dico) -assign("ask_observed_variables_intercept_zero", "Intercept VO=0 ?",envir=.dico) -assign("ask_latent_variables_intercept_zero", "Intercept VL=0 ?",envir=.dico) -assign("ask_how_to_treat_exaequo_rank", "Comment voulez-vous traiter les ex-aequo ? La methode *average* fait la moyenne entre les ex aequo (le plus habituel), *first* attribue le premier rang ex aequo a la premiere valeur dans les donnees, *laste* a la derniere, *min* attribue la valeur minimale a l'ensemble des ex aequo et *max* la valeur maximale.",envir=.dico) -assign("desc_for_ordinal_and_dicho_varible_prefer_min_res", "Pour les variables ordinales et dichomiques, preferez la methode du minimum des residus - minres - ou des moindres carres ponderes - wls. Pour les variables continues, le maximum de vraisemblance si la normalite est respectee - ml",envir=.dico) -assign("desc_saturation_criterion_show_only_above_threshold", "Le critere de saturation permet de n'afficher dans le tableau de resultats que les saturation superieure au seuil fixe",envir=.dico) -assign("desc_to_find_new_analysis_search_in_english", "Pour trouver une nouvelle analyse, il est necessaire de faire votre recherche en anglais. Vous pouvez utiliser plusieurs mots dans la recherche. Une page html reprenant l'ensemble des packages faisant reference a l'analyse recherchee va s'ouvrir.",envir=.dico) -assign("txt_division", "division",envir=.dico) -assign("desc_if_you_select_both_operations_value_will_be_added_to_chose_cols", "Si vous selectionnez les deux options en meme temps, la valeur specifiee sera ajoutee a l'ensemble des colonnes choisies et ensuite les colonnes choisies seront additionnees. Pour additionner une valeur specifique au total, veuillez choisir l'option addition de colonnes uniquement.",envir=.dico) -assign("desc_if_you_select_both_operations_value_will_be_multiplied_to_chose_cols", "Si vous selectionnez les deux options en meme temps, la valeur specifiee sera multipliee a l'ensemble des colonnes choisies et ensuite les colonnes choisies seront multipliees entre elles. Pour multiplier une valeur specifique au total, veuillez choisir l'option multipication de colonnes uniquement.",envir=.dico) -assign("ask_chose_values_on_left_of_minus_symbol", "Veuillez selectionner les valeurs situees a gauche du symbole *moins*. Si plusieurs variables sont selectionnees, les regles du calcul matriciel sont appliques.",envir=.dico) -assign("desc_one_or_same_number_cols_on_both_sides_only", "Il ne doit y avoir qu'une colonne ou le nombre de colonnes a droite du symbole *moins* doit etre egal au nombre de colonnes a gauche du symbole *moins*",envir=.dico) -assign("ask_specify_exponant_value", "Veuillez preciser la valeur de l'exposant. NOTE : Pour les racines, l'exposant est l'inverse la valeur. Par exemple, La racine carree vaut 1/2, la racine cubique 1/3... ",envir=.dico) -assign("desc_expression_must_be_correct_example", "L'expression doit etre correcte. Vous pouvez utiliser directement le nom des variables les operateurs sont +,-,*,/,^,(,). Une expression correcte serait :",envir=.dico) -assign("ask_chose_relation_between_vars_regressions_log", "Veuillez choisir le(s) type(s) de relations entre les variables. Les effets additifs prennent la forme de y=X1+X2 tandis que les effets d'interaction prennent la forme de Y=X1+X2+X1:X2",envir=.dico) -assign("ask_variables_order_for_max_likelihood", "L'ordre d'entree des variables est important pour le calcul du maximum de vraisemblance. Veuillez preciser l'ordre d'entree des variables",envir=.dico) -assign("ask_integrate_probabilities_to_dataset", "voulez-vous integrer les probabilites a votre base de donnees ?",envir=.dico) -assign("ask_specify_other_options_regressions", "Voulez-vous preciser d'autres options ? Vous pouvez en selectionner plusieurs. Les methodes de selection permettent de selectionner le meilleur modele sur la base de criteres statistiques. Les modeles hierarchiques permettent de comparer plusieurs modeles. Les validations croisees permettent de verifier si un modele n'est pas dependant des donnees. Cette option est a utiliser notamment avec les methodes de selection. L'analyse par groupe permet de realiser la meme regression pour des sous-groupes. Les mesures d'influences sont les autres mesures habituellement utilisees pour identifier les valeurs influentes.",envir=.dico) -assign("desc_possible_apply_multiple_selection_criterion", "Il est possible d'appliquer plusieurs criteres de selection simultanement, impliquant ou non plusieurs variables. Veuillez preciser le nombre de variables sur lesquelles vous desirez appliquer un ou plusieurs criteres de selection. Veuillez choisir les variables sur lesquelles vous deirez appliquer une selection",envir=.dico) -assign("desc_skew_and_kurtosis_between_1_and_3", "Type de skew et kurtosis, doit se situer entre 1 et 3:TXT",envir=.dico) -assign("desc_with_two_equal_means_ratio_must_be_5_percent", "Avec deux moyennes egales, ou pratiquement egales, le taux d'erreurs doit etre de 5%. Modifiez progressivement l'ecart entre les ecart-types et voyez comment le taux d'erreur alpha va etre modifie",envir=.dico) -assign("desc_bilateral_superior_inferior_test_t", "Une analyse bilaterale teste l'existence d'une difference. Le choix superieur teste si la moyenne est strictement superieure \n Le choix inferieur teste l'existence d'une difference strictement inferieure",envir=.dico) -assign("txt_numeric_variables", "Variables numĂ©riques",envir=.dico) -assign("txt_select_language", "Choisir la langue",envir=.dico) -assign("txt_dot_adjusted", ".ajustee",envir=.dico) -assign("txt_bca_inferior_limit", "Bca lim inf",envir=.dico) -assign("txt_bca_inferior_limit", "Bca.lim.inf",envir=.dico) -assign("txt_bca_superior_limit", "Bca.lim.sup",envir=.dico) -assign("txt_bca_superior_limit", "Bca lim sup",envir=.dico) -assign("txt_bca_superior_limit", "Bca.lim.sup",envir=.dico) -assign("txt_centered_dot_reduced", "centrer.reduite",envir=.dico) -assign("txt_chi_dot_squared", "chi.2",envir=.dico) -assign("txt_chi_dot_squared_model", "chi.2.modele",envir=.dico) -assign("txt_chi_dot_squared", "chi.carre",envir=.dico) -assign("txt_chi_dot_squared", "chi.deux",envir=.dico) -assign("txt_chi_dot_squared_adjustment", "chi.deux d'ajustement",envir=.dico) -assign("txt_pairwise_comparison", "comparaison 2 a 2",envir=.dico) -assign("txt_continuous", "continues",envir=.dico) -assign("txt_greenhouse_geisser_huynn_feldt_correction", "Correction : Greenhouse-Geisser & Hyunh-Feldt",envir=.dico) -assign("txt_df", "ddl",envir=.dico) -assign("txt_df1", "ddl1",envir=.dico) -assign("txt_df_parenthesis_1", "Ddl(1)",envir=.dico) -assign("txt_df2", "ddl2",envir=.dico) -assign("txt_df_parenthesis_2", "Ddl(2)",envir=.dico) -assign("txt_df_denom", "ddl.denom",envir=.dico) -assign("txt_df_parenthesis_denom", "Ddl (dnom)",envir=.dico) -assign("txt_df_effect", "ddl.effet",envir=.dico) -assign("txt_df_num", "ddl.num",envir=.dico) -assign("txt_df_parenthesis_num", "Ddl (num)",envir=.dico) -assign("txt_df_predictor", "ddl predicteur",envir=.dico) -assign("txt_df_residual", "ddl.resid",envir=.dico) -assign("txt_df_residuals", "ddl.residuels",envir=.dico) -assign("txt_delta_r_squared", "Delta R.deux",envir=.dico) -assign("txt_error", "Erreur",envir=.dico) -assign("txt_error_BP", "Erreur.BP",envir=.dico) -assign("txt_error_spearman", "Erreur.Spearman",envir=.dico) -assign("txt_error_dot_standard_short", "erreur.st",envir=.dico) -assign("txt_error_dot_standard", "erreur.standard",envir=.dico) -assign("txt_error_dot_standard", "Erreur.standard",envir=.dico) -assign("txt_space", "espace",envir=.dico) -assign("txt_estimator", "estimateur",envir=.dico) -assign("txt_global_model_estimate", "Estimation du modele global",envir=.dico) -assign("txt_hf_p_value", "HF.valeur.p",envir=.dico) -assign("txt_ci_inferior", "IC Inf",envir=.dico) -assign("txt_ci_inferior_limit", "IC lim inf",envir=.dico) -assign("txt_ci_superior_limit", "IC lim sup",envir=.dico) -assign("txt_ci_superior", "IC Sup",envir=.dico) -assign("txt_large", "large",envir=.dico) -assign("txt_large_half", "large - 0.5",envir=.dico) -assign("txt_inferior_limit", "lim.inf",envir=.dico) -assign("txt_ci_inferior_limit_dot", "lim.inf.IC",envir=.dico) -assign("txt_ci_inferior_limit_dot", "Lim.inf.IC",envir=.dico) -assign("txt_ci_superior_limit", "lim.sup",envir=.dico) -assign("txt_ci_superior_limit_dot", "lim.sup.IC",envir=.dico) -assign("txt_ci_superior_limit_dot", "Lim.sup.IC",envir=.dico) -assign("txt_r_squared_matrix", "matrice des r.deux",envir=.dico) -assign("txt_truncated_m", "M.tronquee",envir=.dico) -assign("txt_multiplied_by", "multiplie.par",envir=.dico) -assign("txt_dot_cleaned", ".nettoyees",envir=.dico) -assign("txt_cleaned", "nettoyees",envir=.dico) -assign("txt_bootstrap_dot_number", "Nombre.bootstrap",envir=.dico) -assign("txt_odd_ratio_dot", "Odd.ratio",envir=.dico) -assign("desc_install_bad_packages", "Package.mal.installes",envir=.dico) -assign("desc_install_correct_packages", "packages.installes.correctement",envir=.dico) -assign("txt_critical_p_corrected", "p.critique.corrigee",envir=.dico) -assign("txt_percentile_inferior_limit_dot", "Percentile.lim.inf",envir=.dico) -assign("txt_percentile_superior_limit_dot", "Percentile.lim.sup",envir=.dico) -assign("txt_percentage_removed_obs", "Pourcentage.obs.retirees",envir=.dico) -assign("txt_percent_removed_obs", "Pourcent.obs.retirees",envir=.dico) -assign("txt_r_dot_square", "r.carre",envir=.dico) -assign("txt_r_square", "R carre",envir=.dico) -assign("txt_r_dot_square", "R.carre",envir=.dico) -assign("txt_r_dot_two", "r.deux",envir=.dico) -assign("txt_r_dot_two", "R.deux",envir=.dico) -assign("txt_r_dot_two_adjusted", "R.deux.aj",envir=.dico) -assign("txt_log_regression_dot", "Regressions.logistique",envir=.dico) -assign("txt_multiple_regressions_dot", "regressions.multiples",envir=.dico) -assign("txt_multiple_regressions_dot", "Regressions.multiples",envir=.dico) -assign("txt_rho_dot_square", "rho.deux",envir=.dico) -assign("txt_critical_dot_threshold", "seuil.critique",envir=.dico) -assign("txt_critical_dot_threshold", "Seuil.critique",envir=.dico) -assign("txt_spearman_df", "Spearman.ddl",envir=.dico) -assign("txt_specificity", "specifite",envir=.dico) -assign("txt_ultrawide", "ultra large",envir=.dico) -assign("txt_ultrawide", "ultralarge",envir=.dico) -assign("txt_ultrawide_val", "ultra large - 0.707",envir=.dico) -assign("txt_absolute_dot_val", "valeur.absolue.",envir=.dico) -assign("txt_contrast_dot_val", "Valeur.contraste",envir=.dico) -assign("txt_critical_dot_val", "Valeur.critique",envir=.dico) -assign("txt_p_dot_val", "valeur.p",envir=.dico) -assign("txt_p_dot_val_lilliefors", "valeur.p Llfrs",envir=.dico) -assign("txt_p_dot_val_sw", "valeur.p SW",envir=.dico) -assign("txt_test_dot_val", "Valeur.test",envir=.dico) -assign("txt_z_dot_val", "valeur.Z",envir=.dico) -assign("txt_value", "value",envir=.dico) -assign("txt_vector_length_zero", "vector of length zero",envir=.dico) -assign("txt_kendall_w", "W.de.Kendall",envir=.dico) -assign("txt_synthesis", "Synthèse",envir=.dico) -assign("txt_truncated_mean_0_2", "Test sur la moyenne tronquĂ©e Ă  0.2",envir=.dico) -assign("txt_cramer_v_square", "V.carre",envir=.dico) -assign("txt_effect_size_dot", "Taille.effet",envir=.dico) -assign("txt_gg_p_value", "GG.valeur.p",envir=.dico) -assign("txt_var_explained_dot", "Var.expliquee",envir=.dico) -assign("V.sq","V.carre", envir=.dico) - - - -} + .dico <<- new.env(parent=emptyenv()) + assign("ask_2x2_table" , "tableau 2x2 ?" , envir=.dico) + assign("ask_2x2_table_value" , "Veuillez preciser la valeur pour les tableaux 2x2" , envir=.dico) + assign("ask_add_a_value_to_empty_cells" , "Faut-il ajouter une valeur aux cellules vides pour les correlations polychorique ? Pour specifier les valeurs,choisissez TRUE, sinon choisissez [default]" , envir=.dico) + assign("ask_add_value_to_total" , "voulez-vous encore ajouter une valeur au total ?" , envir=.dico) + assign("ask_analysis_by_group" , "Analyse par groupe?" , envir=.dico) + assign("ask_analysis_on_complete_data_or_remove_outliers" , "Desirez-vous l'analyse sur les donnees completes ou sur les donnees pour lesquelles les valeurs influentes ont ete enlevees ?" , envir=.dico) + assign("ask_analysis_type" , "Quelle analyse voulez-vous realiser?" , envir=.dico) + assign("ask_are_frequences_free_parameters" , "est-ce que les frequences des differents group est un parametre libre ? " , envir=.dico) + assign("ask_are_there_inversed_items" , "Y a-t-il des items inverses ?" , envir=.dico) + assign("ask_are_you_ready" , "etes-vous pret?" , envir=.dico) + assign("ask_baseline" , "Quelle est la ligne de base?" , envir=.dico) + assign("ask_bigger_tables_value" , "Veuillez preciser la valeur pour les tableaux plus grand que 2x2" , envir=.dico) + assign("ask_bootstrap_number_min_500" , "veuillez preciser le nombre de bootstrap. Un minimum de 500 est idealement requis. Peut prendre du temps pour N>1000" , envir=.dico) + assign("ask_bootstrap_numbers_1_for_none" , "Veuillez preciser le nombre de bootstrap. Pour ne pas avoir de bootstrap, choisir 1" , envir=.dico) + assign("ask_bootstraps_number" , "Nombre de bootstrap ?" , envir=.dico) + assign("ask_cancel_entered_value_not_num" , "la valeur que vous avez entree n'est pas numerique.Voulez-vous annuler cette analyse ?" , envir=.dico) + assign("ask_cauchy_apriori_distribution" , "Veuillez preciser la distribution a priori de Cauchy" , envir=.dico) + assign("ask_center" , "Centrer?" , envir=.dico) + assign("ask_center_numeric_variables" , "Voulez-vous centrer les variables numeriques ? Centrer est generalement conseille (e.g., Schielzeth, 2010)." , envir=.dico) + assign("ask_chi_squared_type" , "Veuillez preciser le type de chi carre que vous souhaitez realiser." , envir=.dico) + assign("ask_choose_a_variable_with_at_least_two_modalities" , "Une variable categorielle doit avoir au moins 2 modalites differentes. Veuillez choisir une variable avec au moins deux modalites" , envir=.dico) + assign("ask_chose_analysis" , "Veuillez choisir l'analyse que vous desirez realiser." , envir=.dico) + assign("ask_chose_categorial_ranking_factor" , "Veuillez choisissez le facteur de classement categoriel." , envir=.dico) + assign("ask_chose_cols_corresponding_to_repeated_measures" , "Veuillez choisir l'ensemble des colonnes correspondant aux modalites des variables en mesures repetees" , envir=.dico) + assign("ask_chose_covariables" , "Veuillez choisir la ou les covariables" , envir=.dico) + assign("ask_chose_database" , "Veuillez choisir la base de donnees" , envir=.dico) + assign("ask_chose_defining_groups" , "Veuillez choisir la definissant les groupes" , envir=.dico) + assign("ask_chose_dependant_variable" , "Veuillez choisir la variable dependante." , envir=.dico) + assign("ask_chose_first_judge" , "Veuillez choisir le premier juge" , envir=.dico) + assign("ask_chose_independant_group_variables" , "Veuillez choisir les variable-s a groupes independants" , envir=.dico) + assign("ask_chose_interaction_model_predictors" , "Veuillez choisir les predicteurs a entrer dans le modele d'interaction. Il est necessaire d'avoir au moins deux variables" , envir=.dico) + assign("ask_chose_manifest_variables_at_least_three" , "Veuillez choisir les variables manifestes que vous desirez analyser. Vous devez choisir au moins 3 variables" , envir=.dico) + assign("ask_chose_ranking_categorial_factor" , "Veuillez choisir le facteur de classement categoriel." , envir=.dico) + assign("ask_chose_rotation" , "Veuillez choisir le type de rotation. Oblimin est adapte en sciences humaines" , envir=.dico) + assign("ask_chose_sample_variables" , "Veuillez choisir la ou les variables definissant les effectifs" , envir=.dico) + assign("ask_chose_second_judge" , "Veuilez choisir le second juge" , envir=.dico) + assign("ask_chose_selection_method" , "Veuillez choisir la methode de selection que vous souhaitez utiliser" , envir=.dico) + assign("ask_chose_the_working_dir" , "Veuillez choisir le repertoire de travail" , envir=.dico) + assign("ask_chose_variables_at_least_five" , "Veuillez choisir les variables que vous desirez analyser. Vous devez choisir au moins 5 variables" , envir=.dico) + assign("ask_chose_variables_at_least_three" , "Veuillez choisir les variables que vous desirez analyser. Vous devez choisir au moins 3 variables" , envir=.dico) + assign("ask_chose_variable" , "Veuillez choisir les variables que vous desirez analyser." , envir=.dico) + assign("ask_chose_variable_x_axis" , "Veuillez choisir la variable en abcisse" , envir=.dico) + assign("ask_chose_variable_y_axis" , "Veuillez choisir la variable en ordonnee" , envir=.dico) + assign("ask_coding_criterion" , "Quel critere de codage voulez-vous ?" , envir=.dico) + assign("ask_col_separation_index" , "Lors de l'enregistrement de votre fichier, quel est l'indice de separation des colonnes ?" , envir=.dico) + assign("ask_complete_or_outliers" , "Voulez-vous realiser les analyses sur les donnees completes ou sur les donnees sans les valeurs influentes ?" , envir=.dico) + assign("ask_constant_parameters" , "Parametres constants ?" , envir=.dico) + assign("ask_continue" , "Continuer ?" , envir=.dico) + assign("ask_contrast_must_respect_ortho" , "Les contrastes doivent respecter l orthogonalite. Voulez-vous continuer ?" , envir=.dico) + assign("ask_control_variables" , "Veuillez preciser la ou les variables a controler" , envir=.dico) + assign("ask_convert_dependant_variable_to_dichotomic" , "voulez-vous convertir la variable dependante en une variable dichotomique, ?" , envir=.dico) + assign("ask_correction_desired" , "Veuillez preciser le type de correction de la probabilite que vous desirez realiser" , envir=.dico) + assign("ask_correction_type" , "Type de correction ?" , envir=.dico) + assign("ask_correlated_or_orthogonal_factors" , "Est-ce que les facteurs sont correles (FALSE) ou sont-ils orthogonaux (TRUE)?" , envir=.dico) + assign("ask_correlation_matrix_could_not_be_computed" , "La matrice de correlation n'a pu etre realisee. Voulez-vous reessayer ?" , envir=.dico) + assign("ask_correlation_type" , "Veuillez choisir le type de correlations que vous desirez realiser. Pour les variables dichotomiques, les correlations seront des correlations tetrachoriques" , envir=.dico) + assign("ask_corr_or_partial_correlations" , "Correlations ou correlations partielles?" , envir=.dico) + assign("ask_could_not_converge_model_verify_correlation_matrix" , "Nous n'avons pas reussi a faire converger le modele. Veuillez verifier votre matrice de correlations et reessayer avec d'autres parametres" , envir=.dico) + assign("ask_could_not_finish_analysis_respecify_parameters" , "Nous n'avons pas pu terminer correctement l'analyse. Veuillez tenter de respecifier les parametres" , envir=.dico) + assign("ask_covariables" , "Covariable-s ?" , envir=.dico) + assign("ask_criterion_for_dichotomy" , "Veuillez specifier le critere sur lequel vous souhaitez dichotomiser votre variable.Vous pouvez utiliser la mediane ou choisir un seuil specifique." , envir=.dico) + assign("ask_criterion_for_obs_to_keep" , "Veuillez specifier les criteres des observations que vous desirez conserver/garder." , envir=.dico) + assign("ask_criterion_for_variable" , "Quel critere voulez-vous utiliser pour la variable" , envir=.dico) + assign("ask_data" , "Donnees ?" , envir=.dico) + assign("ask_data_format" , "Quel est le format de vos donnees?" , envir=.dico) + assign("ask_decimal_symbol" , "Si certaines donnees contiennent des decimales, quel est le symbole indiquant la decimale ?" , envir=.dico) + assign("ask_denominator_variable_or_value" , "Le denominateur est-il une variable ou une valeur ? " , envir=.dico) + assign("ask_denominator_variable" , "Veuillez selectionner la variable au denominateur " , envir=.dico) + assign("ask_dependant_variable_with_less_than_three_val_verify_dataset" , "La variable dependante a moins de trois valeurs differentes. Verifiez vos donnees ou l'analyse que vous tentez de realiser n'est pas pertinente." , envir=.dico) + assign("ask_did_not_specify_nb_factors_repeated_measure_exit" , "Vous n avez pas precise le nombre de facteurs en mesure repetee, voulez-vous quitte ?" , envir=.dico) + assign("ask_distribution" , "Distribution ?" , envir=.dico) + assign("ask_distribution_type" , "Quelle distribution voulez-vous ?" , envir=.dico) + assign("ask_empty_cells" , "Cellules vides ?" , envir=.dico) + assign("ask_enter_different_values" , "Veuillez entrer les differentes valeurs" , envir=.dico) + assign("ask_enter_number_of_to_be_removed_variable" , "Vous devez entrer le numero permettant de savoir quelle observation doit etre supprimee." , envir=.dico) + assign("ask_exit_because_of_alpha_on_non_matrix" , "Vous essayez de faire un alpha sur autre chose qu'un matrice. Voulez-vous sortir de cette analyse?" , envir=.dico) + assign("ask_exit_no_lower_bound_specified" , "Vous n'avez pas precise la limite inferieure. Voulez-vous quitter la selection ?" , envir=.dico) + assign("ask_exit_no_upper_bound_specified" , "Vous n'avez pas precise la limite superieure. Voulez-vous quitter la selection ?" , envir=.dico) + assign("ask_exportation_filename" , "Quel nom voulez-vous attribuer au fichier ?" , envir=.dico) + assign("ask_factorial_scores" , "Scores factoriels?" , envir=.dico) + assign("ask_factors_number_for_hierarchical_structure" , "Veuillez preciser le nombre de facteurs de la structure hierarchique." , envir=.dico) + assign("ask_factors_ortho" , "Orthogonalite des facteurs ?" , envir=.dico) + assign("ask_factors_superior_level" , "Nombre de facteurs du niveau superieur ?" , envir=.dico) + assign("ask_family" , "Veuillez preciser la famille (i.e. forme de la distribution)." , envir=.dico) + assign("ask_file_format" , "Format du fichier?" , envir=.dico) + assign("ask_file_format_to_import" , "Dans quel format est enregistre votre fichier ?" , envir=.dico) + assign("ask_first_categorical_set" , "Veuillez choisir le premier set de facteur(s) categoriel(s)" , envir=.dico) + assign("ask_first_variables_set" , "Veuillez choisir le premier jeu de variables" , envir=.dico) + assign("ask_fixed_covariables" , "Covariables fixees ?" , envir=.dico) + assign("ask_freq_constance" , "Constance de la frequence ?" , envir=.dico) + assign("ask_f_value" , "Quelle valeur du F voulez-vous utiliser ?" , envir=.dico) + assign("ask_group_variable" , "Variable [groupes] ?" , envir=.dico) + assign("ask_headers_in_database" , "Est-ce que le nom des variables est sur la premiere ligne de votre base de donnees ? Choisir TRUE si c'est le cas" , envir=.dico) + assign("ask_hierarchical_analysis" , "Faut-il realiser une analyse hierarchique ?" , envir=.dico) + assign("ask_how_many_modalities" , "Combien de modalites" , envir=.dico) + assign("ask_how_standard_error_must_be_estimated" , "Comment l'erreur standard doit-elle etre estimee ?" , envir=.dico) + assign("ask_how_to_remove" , "Comment voulez-vous les supprimer?" , envir=.dico) + assign("ask_how_to_treat_missing_values" , "Des valeurs manquantes ont ete detectees. Comment voulez-vous les traiter ? Garder l'ensemble des observations peut biaiser les resultats." , envir=.dico) + assign("ask_id_variable" , "Veuillez choisir la variable identifiant les participants" , envir=.dico) + assign("ask_imitate" , "Imiter ?" , envir=.dico) + assign("ask_independant_variable" , "Veuillez choisir la variable independante." , envir=.dico) + assign("ask_information_matrix" , "Matrice d'information ?" , envir=.dico) + assign("ask_integrate_factorial_scores_in_data" , "Voulez-vous que les scores factoriels soient integres a vos donnees ?" , envir=.dico) + assign("ask_inversed_items" , "items inverses?" , envir=.dico) + assign("ask_is_model_correct" , "Est-ce que votre modele est correct ?" , envir=.dico) + assign("ask_latent_variables_number" , "Veuillez preciser le nombre de variables latentes" , envir=.dico) + assign("ask_level" , "Veuillez choisir le niveau" , envir=.dico) + assign("ask_likelihood" , "Vraisemblance ?" , envir=.dico) + assign("ask_linebase_modalities" , "Veuillez specifier la/les modalite(s) qui serviront pour la ligne de base (e.g. 0). Les autres modalites seront regroupes dans la categorie 1." , envir=.dico) + assign("ask_log_base" , "Veuillez preciser la base du logarithme.Pour obtenir e, tapez e" , envir=.dico) + assign("ask_lower_bound" , "Limite inferieure?" , envir=.dico) + assign("ask_mcnemar_repeated_measure" , "Test de McNemar : les modalites ne sont pas les memes pour le test de McNemar. Est-ce bien un facteur en mesure repetee ?" , envir=.dico) + assign("ask_mediation_type" , "Quel type de mediation ?" , envir=.dico) + assign("ask_mediator" , "veuillez choisir le mediateur" , envir=.dico) + assign("ask_minus_left_hand_variables" , "Veuillez selectionner la -les- variable(s) a gauche du symbole *moins*" , envir=.dico) + assign("ask_minus_right_hand_variables" , "Veuillez selectionner la -les- variable(s) a droite du symbole *moins*." , envir=.dico) + assign("ask_minus_right_operand_variable_or_value" , "Les valeurs a droite du symbole *moins* sont-elles une/des variable(s) ou une valeur ? " , envir=.dico) + assign("ask_missing_values_detected_what_to_do" , "Des valeurs manquantes ont ete detectees. Comment voulez-vous les traiter ?" , envir=.dico) + assign("ask_missing_values_treatment" , "Traitement des valeurs manquantes ?" , envir=.dico) + assign("ask_missing_values_value_na_on_empty" , "Si certaines donnees sont manquantes, comment sont-elles definies ? Vous pouvez laisser NA si les cellules sont vides" , envir=.dico) + assign("ask_missing_value_treatment" , "Nombre de valeurs manquantes par variable. Comment voulez-vous les traiter ?" , envir=.dico) + assign("ask_modalities_for_variable" , "Quelles modalites voulez-vous selectionner pour la variable" , envir=.dico) + assign("ask_modalities_to_keep" , "Veuillez selectionner les modalites que vous desirez conserver." , envir=.dico) + assign("ask_name_for_dataset" , "Quel nom voulez-vous donner aux donnees ?" , envir=.dico) + assign("ask_name_to_attribute_to" , "Quel nom voulez-vous attribuer a" , envir=.dico) + assign("ask_nb_factors_repeated_measure" , "Combien de facteurs en mesure repetee ?" , envir=.dico) + assign("ask_new_variable_name" , "Quel nom voulez-vous attribuer a la nouvelle variable ? " , envir=.dico) + assign("ask_norm_value" , "Quelle est la valeur de la norme ?" , envir=.dico) + assign("ask_not_enough_obs_verify_dataset" , "Il n'y a pas assez d'observations pour realiser l'analyse. Veuillez verifier vos donnees net vous assurer qu'il y a au moins trois observations par modalite de chaque facteur" , envir=.dico) + assign("ask_null_hypothesis_tests_or_bayesian_factors" , "Voulez-vous les tests d'hypothees nuls ou/et les facteurs bayesiens ?" , envir=.dico) + assign("ask_numerator_variable_or_value" , "Le numerateur est-il une variable ou une valeur ? " , envir=.dico) + assign("ask_numerator_variable" , "Veuillez selectionner la variable au numerateur " , envir=.dico) + assign("ask_obs_to_remove" , "Quelle observation souhaitez-vous retirer des analyses ? 0=aucune" , envir=.dico) + assign("ask_other_options" , "Autres options?" , envir=.dico) + assign("ask_ponderate_analysis_by_a_sample_var" , "Faut-il ponderer l'analyse par une variable effectif ?" , envir=.dico) + assign("ask_positive_val_variable_or_value" , "Les valeurs positives sont-elles une/des variable(s) ou une valeur ? " , envir=.dico) + assign("ask_predictor" , "veuillez preciser le predicteur" , envir=.dico) + assign("ask_press_enter_to_continue" , "Appuyez [entree] pour continuer" , envir=.dico) + assign("ask_probabilities_for_modalities" , "Veuillez entrer les probabilites correspondant a chaque modalite de la variable." , envir=.dico) + assign("ask_probabilities" , "Probabilites ?" , envir=.dico) + assign("ask_probability_value" , "Quelle valeur de la probabilite voulez-vous utiliser ?" , envir=.dico) + assign("ask_redefine_analysis_because_modalities_product_is_superior_to_obs" , "Le produit des modalites des variables definissant les groupes est superieur au nombre de vos observations. Il faut au moins une observation par combinaison de modalites de vos variables. Veuillez redefinir votre analyse" , envir=.dico) + assign("ask_regroup_modalities" , "Voulez-vous faire des regroupements entre les modalites ?" , envir=.dico) + assign("ask_rename_variables_with_special_char" , "Certaines noms de variables contiennent des caracteres speciaux pouvant creer des bugs. Voulez-vous renommer ces variables ?" , envir=.dico) + assign("ask_results_desired" , "Quels resultats voulez-vous obtenir ?" , envir=.dico) + assign("ask_results_output" , "Sorties de resultats ?" , envir=.dico) + assign("ask_sampling_type" , "Quel type d'echantillonnage avez-vous realise pour votre analyse ?" , envir=.dico) + assign("ask_save_results_in_external_file" , "Desirez-vous sauvegarder les resultats dans un fichier externe ?" , envir=.dico) + assign("ask_second_categorical_set" , "Veuillez choisir le second set de facteur(s) categoriel(s)" , envir=.dico) + assign("ask_second_mediator" , "veuillez preciser le second mediateur." , envir=.dico) + assign("ask_second_variables_set" , "Veuillez choisir le second jeu de variables" , envir=.dico) + assign("ask_selection_method" , "Quel methode faut-il appliquer pour la methode de selection ?" , envir=.dico) + assign("ask_select_variables_or_modalities_of_repeated_measure_variable" , "Veuillez selectionner les variables OU les modalites de la (des) variables a mesure(s) repetee(s)." , envir=.dico) + assign("ask_separation_value" , "Veuillez preciser la valeur de separation" , envir=.dico) + assign("ask_shorten_long_variables_names" , "Certaines variables ont des noms particulierement longs pouvant gener la lecture. Voulez-vous les raccourcir?" , envir=.dico) + assign("ask_should_intercept_of_latent_variable_be_fixed_to_zero" , "Est-ce que l'intercept des variables latentes doit etre fixe a 0 ?" , envir=.dico) + assign("ask_should_intercept_of_obs_variables_be_fixed_to_zero" , "Faut-il fixer l'intercept des variables observees a 0 ?" , envir=.dico) + assign("ask_simple_or_partial_corr" , "Correlations simples ou partielles?" , envir=.dico) + assign("ask_specify_all_parameters_or_imitate_specific_software" , "Voulez-vous specifier tous les parametres [default] ou imiter un logiciel particulier ?" , envir=.dico) + assign("ask_specify_datasheet_to_import" , "Veuillez specifier la feuille de calcul que vous souhaitez importer" , envir=.dico) + assign("ask_specify_groups" , "Specifier groupes ?" , envir=.dico) + assign("ask_specify_inverted_item" , "Veuillez preciser les items inverses" , envir=.dico) + assign("ask_specify_likelihood" , "Veuillez preciser la vraisemblance." , envir=.dico) + assign("ask_specify_norm_value" , "Veuillez specifier la valeur de la norme" , envir=.dico) + assign("ask_specify_other_options" , "Specifier les autres options?" , envir=.dico) + assign("ask_specify_sample" , "Specifier effectifs ?" , envir=.dico) + assign("ask_specify_sample_variable" , "Specifier la vriable effectifs ?" , envir=.dico) + assign("ask_specify_variables_for_ranks" , "Veuillez preciser les variables dont vous souhaiter faire les rangs" , envir=.dico) + assign("ask_specify_variables_type" , "Veuillez preciser le(s) type(s) de variable(s) que vous souhaitez inclure dans l'analyse.nVous pouvez en choisir plusieurs (e.g., pour anova mixte ou des ancova" , envir=.dico) + assign("ask_standard_error" , "Erreur standard ?" , envir=.dico) + assign("ask_standardization" , "Standardisation ?" , envir=.dico) + assign("ask_standardization_vl" , "Standardisation VL?" , envir=.dico) + assign("ask_standardize_obs_variables_before" , "Faut-il standardise (i.e. centrer reduire) les variables observees au prelable (TRUE) ou non (FALSE) ?" , envir=.dico) + assign("ask_statistical_approach" , "Approche statistique ?" , envir=.dico) + assign("ask_subgroups" , "Vous pouvez decomposer les statistiques descriptives par sous-groupe en choisissant une ou plusieurs variables categorielles. Voulez-vous specifier les sous-groupes ?" , envir=.dico) + assign("ask_sufficient_matrix_for_afe" , "La matrice est-elle satisfaisante pour une AFE ?" , envir=.dico) + assign("ask_suppress_this_obs" , "Voulez-vous supprimer cette observation ?" , envir=.dico) + assign("ask_test_hierarchical_structure" , " Desirez-vous tester une structure hierarchique ? L'omega teste une structure hierarchique et une AFE hierarchique seront realisees." , envir=.dico) + assign("ask_time1" , "Veuillez choisir le temps 1." , envir=.dico) + assign("ask_time2" , "Veuillez choisir le temps 2." , envir=.dico) + assign("ask_transform_numerical_to_categorial_variables" , "Vous devez utiliser des variables categorielles. Voulez-vous transformer les variables numeriques en variables categorielles ?" , envir=.dico) + assign("ask_troncature_threshold" , "Veuillez fixer le seuil de la troncature" , envir=.dico) + assign("ask_t_test_type" , "Veuillez preciser le type de test t que vous souhaitez realiser." , envir=.dico) + assign("ask_type_correlation" , "Veuillez preciser le type de correlation que vous souhaitez realiser." , envir=.dico) + assign("ask_upper_bound" , "Limite superieure?" , envir=.dico) + assign("ask_value_for_missing_values" , "Par quelle valeur sont definies les valeurs manquantes ?" , envir=.dico) + assign("ask_value_for_operation" , "Veuillez specifier la valeur pour realiser votre operation mathematique." , envir=.dico) + assign("ask_value_for_selected_obs" , "Veuillez preciser la valeur sur laquelle les observations doivent etre selectionnees." , envir=.dico) + assign("ask_value" , "Precisez la valeur?" , envir=.dico) + assign("ask_variabels_for_polyc_tetra_mixt_corr" , "Veuillez choisir les variables dont il faut realiser les correlations polychorique/tetrachorique/mixte." , envir=.dico) + assign("ask_variable_at_this_point" , "Quelle variable a cette etape" , envir=.dico) + assign("ask_variable_name" , "Nom de la nouvelle variable ?" , envir=.dico) + assign("ask_variables_for_description_statistics" , "veuillez choisir les variables pour lesquelles vous desirez obtenir les statistiques descriptives" , envir=.dico) + assign("ask_variables_groups" , "Variable(s) groupes ?" , envir=.dico) + assign("ask_variables_names" , "Nom de variables?" , envir=.dico) + assign("ask_variables_to_abs" , "Veuillez selectionner les variables dont il faut faire la valeur absolue " , envir=.dico) + assign("ask_variables_to_add" , "Veuillez selectionner les variables a additionner." , envir=.dico) + assign("ask_variables_to_exp" , "Veuillez selectionner les variables auxquelles s'applique l'exposant " , envir=.dico) + assign("ask_variables_to_log" , "Veuillez selectionner les variables dont il faut faire le logarithme " , envir=.dico) + assign("ask_variables_to_mean" , "Veuillez selectionner les variables a moyenner " , envir=.dico) + assign("ask_variables_to_multiply" , "Veuillez selectionner les variables a multiplier. " , envir=.dico) + assign("ask_variables_to_order" , "Veuillez selectionner la (les) variable(s) a trier" , envir=.dico) + assign("ask_variables_type_correlations" , "Veuillez preciser le type de variables. Des correlations tetra/polychoriques seront realisees sur les variables dichotomiques/ordinales et Bravais-Pearson sur les variables continues" , envir=.dico) + assign("ask_variables_types_correlations" , "Veuillez preciser le type de variables. Des correlations tetra/polychoriques seront realisees sur les variables ordinales et Bravais-Pearson sur les variables continues" , envir=.dico) + assign("ask_variables_used_for_exponential" , "Veuillez selectionner les variables servant a l'exponentiel " , envir=.dico) + assign("ask_variables_used_for_groups" , "Veuillez choisir la ou les variables definissant les groupes" , envir=.dico) + assign("ask_variable" , "Variable a analyser ?" , envir=.dico) + assign("ask_wanted_model" , "Veuillez choisir le modele que vous desirez analyser avec aov.plus" , envir=.dico) + assign("ask_what_do_you_want" , "Que voulez-vous ?" , envir=.dico) + assign("ask_what_is_your_choice" , "Quel est votre choix ?" , envir=.dico) + assign("ask_what_to_print" , "Que voulez-vous afficher ?" , envir=.dico) + assign("ask_which_algorithm" , "Quel algorithme desirez-vous?" , envir=.dico) + assign("ask_which_analysis_you_looking_for" , "Quelle analyse recherchez vous ?" , envir=.dico) + assign("ask_which_baseline" , "Quelle est la ligne de base ?" , envir=.dico) + assign("ask_which_constant_parameters" , "Quels sont les parametres que vous desirez maintenir constants ?" , envir=.dico) + assign("ask_which_contrasts_for_variable" , "Quels contrastes pour la variable" , envir=.dico) + assign("ask_which_contrasts" , "Quel types de contraste voulez-vous ?" , envir=.dico) + assign("ask_which_correction" , "Quelle correction de la probabilite voulez-vous appliquer ? Pour ne pas appliquer de correction, choisir +none+" , envir=.dico) + assign("ask_which_data_to_analyse" , "Quelles donnees voulez-vous analyser?" , envir=.dico) + assign("ask_which_data_to_export" , "Quelles donnees voulez-vous exporter ?" , envir=.dico) + assign("ask_which_estimator" , "Quelles estimateur ?" , envir=.dico) + assign("ask_which_factors_combination_for_adjust_means" , "Pour quelle combinaison de facteurs desirez-vous afficher les moyennes ajustees ?" , envir=.dico) + assign("ask_which_information_matrix_for_standard_error_estimation" , "Sur quelle matrice d'information doit se realiser l'estimation des erreurs standards ?" , envir=.dico) + assign("ask_which_mathematical_operation" , "Veuillez choisir l'operation mathematique que vous desirez realiser " , envir=.dico) + assign("ask_which_operation" , "Quelle operation voulez-vous?" , envir=.dico) + assign("ask_which_options" , "Quelles options ?" , envir=.dico) + assign("ask_which_options_to_specify" , "Quelles options voulez-vous specifier ?" , envir=.dico) + assign("ask_which_output" , "Quel format souhaitez-vous ?" , envir=.dico) + assign("ask_which_output_results" , "Quelles sorties de resultats souhaitez-vous ?" , envir=.dico) + assign("ask_which_regression_type" , "Quel type de regression ?" , envir=.dico) + assign("ask_which_results_warning_on_default_output" , "Quels resultats souhaitez-vous ? Attention : les sorties par defaut ne peuvent etre sauvegrdees. Si vous voulez une sauvarde, choisissez le detail" , envir=.dico) + assign("ask_which_rotation" , "Quelle rotation" , envir=.dico) + assign("ask_which_saturation_criterion" , "Quel est le critere de saturation que vous voulez utiliser ?" , envir=.dico) + assign("ask_which_size_effect" , "Quelle taille d effet voulez-vous ?" , envir=.dico) + assign("ask_which_squared_sum" , "Quelle somme des carres voulez-vous utiliser ?" , envir=.dico) + assign("ask_which_test" , "Quel test voulez-vous utiliser ?" , envir=.dico) + assign("ask_which_value_for_operation" , "Quelle valeur voulez-vous pour votre operation mathematique ?" , envir=.dico) + assign("ask_which_variable_identifies_participants" , "Quelle est la variable identifiant les participants ?" , envir=.dico) + assign("ask_you_did_not_chose_a_variable_continue_or_abort" , "Vous n avez pas choisi de variable. Voulez-vous continuer (ok) ou abandonner (annuler) cette analyse ?" , envir=.dico) + assign("desc_abs_val_applied_to_var" , "la valeur absolue a ete applique a la variable" , envir=.dico) + assign("desc_accepted_values_are_z_and_grubbs" , "Les valeurs admises pour critere sont z et Grubbs " , envir=.dico) + assign("desc_all_tests_description" , "le modele parametrique renvoie l'anova classique,le non parametrique calcule le test de Kruskal Wallis nsi c'est un modele a groupes independants, ou une anova de Friedman pour un modele en Mesures repetees.nLe modele bayesien est l'equivalent du modele teste dans l'anova en adoptant une approche bayesienne,nles statistiques robustes sont des anovas sur des medianes ou les moyennes tronquees avec ou sans bootstrap." , envir=.dico) + assign("desc_alpha_increased_with_value_equals_to" , "vous multipliez l'erreur de 1e espece. Le risque de commettre une erreur de 1e espece est de" , envir=.dico) + assign("desc_analysis_aborted" , "L'analyse n'a pas pu aboutir" , envir=.dico) + assign("desc_and" , "et" , envir=.dico) + assign("desc_and_variabe" , "et la variable" , envir=.dico) + assign("desc_and_variable_y" , " et la variable " , envir=.dico) + assign("desc_applied_correction_is" , "la correction appliquee est la correction de" , envir=.dico) + assign("desc_at_least_10_obs_needed" , "Il faut au moins 10 observations plus le nombre de variables pour realiser l'analyse. Verifiez vos donnees." , envir=.dico) + assign("desc_at_least_independant_variables_or_repeated_measures" , "Il est indispensable d'avoir au minimum des variables a groupes independants ou en mesures repetees" , envir=.dico) + assign("desc_at_least_on_contrast_matrix_incorrect" , "Au moins une de vos matrices de contrastes n'est pas correcte." , envir=.dico) + assign("desc_at_least_one_denom_is_zero" , "Au moins une des valeurs au denominateur est un 0. La valeur renvoyee dans ce cas est infinie - inf" , envir=.dico) + assign("desc_at_least_one_non_numeric" , "au moins une variable n'est pas numerique" , envir=.dico) + assign("desc_at_least_one_var_is_not_num" , "au moins une des variables n'est pas numerique" , envir=.dico) + assign("desc_authorized_values_for_contrasts" , "Les valeurs autorisees pour les contrastes sont +none+ pour aucun contraste, +pairwise+ pour les comparaisons 2 a 2 ou une liste de coefficients de contrastes" , envir=.dico) + assign("desc_avoid_spaces_and_punctuations" , "Evitez les espaces ainsi que les signes de ponctuations, a l'exception . et _ " , envir=.dico) + assign("desc_bayesian_factors_could_not_be_computed" , "Les facteurs bayesiens n'ont pas pu etre calcules." , envir=.dico) + assign("desc_beyond_with_lower_and_upper" , "au-dela (avec une limite inferieure et superieure" , envir=.dico) + assign("desc_biased_results_risk_because_of_low_number_of_obs_or_zero_variance" , "il y a moins de 3 observations pour un des groupes ou \nla variance d'au moins un groupe vaut 0. Les resultats risquent d'etre considerablement biaises" , envir=.dico) + assign("desc_bootstraps_number_must_be_positive" , "Le nombre de bootstrap doit etre un nombre entier positif" , envir=.dico) + assign("desc_bootstrap_t_adapt_to_truncated_mean" , "Le bootstrap-t method est un bootstrap adapte au calcul de la moyenne tronquee" , envir=.dico) + assign("desc_cannot_compute_mahalanobis" , "Desole, nous ne pouvons pas calculer la distance de Mahalanobis sur vos donnees. Les analyses seront resalisees sur les donnees completes" , envir=.dico) + assign("desc_cannot_group_variables_because_not_described" , "Vous ne pouvez pas avoir de variable *groupes* etant donne que toutes les variables doivent etre decrites" , envir=.dico) + assign("desc_cannot_have_both_within_RML_arguments" , "Vous ne pouvez pas avoir a la fois des arguments dans within et RML" , envir=.dico) + assign("desc_cells_for_mcnemar" , "Les cellules utilisees pour le calcul du McNemar sont celles de la 1e ligne 2e colonne et de la 2e ligne 1e colonne" , envir=.dico) + assign("desc_centered_data_schielzeth_recommandations" , "En accord avec les recommandations de Schielzeth 2010, les donnees ont ete prealablement centrees" , envir=.dico) + assign("desc_chi_squared_adjustment_on_variable_x" , "chi deux d'ajustement sur la variable" , envir=.dico) + assign("desc_close_browser_to_come_back" , "Ne pas oublier de fermer la fenetre htmlt (firexfox, chrome, internet explorer...) pour revenir Ă  la session R" , envir=.dico) + assign("desc_cross_validation_is_not_yet_supported" , "La validation croisee n'est pas encore disponible." , envir=.dico) + assign("desc_data_saved_in" , "les donnees sont sauvegardees dans" , envir=.dico) + assign("desc_data_succesfully_ordered" , "les donnees ont ete triees correctement " , envir=.dico) + assign("desc_descriptive_statistics_on" , "Statistiques descriptives sur" , envir=.dico) + assign("desc_distribution_is_hypergeometric_when" , "L'option *Effectif total fixe pour les lignes et les colonnes* lorsque les totaux pour les lignes et les colonnes sont fixes.La distribution est hypergeometrique" , envir=.dico) + assign("desc_each_participant_must_appear_only_once_" , "Chaque participant doit apparaĂ®tre une et une seule fois pour chaque combinaison des modalites" , envir=.dico) + assign("desc_effect_size_by_walker" , "La taille d'effet est calculee a partir de la formule proposee par Walker, 2003" , envir=.dico) + assign("desc_entered_value_not_num" , "la valeur entree n'est pas numerique" , envir=.dico) + assign("desc_exponential_has_been_applied_to_var" , "l'exponentiel a ete applique a la variable" , envir=.dico) + assign("desc_facotrs_must_be_positive_int_inferior_to_variables_num" , "Le nombre de facteur doit etre un entier positif inferieur au nombre de variables" , envir=.dico) + assign("desc_fb_ratio_between_models" , "Rapport des FB entre les modeles" , envir=.dico) + assign("desc_file_is_saved_in" , "le fichier est sauvegarde dans" , envir=.dico) + assign("desc_flattening_and_asymetry_configurable" , "Vous pouvez specifier la troncature et les parametres pour l'aplatissement et l'asymetrie en choisissant autres options" , envir=.dico) + assign("desc_for_bigger_samples_bootstrap_t_prefered" , "Pour des echantillons plus importants, les boostrap utilisant la methode t doit etre preferee." , envir=.dico) + assign("desc_for_easier_to_work" , "Pour que easieR fonctionne correctement, il faut installer Pandoc disponible Ă  l'url suivant : https://github.com/jgm/pandoc/releases" , envir=.dico) + assign("desc_graph_thickness_gives_density" , "L'epaisseur du graphique donne la densite, permettant de mieux cerner la distribution." , envir=.dico) + assign("desc_has_been_added_to" , "a ete ajoutee a" , envir=.dico) + assign("desc_has_been_added_to_variable" , "a ete ajoutee a la variable" , envir=.dico) + assign("desc_has_been_applied_to_variable" , " a ete applique a la variable" , envir=.dico) + assign("desc_has_been_put_to_the_power_of" , " a ete elevee a la puissance" , envir=.dico) + assign("desc_has_multiplied_variables" , "a multiplie la -les- variable-s" , envir=.dico) + assign("desc_highest_value" , "Valeur la plus elevee" , envir=.dico) + assign("desc_how_to_cite_easier" , "Pour citer easieR dans vos publication / to cite easieR in you publications use :\n Stefaniak, N. (2020). " , envir=.dico) + assign("desc_identical_option_total_sample" , "L'option Effectif total fixe pour les colonnes* est identique a la precedente pour les colonnes" , envir=.dico) + assign("desc_identified_outliers" , "Observations considerees comme influentes" , envir=.dico) + assign("desc_if_true_covariates_as_fixed" , "Si vrai, on considere les covaries exogenes comme fixes, sinon on les considere comme aleatoires et leurs parametres sont libres" , envir=.dico) + assign("desc_if_true_latent_residuals_one" , "Si vrai, les residus des variables latentes sont fixes a 1, sinon les parametres de la variable latente sont estimes en fixant le premier indicateur a 1" , envir=.dico) + assign("desc_improve_likelihood_for_each_variable" , "Amelioration de la vraisemblance pour chaque variable" , envir=.dico) + assign("desc_incorrect_model" , "Le modele specifie est incorrect. Verifiez vos variables et votre modele" , envir=.dico) + assign("desc_instable_model_high_multicolinearity" , "La multicolinearite est trop importante. Le modele est instable" , envir=.dico) + assign("desc_insufficient_obs" , "Le nombre d'observations est insuffisant pour mener a bien les analyses pour ce groupe" , envir=.dico) + assign("desc_insufficient_sample_for_combinations_between" , "Les effectifs sont insuffisants pour le nombre de combinaisons entre la variable " , envir=.dico) + assign("desc_in_that_case_non_parametric_is_classical_chi_squared" , "Dans ce cas, le test non parametrique est le test de chi carre classique" , envir=.dico) + assign("desc_issue_in_hierarchical_regression" , "Un probleme a ete identifie dans les etapes de votre regression hierarchique" , envir=.dico) + assign("desc_kmo_could_not_be_computed_verify_matrix" , "Le KMO n'a pas pu etre calcule. Verifiez votre matrice de correlation." , envir=.dico) + assign("desc_kmo_must_strictly_be_more_than_a_half" , "le KMO doit absolument etre superieur a 0.5" , envir=.dico) + assign("desc_kmo_on_matrix_could_not_be_obtained" , "Le KMO sur la matrice n'a pu etre obtenu." , envir=.dico) + assign("desc_kmo_on_matrix_could_not_be_obtained_trying" , "Le KMO sur la matrice n'a pu etre obtenu. Nous tentons de realiser un lissage de la matrice de correlation" , envir=.dico) + assign("desc_large_format_must_be_numeric_or_integer" , "Si vos donnees sont en format large, les mesures doivent toutes etre numeriques ou des entiers (integer)" , envir=.dico) + assign("desc_list_of_objects_still_in_mem" , "Liste des objects encore en memoire de R" , envir=.dico) + assign("desc_log_with_base" , "le logarithme de base" , envir=.dico) + assign("desc_manifest_variables_of" , "Variables manifestes de" , envir=.dico) + assign("desc_manual_contrast_need_coeff_matrice" , "Si vous entrez des contrastes manuellement, toutes les variables de l'analyse doivent avoir leur matrice de coefficients" , envir=.dico) + assign("desc_matrix_is_singular_mardia_cannot_be_performed" , "La matrice est singuliere et le test de Mardia ne peut etre realise. Seules les analyses univariees peuvent etre realisees" , envir=.dico) + assign("desc_mcnemar_need_2x2_table_yours_are_different" , "Le test de McNemar implique un tableau 2x2. Les dimensions de votre tableau sont differentes." , envir=.dico) + assign("desc_modalities_product_must_correspond_to_cols_selected" , "le produit des modalites de chacune des variables doit correspondre au nombre de colonnes selectionnees." , envir=.dico) + assign("desc_model_contains_error" , "Le modele ne peut etre evalue. Il doit contenir une erreur" , envir=.dico) + assign("desc_model_could_not_converge" , "Le modele n'a pas pu converger. Les parametres ont ete adaptes pour permettre au modele de converger" , envir=.dico) + assign("desc_model_seems_incorrect_could_not_be_created" , "Le modele semble incorrect et n'a pas pu etre cree." , envir=.dico) + assign("desc_most_common_effect_size" , "la taille d'effet la plus frequente est le eta carre partiel - pes.\nLa taille d'effet la plus precise est le eta carre generalise - ges" , envir=.dico) + assign("desc_multicolinearity_risk" , "risque de multicolinearite si le determinant de la matrice est inferieur a 0.00001" , envir=.dico) + assign("desc_multiple_ways_to_compute_squares_sum" , "Il existe plusieurs maniere de calculer la somme des carres. Le choix par defaut des logiciels commerciaux est une somme des carres\nde type 3, mettant la priorite sur les interactions plutot que sur les effets principaux." , envir=.dico) + assign("desc_must_be_dichotomic" , "modalites. Elle est incompatible avec une regression logistique. Elle doit etre dichotomique" , envir=.dico) + assign("desc_nb_factors_must_be_positive_integer" , "Le nombre de facteur doit etre un entier positif inferieur au nombre de facteurs" , envir=.dico) + assign("desc_need_at_least_three_observation_by_combination" , "Certaines combinaisons des modalites ont moins de 3 observations. Vous devez avoir au moins 3 observations pour chaque combinaison" , envir=.dico) + assign("desc_neg_log_impossible" , "il n'est pas possible de calculer des logarithmes pour une base est negative. NA est renvoye" , envir=.dico) + assign("desc_no_analysis_can_be_performed_given_your_data" , "Les variables que vous avez choisies pour realiser votre analyse ne permettent de faire aucune analyse. Veuillez redefinir votre analyse" , envir=.dico) + assign("desc_no_data_in_R_memory" , "il n'y a pas de donnees dans la memoire de R, veuillez importer les donnnees sur lesquelles realiser l'analyse" , envir=.dico) + assign("desc_non_equal_independant_variable_modalities_occurrence" , "Le nombre d'occurrence pour chaque modalite de votre variable independante n'est pas identique. Veuillez choisir un identifiant participant" , envir=.dico) + assign("desc_non_numeric_value" , "La valeur entree n'est pas numerique, vous devez entrer une valeur numerique" , envir=.dico) + assign("desc_non_numeric_variable" , "la variable n est pas numerique" , envir=.dico) + assign("desc_non_param_are_rho_and_tau" , "Le test non parametrique correspond au rho de Spearman et au tau de Kendall" , envir=.dico) + assign("desc_non_param_is_wilcoxon_or_mann_withney" , "Le test non parametrique est le test de Wilcoxon (ou Mann-Whitney)" , envir=.dico) + assign("desc_no_obs_for_combination" , "pas d'observations pour la combinaison" , envir=.dico) + assign("desc_no_result_saved" , "aucun resultat n'a ete sauvegarde" , envir=.dico) + assign("desc_norm_must_be_numeric" , "La norme doit etre une valeur numerique." , envir=.dico) + assign("desc_no_saved_analysis_found" , "Aucune analyse sauvegardee n'a pu etre trouvee" , envir=.dico) + assign("desc_number_of_judge_is" , "le nombre de juge =" , envir=.dico) + assign("desc_number_of_missing_values" , "Nombre de valeurs manquantes par variable" , envir=.dico) + assign("desc_number_of_observations_is" , "le nombre d'observations =" , envir=.dico) + assign("desc_number_outliers_removed" , "Nombre d'observations retirees" , envir=.dico) + assign("desc_obs_with_asterisk_are_outliers" , "Les observations marquees d'un asterisque sont considerees comme influentes au moins sur un critere" , envir=.dico) + assign("desc_odd_ratio_cannot_be_computed" , "On ne peut pas calculer les OR pour des tableaux plus grands que 2x3 ou des tableaux contenant des 0" , envir=.dico) + assign("desc_only_one_dependant_variable_alllowed" , "Il ne peut y avoir qu'une seule variable dependante." , envir=.dico) + assign("desc_only_one_file_format_at_time_EPS_JPG" , "Only one file format for saving figure may be used at a time (you have both EPS and JPG specified)." , envir=.dico) + assign("desc_only_one_file_format_at_time_EPS_PDF" , "Only one file format for saving figure may be used at a time (you have both PDF and EPS specified)." , envir=.dico) + assign("desc_only_one_file_format_at_time_PDF_JPG" , "Only one file format for saving figure may be used at a time (you have both PDF and JPG specified)." , envir=.dico) + assign("desc_only_values_above_diagonal_are_adjusted_for_multiple_comp" , "Seules les valeurs au-dessus de la diagonales sont ajustees pour comparaisons multiples" , envir=.dico) + assign("desc_operation_succesful" , "L'operation mathematique s'est deroulee correctement." , envir=.dico) + assign("desc_order" , "de tri" , envir=.dico) + assign("desc_outliers_identified_on_4_div_n" , "les valeurs influentes sont identifiees sur la base de 4/n" , envir=.dico) + assign("desc_outliers_identified_on_mahalanobis" , "les valeurs influentes sont identifiees sur la base de la distance de Mahalanobis avec un seuil du chi a 0.001" , envir=.dico) + assign("desc_outliers_on_4_div_n" , "les valeurs influentes sont identifiees sur la base de 4/n" , envir=.dico) + assign("desc_packages_used_for_this_function" , "Packages utilises pour cette fonction" , envir=.dico) + assign("desc_param_is_BP" , "Le test parametrique est la correlation de Bravais-Pearson" , envir=.dico) + assign("desc_param_is_t_test" , "Le test parametrique est le test t classique" , envir=.dico) + assign("desc_param_test_is_classical_reg_robusts_are_m_estimator" , "Le test parametrique est la regression classique et les tests robustes sont une estimation sur un M estimeur ainsi qu'un bootstrap." , envir=.dico) + assign("desc_percentile_bootstrap_prefered_for_small_samples" , "la methode du percentile bootstrap doit etre preferee pour les petits echantillons" , envir=.dico) + assign("desc_perfectly_correlated_variables_in_matrix_trying_to_solve" , "vous tenter de faire une matrice de correlations avec des variables parfaitement correlees. Cela pose souci pour le calcul de la distance de Mahalanobis. Nous tentons de resoudre le souci" , envir=.dico) + assign("desc_polyc_correlations_failed_rho_used_instead" , "Les correlations polychoriques ont echoue. Les correlations utilisees sont des rho de Spearman" , envir=.dico) + assign("desc_proba_and_IC_estimated_on_bootstrap" , "Les probabilites et les IC sont estimes sur la base d'un bootsrap. L'IC est corrige pour comparaison multiple, contrairement a la probabilite reportee." , envir=.dico) + assign("desc_probabilities_vector_please_no_fraction" , "Vecteur des probabilites. Attention : ne pas entrer des fractions" , envir=.dico) + assign("desc_red_dot_is_mean_error_is_sd" , "Le point rouge est la moyenne. La barre d'erreur est l'ecart-type" , envir=.dico) + assign("desc_references" , "References des packages utilises pour cette analyse" , envir=.dico) + assign("desc_removed_variable" , "variable supprimee" , envir=.dico) + assign("desc_removing_outliers_weakens_sample_size" , "La suppression des valeurs influentes entraĂ®ne un effectif trop faible sur certaines modalites pour mener a bien l'analyse" , envir=.dico) + assign("desc_result_succesfully_imported_in" , "Les resultats ont ete correctement importes dans" , envir=.dico) + assign("desc_robusts_statistics_could_not_be_computed" , "Les statistiques robustes n'ont pas pu etre realisees" , envir=.dico) + assign("desc_robust_statistics_are_alternative_to_the_principal_but_slower" , "Les statistiques robustes sont des analyses alternatives a l'analyse principale, impliquant le plus souvent des bootstraps. Ces analyses sont souvent plus lentes" , envir=.dico) + assign("desc_saturation_criterion_must_be_between_zero_and_one" , "Le critere de saturation doit etre compris entre 0 et 1." , envir=.dico) + assign("desc_search_here" , "Tapez votre recherche ici" , envir=.dico) + assign("desc_selected_obs_are_in" , "les observations que vous avez selectionnees sont dans" , envir=.dico) + assign("desc_selection_for_bayesian_factor_does_not_apply_to_complex_models" , "Les methodes de selection pour les facteurs bayesiens ne s'appliquent pas pour des modeles complexes." , envir=.dico) + assign("desc_should_specify_nb_factors_repeated_measure" , "vous devez specifier le nombre de facteurs en mesure repetee" , envir=.dico) + assign("desc_single_dependant_variable_allowed_in_paired_t" , "Il ne peut y avoir qu'une seule variable dependante pour les t de student pour echantillons apparies" , envir=.dico) + assign("desc_singular_matrix_mahalanobis_on_max_info" , "Votre matrice est singuliere, ce qui pose souci. Nous tentons de de resoudre le souci. Si possible, la distance de Mahalanobis sera alors calculee sur le maximum d'information tout en evitant la singularite." , envir=.dico) + assign("desc_some_values_are_not_numeric" , "Toutes les valeurs entrees ne sont pas numerique. Veuillez entrer des valeurs numeriques uniquement" , envir=.dico) + assign("desc_special_characters_have_been_removed" , "Les accents / caracteres speciaux ont volontairement ete supprimes pour assurer la portabilite de easieR sur tous les ordinateurs." , envir=.dico) + assign("desc_specify_f_value" , "Vous devez specifier la valeur du F. Cette valeur doit etre superieure a 1" , envir=.dico) + assign("desc_specify_lower_bound" , "vous devez preciser la limite inferieure" , envir=.dico) + assign("desc_specify_probability_value" , "Vous devez specifier la valeur de la probabilite. Cette valeur doit etre entre 0 et 1" , envir=.dico) + assign("desc_specify_upper_bound" , "vous devez preciser la limite superieure" , envir=.dico) + assign("desc_standardized_saturation_on_correlation_matrix" , "saturations standardisees basees sur la matrice de correlations" , envir=.dico) + assign("desc_succesfully_imported" , "les donnees ont ete importees correctement" , envir=.dico) + assign("desc_succesful_operation" , "L'operation a ete realisee correctement" , envir=.dico) + assign("desc_tested_model_is" , "le modele teste est" , envir=.dico) + assign("desc_there_is_no_rotation" , "il n'y a pas de rotation" , envir=.dico) + assign("desc_the_variable_lower" , "la variable" , envir=.dico) + assign("desc_the_variable_upper" , "La variable" , envir=.dico) + assign("desc_this_analysis_will_not_be_performed" , ". Cette analyse ne sera pas realisee." , envir=.dico) + assign("desc_this_index_is_prefered_for_most_cases" , " Cet indice est adapte dans la plupart des situations. Le M-estimator modifie doit etre prefere pour N<20" , envir=.dico) + assign("desc_this_is_large_format" , "ceci est le format large" , envir=.dico) + assign("desc_this_is_long_format" , "ceci est le format long" , envir=.dico) + assign("desc_times_less" , "fois moins" , envir=.dico) + assign("desc_times_more" , "fois plus" , envir=.dico) + assign("desc_to_display_results_use_summary" , "Pour afficher les resultats, veuillez utiliser summary(modele.cfa)" , envir=.dico) + assign("desc_total_observations" , "nombre total d'observations" , envir=.dico) + assign("desc_truncature_on_m_estimator_adapts_to_sample" , "La troncature sur le M-estimator s'adapte en fonction des caracteristiques de l'echantillon." , envir=.dico) + assign("desc_two_cols_are_needed" , "Pour un facteur en mesures repetees en format large, il faut au moins deux colonnes" , envir=.dico) + assign("desc_two_modalities_for_independante_categorial_variable" , "Vous devez utiliser une variable independante categorielle a 2 modalites" , envir=.dico) + assign("desc_unauthorized_char_replaced" , "Des caracteres non autorises ont ete utilises pour le nom. Ces caracteres ont ete remplaces par des points" , envir=.dico) + assign("desc_unavailable_distal_mediations" , "Les mediations distales ne sont pas disponibles pour le moment / Distal mediations are not available for now" , envir=.dico) + assign("desc_user_exited_aov_plus" , "vous avez quitte aov.plus" , envir=.dico) + assign("desc_value_must_be_between_zero_and_one" , "La valeur doit etre comprise entre 0 et 1" , envir=.dico) + assign("desc_value_must_be_numeric" , "La valeur doit etre numerique et comprise entre le minimum et le maximum de la variable dependante." , envir=.dico) + assign("desc_variable_added" , "Variable ajoutee" , envir=.dico) + assign("desc_variable_must_be_numeric_and_of_non_null_variance" , "la variable doit etre numerique et avoir une variance non nulle." , envir=.dico) + assign("desc_variable_must_be_positive_int" , "la variable doit etre un entier *integer* positif" , envir=.dico) + assign("desc_variables_are_in" , "les variables selectionnees sont dans" , envir=.dico) + assign("desc_we_could_not_compute_anova_on_medians" , "Desole, nous n'avons pas pu calcule l'anova sur les medianes, possiblement en raison d'un nombre important d'ex aequo." , envir=.dico) + assign("desc_we_could_not_compute_robust_anova" , "Desole, nous n'avons pas pu calcule l'anova robuste." , envir=.dico) + assign("desc_working_dir_is_now" , "Le repertoire de travail est a present" , envir=.dico) + assign("desc_you_can_chose_predefined_or_manual_contrasts" , "Vous pouvez choisir les contrastes predefinis ou les specifier manuellement. Dans ce dernier cas, veuillez choisir specifier les contrastes" , envir=.dico) + assign("desc_you_can_still_add" , "Vous pouvez encore ajouter une valeur specifique au total. Laissez 0 si vous ne souhaitez rien ajouter" , envir=.dico) + assign("desc_you_can_still_multiply" , "Vous pouvez encore multiplier le total par une valeur specifique. Laissez 1 si vous ne souhaitez plus multiplier par une nouvelle valeur" , envir=.dico) + assign("desc_you_did_this_operation" , "vous avez realise l'operation suivante :" , envir=.dico) + assign("desc_you_exited_afe" , "vous avez quitte l'AFE" , envir=.dico) + assign("desc_you_have_selected" , "vous avez selectionne" , envir=.dico) + assign("desc_you_must_give_obs_number" , "Vous devez entrer le numero de l'observation" , envir=.dico) + assign("desc_your_dependant_variable_has" , "Votre veriable dependante a" , envir=.dico) + assign("desc_z_must_be_a_number" , "z doit etre un nombre" , envir=.dico) + assign("desc_author" , "author: 'Genere automatiquement par easieR'" , envir=.dico) + assign("desc_title" , "title: 'Resultats de vos analyses'" , envir=.dico) + assign("txt_absolute_value" , "valeur absolue" , envir=.dico) + assign("txt_added_variables_graph" , "Graphe des variables ajoutees" , envir=.dico) + assign("txt_additions" , "additions" , envir=.dico) + assign("txt_additive_effects" , "Effets additifs" , envir=.dico) + assign("txt_additive_model_variables" , "Variables modele additif" , envir=.dico) + assign("txt_add_of_cols" , "addition de colonnes" , envir=.dico) + assign("txt_add_of_specific_value" , "addition d'une valeur specifique" , envir=.dico) + assign("txt_adequation_adjustement_indexes" , "Indices d'adequation et d'ajustement" , envir=.dico) + assign("txt_adequation_measurement_of_matrix" , "Mesure d'adequation de la matrice" , envir=.dico) + assign("txt_adequation_measures" , "Mesures d'adequation" , envir=.dico) + assign("txt_adequation_outside_diagonal" , "Adequation basee sur les valeurs en dehors de la diagonale" , envir=.dico) + assign("txt_adjusted_data_loftus_masson" , "Donnees ajustees (Loftus & Masson, 1994)" , envir=.dico) + assign("txt_adjusted_means_graph" , "Moyennes ajustee-Graphique" , envir=.dico) + assign("txt_adjusted_means" , "Moyennes ajustee" , envir=.dico) + assign("txt_adjustement_measure" , "Mesure d'ajustement" , envir=.dico) + assign("txt_adjusted_p_dot_value" , "Valeur P corrigĂ©e" , envir=.dico) + assign("txt_agreement" , "Accord" , envir=.dico) + assign("txt_aic_criterion" , "AIC - Akaike Information criterion" , envir=.dico) + assign("txt_alpha_warning" , "Avertissement alpha" , envir=.dico) + assign("txt_alternative" , "alternative" , envir=.dico) + assign("txt_analysis_factor_component" , "analyses de facteurs et de composantes" , envir=.dico) + assign("txt_analysis_on" , "analyse sur" , envir=.dico) + assign("txt_analysis_on_truncated_means" , "Analyse sur les moyennes tronquees" , envir=.dico) + assign("txt_analysis_on_variable" , "Analyse sur la variable" , envir=.dico) + assign("txt_analysis_premature_abortion" , "Arret premature de l'analyse" , envir=.dico) + assign("txt_ancova_application_conditions" , "Conditions d'application de l'ancova" , envir=.dico) + assign("txt_and_the_number_of_obs" , "et le nombre d'observations =" , envir=.dico) + assign("txt_and_YZ" , "et YZ =" , envir=.dico) + assign("txt_anova_ancova" , "analyse de variance et covariance" , envir=.dico) + assign("txt_anova" , "Anova" , envir=.dico) + assign("txt_anova_on" , "anova sur" , envir=.dico) + assign("txt_anova_on_modified_huber_estimator" , "Anova sur l'estimateur modifie de localisation de Huber" , envir=.dico) + assign("txt_anova_on_truncated_means" , "Anova basee sur les moyennes tronquees" , envir=.dico) + assign("txt_anova_with_welch_correction" , "Anova avec correction de Welch pour variances heterogenes" , envir=.dico) + assign("txt_apparied_correlations" , "Correlations appariees" , envir=.dico) + assign("txt_apriori" , "a priori" , envir=.dico) + assign("txt_autocorrelation" , "Autocorrelation" , envir=.dico) + assign("txt_backward" , "Backward" , envir=.dico) + assign("txt_backward_step_descending" , "Backward- pas-a-pas descendant" , envir=.dico) + assign("txt_barlett_test" , "Test de Barlett" , envir=.dico) + assign("txt_bayes_factor_10" , "Bayes Factor (10)" , envir=.dico) + assign("txt_bayes_factor" , "BayesFactor" , envir=.dico) + assign("txt_bayesian_approach_hierarchical_models" , "Approche bayesienne des modeles hierarchique" , envir=.dico) + assign("txt_bayesian_factor_by_group" , "Facteur bayesien par groupe" , envir=.dico) + assign("txt_bayesian_factor" , "Facteur bayesien" , envir=.dico) + assign("txt_bayesian_factor_of_model" , "FB du modele" , envir=.dico) + assign("txt_bayesian_factors_10" , "Facteur bayesiens 10" , envir=.dico) + assign("txt_bayesian_factors_compute_null_with_bayesian_approach" , "Facteurs bayesiens : calcule l'equivalent du test d'hypothese nulle en adoptant une approche bayesienne." , envir=.dico) + assign("txt_bayesian_factors_for_BP" , "Facteurs Bayesiens pour la correlation de Bravais-Pearson" , envir=.dico) + assign("txt_bayesian_factors_for_spearman" , "Facteurs Bayesiens pour la correlation de Spearman" , envir=.dico) + assign("txt_bayesian_factors_sequential" , "Facteurs bayesiens sequentiels" , envir=.dico) + assign("txt_bca_bootstrap_on_m_estimator" , "Bootstrap de type BCa sur le M-estimator" , envir=.dico) + assign("txt_beta_table" , "table des betas" , envir=.dico) + assign("txt_between" , "entre" , envir=.dico) + assign("txt_bidirectionnal" , "Bidirectionnel" , envir=.dico) + assign("txt_b_m_estimator" , "b (M estimator)" , envir=.dico) + assign("txt_bootstrap_on_BP" , "Bootstrap sur la correlation de Bravais Pearson" , envir=.dico) + assign("txt_bootstrap_t_method" , "bootstrap-t method" , envir=.dico) + assign("txt_bootstrap_t_method_on_truncated_means" , "Bootstrap utilisant la methode t sur les moyennes tronquees" , envir=.dico) + assign("txt_BP_correlation_by_group" , "Correlation de Bravais-Pearson par groupe" , envir=.dico) + assign("txt_breusch_pagan_test" , "Verification de la non-constance de la variance d'erreur (test de Breusch-Pagan)" , envir=.dico) + assign("txt_cancel" , "annuler" , envir=.dico) + assign("txt_cauchy_prior_width" , "Cauchy Prior Width (r)" , envir=.dico) + assign("txt_center_or_center_reduce" , "Centrer / centrer reduire" , envir=.dico) + assign("txt_center_reduce" , "centrer reduire" , envir=.dico) + assign("txt_ceres_graph_linearity" , "Graphique de Ceres testant la linearite" , envir=.dico) + assign("txt_chi_adjustement" , "Ajustement" , envir=.dico) + assign("txt_chi_independance" , "Independance" , envir=.dico) + assign("txt_chi_results_between_var_x" , "Resultats du chi.deux entre la variable" , envir=.dico) + assign("txt_chi_squared" , "chi deux" , envir=.dico) + assign("txt_chi_squared_empirical" , "chi carre empirique" , envir=.dico) + assign("txt_chi_squared_likelihood_max" , "chi carre du maximum de vraisemblance" , envir=.dico) + assign("txt_chi_squared_null_model" , "chi carre du modele null" , envir=.dico) + assign("txt_chi_squared_type" , "Type de khi deux" , envir=.dico) + assign("txt_coeff_table" , "Table des coefficients" , envir=.dico) + assign("txt_col_correspoding_to_variable" , "Colonnes correspondant Ă  la variable" , envir=.dico) + assign("txt_col_mean" , "moyenne de colonnes" , envir=.dico) + assign("txt_cols" , "colonnes" , envir=.dico) + assign("txt_col_separator" , "Separateur de colonnes" , envir=.dico) + assign("txt_cols_in_repeated_measure" , "Colonnes en mesures repetees" , envir=.dico) + assign("txt_cols_multiplication" , "multiplication de colonnes" , envir=.dico) + assign("txt_comma" , "virgule" , envir=.dico) + assign("txt_compare_to_baseline" , "comparaison a une ligne de base" , envir=.dico) + assign("txt_compare_two_correlations" , "Comparaison de deux correlations" , envir=.dico) + assign("txt_comparison_of_two_correlations" , "comparaison des deux correlations" , envir=.dico) + assign("txt_comparison_on_truncated_means" , "Comparaison basee sur les moyennes tronquees" , envir=.dico) + assign("txt_comparisons_XY" , "comparaison des correlations XY=" , envir=.dico) + assign("txt_comparison_to_norm" , "Comparaison a une norme" , envir=.dico) + assign("txt_comparison_two_by_two" , "Comparaison 2 a 2" , envir=.dico) + assign("txt_compile_report" , "generer un rapport" , envir=.dico) + assign("txt_complementary_results" , "Resultats complementaires (e.g. contrastes d'interaction et moyennes ajustees)" , envir=.dico) + assign("txt_complete_dataset" , "Donnees completes" , envir=.dico) + assign("txt_complete_model" , "Modele complet" , envir=.dico) + assign("txt_complexity" , "complexite" , envir=.dico) + assign("txt_complex_model" , "modele complexe" , envir=.dico) + assign("txt_confidance_threshold" , "Seuil de confiance (1- alpha)" , envir=.dico) + assign("txt_confidence_interval_estimated_by_bootstrap" , "Intervalle de confiance estime par bootstrap" , envir=.dico) + assign("txt_confidence_interval" , "Intervalle de confiance" , envir=.dico) + assign("txt_confidence_interval_inferior_limit" , "Lim.inf" , envir=.dico) + assign("txt_confidence_interval_superior_limit" , " Lim.sup" , envir=.dico) + assign("txt_confidence_interval_of_saturations_on_bootstrap" , "Intervalle de confiance des saturations sur la base du bootstrap - peut etre biaise en presence de Heyhood case" , envir=.dico) + assign("txt_confidence_interval_on_bootstrap" , "Intervalle de confiance base sur le bootstrap" , envir=.dico) + assign("txt_confidence_interval_on_standard_error" , "Intervalle de confiance base sur l'erreur standard de l'alpha" , envir=.dico) + assign("txt_confirmatory_factorial_analysis" , "Analyse factorielle confirmatoire" , envir=.dico) + assign("txt_contrast" , "contraste" , envir=.dico) + assign("txt_contrasts" , "contrastes" , envir=.dico) + assign("txt_contrasts_for" , "Contrastes pour" , envir=.dico) + assign("txt_contrasts_table_imitating_commercial_softwares" , "Table des contrastes imitant les logiciels commerciaux" , envir=.dico) + assign("txt_contrasts_table" , "Table des contrastes" , envir=.dico) + assign("txt_control_variables" , "Variable-s a controler" , envir=.dico) + assign("txt_correction_for_polyc_corr_must_be_between_zero_and_one" , "La correction pour le calcul de correlations polycoriques doit etre comprise entre 0 et 1." , envir=.dico) + assign("txt_correlation_between_scores_and_factors" , "Correlations des scores avec les facteurs" , envir=.dico) + assign("txt_correlation_between_var_x" , "Correlation entre la variable" , envir=.dico) + assign("txt_correlation_is" , "correlation de" , envir=.dico) + assign("txt_correlation_matrix_determinant" , "Determinant de la matrice de correlation" , envir=.dico) + assign("txt_correlation_matrix_determinant_information" , "Determinant de la matrice de correlations : information" , envir=.dico) + assign("txt_correlations_between_factors" , "correlations entre facteurs" , envir=.dico) + assign("txt_correlations_comparison" , "comparaison de correlations" , envir=.dico) + assign("txt_correlations_matrix_afe" , "Matrice de correlation utilisee pour AFE" , envir=.dico) + assign("txt_covariance_matrix_adjusted" , "Matrice de covariance ajustee" , envir=.dico) + assign("txt_covariance_matrix_estimated" , "Matrice de covariance estimee" , envir=.dico) + assign("txt_cox_snell_r_2" , "Cox and Snell R^2" , envir=.dico) + assign("txt_cronbach_alpha" , "Alpha de Cronbach" , envir=.dico) + assign("txt_cronbach_alpha_on_whole_scale" , "Alpha de Cronbach sur la totalite de l'echelle" , envir=.dico) + assign("txt_cross_validation" , "Validation croisee" , envir=.dico) + assign("txt_csv_file" , "Fichier CSV" , envir=.dico) + assign("txt_cumulated_explaination_ratio" , "Proportion cumulee de l'explication" , envir=.dico) + assign("txt_cumulated_explained_variance_ratio" , "proportion de variance expliquee cumulee" , envir=.dico) + assign("txt_dataframe_choice" , "Choix du dataframe" , envir=.dico) + assign("txt_data_import_export_save" , "Donnees - (Importation, exportation, sauvegarde)" , envir=.dico) + assign("txt_decimal_separator" , "Separateur de decimales" , envir=.dico) + assign("txt_default_outputs" , "Sorties par defaut" , envir=.dico) + assign("txt_delete_observations_with_missing_values" , "Suppression des observations avec valeurs manquantes" , envir=.dico) + assign("txt_denominator" , "Denominateur" , envir=.dico) + assign("txt_dependant_variables" , "Variable-s dependante-s" , envir=.dico) + assign("txt_dependant_variable" , "Variable dependante" , envir=.dico) + assign("txt_descriptive_statistics_by_group" , "statistiques descriptives par groupe" , envir=.dico) + assign("txt_detailed_corr_analysis" , "Analyse detaillee (Bravais Pearson/Spearman/tau) pour une ou peu de correlations" , envir=.dico) + assign("txt_deviation" , "Deviance" , envir=.dico) + assign("txt_dichotomic_ordinal" , "dichotomiques/ordinales" , envir=.dico) + assign("txt_difference" , "Difference" , envir=.dico) + assign("txt_distance_mediation_effect" , "Effet de mediation distante" , envir=.dico) + assign("txt_distance_mediator" , "Mediation a distance" , envir=.dico) + assign("txt_do_nothing_keep_all_obs" , "Ne rien faire - Garder l'ensemble des observations" , envir=.dico) + assign("txt_dot" , "point" , envir=.dico) + assign("txt_durbin_watson_test_autocorr" , "Test de Durbin-Watson - autocorrelations" , envir=.dico) + assign("txt_dw_statistic" , "statistique de D-W" , envir=.dico) + assign("txt_dynamic_crossed_table" , "Tableau croise dynamique" , envir=.dico) + assign("txt_effect" , "Effet" , envir=.dico) + assign("txt_equals_to" , "egal a" , envir=.dico) + assign("txt_error" , "erreur" , envir=.dico) + assign("txt_estimated_parameters_not_standardized" , "Parametres estimes non standardises" , envir=.dico) + assign("txt_estimated_parameters" , "Parametres estimes" , envir=.dico) + assign("txt_estimated_parameters_standardized" , "Parametres estimes standardises" , envir=.dico) + assign("txt_estimation" , "estimation" , envir=.dico) + assign("txt_excel_file" , "Fichier Excel" , envir=.dico) + assign("txt_exogenous_fixed_variables" , "Variables exogenes fixees [fixed.x=default]" , envir=.dico) + assign("txt_expected" , "Attendus" , envir=.dico) + assign("txt_expected_sample" , "Effectifs attendus" , envir=.dico) + assign("txt_experimental_pan_between" , "Pan experimental entre" , envir=.dico) + assign("txt_explaination_ratio" , "Proportion de l'explication" , envir=.dico) + assign("txt_explained_variance_ratio" , "proportion de variance expliquee" , envir=.dico) + assign("txt_explained_variance" , "Variance expliquee" , envir=.dico) + assign("txt_exponant" , "exposant" , envir=.dico) + assign("txt_exponant_or_root" , "exposant ou racine" , envir=.dico) + assign("txt_exponential" , "exponentiel" , envir=.dico) + assign("txt_export_data" , "exporter des donnees" , envir=.dico) + assign("txt_factorial_analysis" , "Analyse factorielle" , envir=.dico) + assign("txt_factorial_analysis_using_fa_with_method" , "analyse factorielle en utilisant la fonction fa du package psych avec la methode" , envir=.dico) + assign("txt_factorial_exploratory_analysis" , "Analyse factorielle exploratoire" , envir=.dico) + assign("txt_factor_name" , "Nom du facteur" , envir=.dico) + assign("txt_factors" , "facteurs." , envir=.dico) + assign("txt_factors_ortho" , "Orthogonalite des facteurs [orthogonal=FALSE]" , envir=.dico) + assign("txt_factors_to_keep_accord_to_parallel_analysis_is" , "le nombre de facteurs a retenir selon l'analyse en parallele est de" , envir=.dico) + assign("txt_fiability_analysis" , "analyse de fiabilite et d accord" , envir=.dico) + assign("txt_fiability_by_removed_item" , "fiabilite par item supprime" , envir=.dico) + assign("txt_for_a_detailed_results_description_distal" , "Pour une description detaillee des resultats, ?distal.med" , envir=.dico) + assign("txt_for_a_detailed_results_description_mediation" , "Pour une description detaillee des resultats, ?mediation" , envir=.dico) + assign("txt_forward_step_ascending" , "Forward - pas-a-pas ascendant" , envir=.dico) + assign("txt_friedman_anova_pairwise_comparison" , "Comparaison 2 a 2 pour ANOVA de Friedman" , envir=.dico) + assign("txt_f_value" , "valeur du F" , envir=.dico) + assign("txt_get_working_dir" , "obtenir le repertoire de travail" , envir=.dico) + assign("txt_global_model_estimation" , "Estimation du modele global" , envir=.dico) + assign("txt_graphic_mean_sd" , "Representation graphique - Moyenne et ecart-type" , envir=.dico) + assign("txt_graphics" , "Graphiques" , envir=.dico) + assign("txt_graphics_informations" , "Informations sur les graphiques" , envir=.dico) + assign("txt_group_analysis" , "Analyse par groupe" , envir=.dico) + assign("txt_groups_analysis" , "analyse par groupes" , envir=.dico) + assign("txt_groups_variables" , "Variable-s groupes" , envir=.dico) + assign("txt_grubbs_test" , "Test de Grubbs" , envir=.dico) + assign("txt_hierarchical_factorial_analysis" , "Analyse factorielle hierarchique" , envir=.dico) + assign("txt_hierarchical_model_analysis" , "Analyse hierarchique des modeles " , envir=.dico) + assign("txt_hierarchical_models_complete_model_sig_at_each_step" , "Modeles hierarchique - significativite du modele complet a chaque etape" , envir=.dico) + assign("txt_hierarchical_models_deviance_table" , "Table de l'analyse de la deviance des modeles hierarchiques" , envir=.dico) + assign("txt_hierarchical_models" , "Modeles hierarchiques" , envir=.dico) + assign("txt_hierarchical_models_variance_analysis_table" , "Table de l'analyse de variance des modeles hierarchiques" , envir=.dico) + assign("txt_hosmer_lemeshow_r_2" , "Hosmer and Lemeshow R^2" , envir=.dico) + assign("txt_hypergeom_total_sample_fixed_rows_cols" , "hypergeom - Effectif total fixe pour les lignes et les colonnes" , envir=.dico) + assign("txt_hypothesis_analysis" , "Analyses - Tests d'hypothese" , envir=.dico) + assign("txt_identified_outliers_synthesis" , "Synthese du nombre d'observations considerees comme influentes" , envir=.dico) + assign("txt_identifying_outliers" , "Identification des valeurs influentes" , envir=.dico) + assign("txt_id_variable" , "Variable *Identifiant*" , envir=.dico) + assign("txt_import_data" , "importer des donnees" , envir=.dico) + assign("txt_imput_missing_values" , "Imputation de valeurs manquantes" , envir=.dico) + assign("txt_independant_correlations" , "Correlations independantes" , envir=.dico) + assign("txt_independant_group_variables" , "Variables a groupes independants" , envir=.dico) + assign("txt_independant_variable" , "Variable independante" , envir=.dico) + assign("txt_indepmulti_fixed_sample_rows_cols" , "indepMulti - Effectif fixe pour les colonnes - variable" , envir=.dico) + assign("txt_indepmulti_total_fixed_rows_cols" , "indepMulti - Effectif total fixe pour les lignes - variable" , envir=.dico) + assign("txt_inferior" , "Inferieur" , envir=.dico) + assign("txt_inferior_or_equal_to" , "inferieur ou egal a" , envir=.dico) + assign("txt_inferior_proba" , "probabilite inferieure" , envir=.dico) + assign("txt_inferior_to" , "inferieur a" , envir=.dico) + assign("txt_inflation_variance_factor" , "Facteur d'inflation de la variance" , envir=.dico) + assign("txt_influence_method" , "Mesure d influence" , envir=.dico) + assign("txt_information" , "Information" , envir=.dico) + assign("txt_init_values" , "Valeurs de depart" , envir=.dico) + assign("txt_inspect_initial_values" , "Inspecter les valeurs de depart" , envir=.dico) + assign("txt_inspect_model_matrices" , "Inspecter les matrices du modele" , envir=.dico) + assign("txt_inspect_model_representation" , "Inspecter la representation du modele" , envir=.dico) + assign("txt_interaction_effects" , "Effets d'interaction" , envir=.dico) + assign("txt_interactive_model_variables" , "Variables modele interactif" , envir=.dico) + assign("txt_is_different_from" , "est different de" , envir=.dico) + assign("txt_jointmulti_total_fixed_sample" , "jointMulti - Effectif total fixe" , envir=.dico) + assign("txt_judge1" , "Juge 1" , envir=.dico) + assign("txt_judge2" , "Juge 2" , envir=.dico) + assign("txt_kaiser_meyer_olkin_index" , "Indice de Kaiser-Meyer-Olkin global" , envir=.dico) + assign("txt_keep_default_values" , "Garder les valeurs par defaut" , envir=.dico) + assign("txt_kendall_coeff" , "Coefficient de concordance de Kendall" , envir=.dico) + assign("txt_kendall_partial_semipartial_tau" , "Tau partiel/semi-partiel de Kendall" , envir=.dico) + assign("txt_kendall_partial_tau" , "Tau partiel de Kendall" , envir=.dico) + assign("txt_kendall_semipartial_tau" , "Tau semi-partiel de Kendall" , envir=.dico) + assign("txt_kendall_tau" , "Tau de Kendall" , envir=.dico) + assign("txt_kolmogorov_smirnov_comparing_two_distrib" , "Test de Kolmogorov-Smirnov comparant deux distributions" , envir=.dico) + assign("txt_labeled_outliers" , "Valeurs considerees comme influentes" , envir=.dico) + assign("txt_latent_variable_name" , "Nom de la variable latente" , envir=.dico) + assign("txt_less_square_diagonally_pondered" , "moindre carre pondere diagonalement" , envir=.dico) + assign("txt_less_square_generalized" , "moindre carre generalises" , envir=.dico) + assign("txt_less_square_not_pondered" , "moindre carre non pondere" , envir=.dico) + assign("txt_less_square_pondered" , "moindre carre pondere" , envir=.dico) + assign("txt_levene_test_verifying_homogeneity_variances" , "Test de Levene verifiant l'homogeneite des variances" , envir=.dico) + assign("txt_likelihood_only_for_estimator" , "Vraisemblance (seulement pour estimator=ML) [likelihood=default]" , envir=.dico) + assign("txt_likelihood_ratio_g_test" , "Rapport de vraisemblance (G test)" , envir=.dico) + assign("txt_lilliefors_d" , "D de Lilliefors" , envir=.dico) + assign("txt_linearity_graph_between_predictors_and_dependant_variable" , "Graphique testant la linearite entre les predicteurs et la variable dependante" , envir=.dico) + assign("txt_link_only_for_estimator" , "Lien (seulement pour estimator=MML) [link=probit]" , envir=.dico) + assign("txt_list_of_objects_in_mem" , "liste des objets en memoire" , envir=.dico) + assign("txt_logarithm" , "logarithme" , envir=.dico) + assign("txt_long_or_large_format" , "Format large au format long" , envir=.dico) + assign("txt_lower_bound_rmsea" , "limite inferieure du RMSEA" , envir=.dico) + assign("txt_mann_whitney_test" , "test de Mann-Whitney - Wilcoxon" , envir=.dico) + assign("txt_mathematical_operations_on_variables" , "Operations mathematiques sur des variables" , envir=.dico) + assign("txt_matrix_type" , "type de matrice" , envir=.dico) + assign("txt_max_likelihood_chi_squared_proba_value" , "valeur de la probabilite du chi carre du maximum de vraisemblance" , envir=.dico) + assign("txt_max_likelihood" , "maximum de vraisemblance" , envir=.dico) + assign("txt_mcnemar_results_between_var_x" , "Resultats du test de McNemar entre la variable" , envir=.dico) + assign("txt_mcnemar_test" , "Test de McNemar" , envir=.dico) + assign("txt_mcnemar_test_with_continuity_correction" , "Test de McNemar avec correction de continuite" , envir=.dico) + assign("txt_mcnemar_test_without_yates_correction" , "Test de McNemar sans correction de continuite" , envir=.dico) + assign("txt_mcnemar_test_with_yates_correction" , "Test de McNemar avec correction de Yates" , envir=.dico) + assign("txt_mean1" , "Moyenne1" , envir=.dico) + assign("txt_mean2" , "Moyenne2" , envir=.dico) + assign("txt_mean_complexity" , "Complexite moyenne" , envir=.dico) + assign("txt_mean_complexity_is" , "la complexite moyenne est de" , envir=.dico) + assign("txt_means_adjusted_standard_errors" , "moyennes et erreurs-types ajustees" , envir=.dico) + assign("txt_means_comparison" , "Comparaison de moyennes" , envir=.dico) + assign("txt_mean_sd_for_adjusted_data" , "Moyenne et ecart-type pour les donnees ajustees" , envir=.dico) + assign("txt_mean_sd_for_non_adjusted_data" , "Moyenne et ecart-type pour les donnees non ajustees" , envir=.dico) + assign("txt_mean_sd" , "Moyenne et ecart-type" , envir=.dico) + assign("txt_measured_variable_name" , "Nom de la variable mesuree" , envir=.dico) + assign("txt_median" , "Mediane" , envir=.dico) + assign("txt_mediation_effect" , "Effets de mediation" , envir=.dico) + assign("txt_mediator2" , "Mediateur 2" , envir=.dico) + assign("txt_mediator" , "Mediateur" , envir=.dico) + assign("txt_method_choice" , "Choix de la methode" , envir=.dico) + assign("txt_min_correlation_between_scores_and_factors" , "Correlation minimale possible des scores avec les facteurs" , envir=.dico) + assign("txt_minus" , "moins" , envir=.dico) + assign("txt_missing_values_treatment" , "Traitement des valeurs manquantes" , envir=.dico) + assign("txt_mixt_correlations" , "correlations mixtes" , envir=.dico) + assign("txt_modalities_name_for" , "Noms des modalites pour" , envir=.dico) + assign("txt_modalities_to_regroup" , "Modalites a regrouper" , envir=.dico) + assign("txt_modality" , "modalite" , envir=.dico) + assign("txt_model_degrees_of_freedom" , "degres de liberte du modele" , envir=.dico) + assign("txt_model_matrix" , "Matrices du modeles" , envir=.dico) + assign("txt_model_representation" , "Representation du modele" , envir=.dico) + assign("txt_model_significance" , "Significativite du modele global" , envir=.dico) + assign("txt_multicolinearity_tests" , "Tests de multicolinearite" , envir=.dico) + assign("txt_multicolinearity_test" , "Test de multicolinearite" , envir=.dico) + assign("txt_multiple_imputation_amelia" , "Multiple imputation - Amelia" , envir=.dico) + assign("txt_multiple_r_square_of_factors_scores" , "R carre multiple des scores avec les facteurs" , envir=.dico) + assign("txt_multiplication" , "multiplication" , envir=.dico) + assign("txt_multivariate_normality" , "Normalite multivariee" , envir=.dico) + assign("txt_nb_variables_measured" , "Nombre de variables mesurees" , envir=.dico) + assign("txt_negative_values" , "Valeurs negatives" , envir=.dico) + assign("txt_new_data_set" , "nouveau set de donnees" , envir=.dico) + assign("txt_new_dir" , "nouveau repertoire" , envir=.dico) + assign("txt_N_of_XY_corr" , "N de la correlation XY" , envir=.dico) + assign("txt_N_of_XY_NUM_corr" , "N de la correlation XY:TXT" , envir=.dico) + assign("txt_N_of_XZ_corr" , "N de la correlation XZ" , envir=.dico) + assign("txt_N_of_XZ_NUM_corr" , "N de la correlation XZ:TXT" , envir=.dico) + assign("txt_non_adjusted_data" , "Donnees non ajustees" , envir=.dico) + assign("txt_non_centered" , "Non centre" , envir=.dico) + assign("txt_no" , "non" , envir=.dico) + assign("txt_non_parametric_test" , "Test non parametrique" , envir=.dico) + assign("txt_non_param_model" , "Modele non parametrique" , envir=.dico) + assign("txt_non_param_test" , "test non parametrique" , envir=.dico) + assign("txt_non_pondered_coeff" , "Coefficient kappa non pondere" , envir=.dico) + assign("txt_non_standardized_residuals" , "Residus non standardises" , envir=.dico) + assign("txt_null_hypothesis_tests" , "Tests de H0" , envir=.dico) + assign("txt_null_model_degrees_of_freedom" , "Degres de liberte du modele null" , envir=.dico) + assign("txt_numerator" , "Numerateur" , envir=.dico) + assign("txt_objective_function_of_model" , "fonction objective du modele" , envir=.dico) + assign("txt_objective_function_of_null_model" , "fonction objective du modele null" , envir=.dico) + assign("txt_objects_in_mem" , "Objets en memoire" , envir=.dico) + assign("txt_object_to_remove" , "Objets a supprimer" , envir=.dico) + assign("txt_observed" , "Observes" , envir=.dico) + assign("txt_observed_sample" , "Effectifs Observes" , envir=.dico) + assign("txt_odd_ratio" , "Odd ratio" , envir=.dico) + assign("txt_order" , "Trier" , envir=.dico) + assign("txt_orthogonals_inverse" , "orthogonaux inverses" , envir=.dico) + assign("txt_orthogonals" , "orthogonaux" , envir=.dico) + assign("txt_other_correlations" , "Autres correlations" , envir=.dico) + assign("txt_other_data" , "autres donnees" , envir=.dico) + assign("txt_outliers" , "observations influentes" , envir=.dico) + assign("txt_outliers_synthesis" , "Synthese des observations influentes" , envir=.dico) + assign("txt_outliers_values" , "Valeurs influentes" , envir=.dico) + assign("txt_packages_install" , "Installation des packages" , envir=.dico) + assign("txt_packages_update" , "mise a jour des packages" , envir=.dico) + assign("txt_packages_verification" , "Verification des packages" , envir=.dico) + assign("txt_parallel_analysis" , "analyses paralleles" , envir=.dico) + assign("txt_param_model" , "Modele parametrique" , envir=.dico) + assign("txt_param_tests" , "Test parametrique" , envir=.dico) + assign("txt_param_test" , "test parametrique" , envir=.dico) + assign("txt_partial_and_semi_correlations" , "Correlations partielle et semi partielle" , envir=.dico) + assign("txt_partial_corr_BP_by_group" , "Correlation partielle de Bravais-Pearson par groupe" , envir=.dico) + assign("txt_partial_correlations_matrix" , "Matrice de Correlations partielles" , envir=.dico) + assign("txt_partial_rho" , "Rho partiel de Spearman" , envir=.dico) + assign("txt_partial_semi_BP" , "Correlation partielle/semi-partielle de Bravais Pearson" , envir=.dico) + assign("txt_partial_semi_partial_rho" , "Rho partiel/semi partiel de Spearman" , envir=.dico) + assign("txt_partial_spearman_by_group" , "Correlation partielle de Spearman par groupe" , envir=.dico) + assign("txt_participants_id" , "Identifiant participant" , envir=.dico) + assign("txt_partila_correlations" , "Correlations partielles" , envir=.dico) + assign("txt_percentage_col" , "Pourcentage par colonne" , envir=.dico) + assign("txt_percentage_row" , "Pourcentage par ligne" , envir=.dico) + assign("txt_percentage_total" , "Pourcentage total" , envir=.dico) + assign("txt_percentile_bootstrap_on_m_estimators" , "Percentile bootstrap sur les M-estimator" , envir=.dico) + assign("txt_p_estimation_with_monter_carlo" , "Valeur estimee de p par simulation de Monte Carlo" , envir=.dico) + assign("txt_plus" , "plus" , envir=.dico) + assign("txt_poisson_total_not_fixed_sample" , "poisson - Effectif total non fixe" , envir=.dico) + assign("txt_polyc_correlations" , "correlations polychoriques" , envir=.dico) + assign("txt_polynomials" , "polynomiaux" , envir=.dico) + assign("txt_pondered_kappa" , "Coefficient kappa pondere" , envir=.dico) + assign("txt_positive_values" , "Valeurs positives" , envir=.dico) + assign("txt_predicted_probabilities" , "Probabilites predites" , envir=.dico) + assign("txt_predictor" , "Predicteur" , envir=.dico) + assign("txt_principal_analysis" , "Analyse principale" , envir=.dico) + assign("txt_principal_analysis_using_psych_with_algo" , "analyse en composante principale en utilisant la fonction [principal] du package psych, l'algorithme est" , envir=.dico) + assign("txt_principal_component_analysis" , "Analyse en composante principale" , envir=.dico) + assign("txt_probabilities" , "probabilites" , envir=.dico) + assign("txt_probability_matrix" , "matrice des probabilites" , envir=.dico) + assign("txt_probability_value" , "valeur de la probabilite" , envir=.dico) + assign("txt_proper_values_index" , "Indice des valeurs propres" , envir=.dico) + assign("txt_pseudo_r_square_delta" , "Delta du pseudo R carre" , envir=.dico) + assign("txt_p_value_with_monte_carlo" , "Valeur p par simulation de Monte Carlo" , envir=.dico) + assign("txt_ranks_lower" , "rangs" , envir=.dico) + assign("txt_ranks_upper" , "Rangs" , envir=.dico) + assign("txt_references" , "References" , envir=.dico) + assign("txt_remove_object_in_memory" , "Suppression d objet en memoire" , envir=.dico) + assign("txt_replace_by_mean" , "Remplacer par la moyenne" , envir=.dico) + assign("txt_replace_by_median" , "Remplacer par la mediane" , envir=.dico) + assign("txt_residual_distribution" , "Distribution du residu" , envir=.dico) + assign("txt_residual_error" , "Erreur residuelle" , envir=.dico) + assign("txt_residual" , "residu" , envir=.dico) + assign("txt_residuals_distribution" , "Distribution des residus" , envir=.dico) + assign("txt_residue" , "Residus" , envir=.dico) + assign("txt_residues_significativity_holm_correction" , "Significativite des residus - probabilite corrigee en appliquant la methode de Holm" , envir=.dico) + assign("txt_residue_standardized_adjusted" , "Residus standardises ajustes" , envir=.dico) + assign("txt_residue_standardized" , "Residus standardises" , envir=.dico) + assign("txt_result" , "Resultat" , envir=.dico) + assign("txt_rho" , "Rho de Spearman" , envir=.dico) + assign("txt_robust_analysis" , "Analyses robustes" , envir=.dico) + assign("txt_robusts" , "robustes" , envir=.dico) + assign("txt_robusts_statistics" , "Statistiques robustes" , envir=.dico) + assign("txt_robust_statistics" , "Statistiques robustes - peut prendre du temps" , envir=.dico) + assign("txt_robusts_tests_with_bootstraps" , "Test robustes - impliquant des bootstraps" , envir=.dico) + assign("txt_rotation_is_a_rotation" , "la rotation est un rotation" , envir=.dico) + assign("txt_sample_size_NUM" , "Taille de l'echantillon:TXT" , envir=.dico) + assign("txt_saturations_sum_of_squares" , "Sommes des carres des saturations" , envir=.dico) + assign("txt_search_for_new_function" , "rechercher une nouvelle fonction" , envir=.dico) + assign("txt_second_variables_set" , "Second jeu de variables" , envir=.dico) + assign("txt_selected_data" , "donnees que vous venez de selectionner" , envir=.dico) + assign("txt_selection_method_akaike" , "Methode de selection - criteres d'information d'Akaike" , envir=.dico) + assign("txt_selection_method_bayesian_factor" , "Methodes de selection : facteurs bayesiens" , envir=.dico) + assign("txt_selection_method" , "Methode de selection" , envir=.dico) + assign("txt_selection_methods" , "Methodes de selection" , envir=.dico) + assign("txt_selection" , "selection" , envir=.dico) + assign("txt_select_obs" , "Selectionner des observations" , envir=.dico) + assign("txt_select_variables" , "Selectionner des variables" , envir=.dico) + assign("txt_semi_BP" , "Correlation semi-partielle de Bravais Pearson" , envir=.dico) + assign("txt_semicolon" , "point virgule" , envir=.dico) + assign("txt_semi_partial_rho" , "Rho semi-partiel de Spearman" , envir=.dico) + assign("txt_sequential_bayesian_factors_robustness_analysis" , "Facteurs bayesiens sequentiels - Analyse de robustesse" , envir=.dico) + assign("txt_shapiro_wilk" , "W de Shapiro-Wilk" , envir=.dico) + assign("txt_simple_mediation_effect" , "Effets de mediation simple" , envir=.dico) + assign("txt_slopes_homogeneity_between_groups_on_dependant_variable" , "Test de l'homogeneite des pentes entre les groupes sur la variable dependante" , envir=.dico) + assign("txt_spearman_kendall_corr_by_group" , "Correlation de Spearman/Kendall par groupe" , envir=.dico) + assign("txt_specific_val_multiplication" , "multiplication d'une valeur specifique" , envir=.dico) + assign("txt_specify_contrasts" , "specifier vos contrastes" , envir=.dico) + assign("txt_specify_model" , "Specifier le modele" , envir=.dico) + assign("txt_specify_working_dir" , "specifier le repertoire de travail" , envir=.dico) + assign("txt_spss_file" , "fichier SPSS" , envir=.dico) + assign("txt_square" , "carree" , envir=.dico) + assign("txt_rectangular" , "rectangulaire" , envir=.dico) + assign("txt_standardized_parameters" , "Parametres standardises" , envir=.dico) + assign("txt_statistic" , "statistique" , envir=.dico) + assign("txt_step" , "etape" , envir=.dico) + assign("txt_student_bootstrap_on_truncated_means" , "bootstrap studentise sur les moyennes tronquees" , envir=.dico) + assign("txt_student_t_by_group" , "t de Student par groupe" , envir=.dico) + assign("txt_student_t_independant" , "t de student pour echantillons independants" , envir=.dico) + assign("txt_student_t" , "t de Student" , envir=.dico) + assign("txt_student_t_test_norm" , "Test de Student - comparaison a une norme" , envir=.dico) + assign("txt_student_t_test_paired" , "Test de Student - comparaison de deux echantillons apparies" , envir=.dico) + assign("txt_substraction" , "soustraction" , envir=.dico) + assign("txt_sufficient_factors" , "facteurs suffise(nt)" , envir=.dico) + assign("txt_superior_or_equal_to" , "superieur ou egal a" , envir=.dico) + assign("txt_superior_proba" , "probabilite superieure" , envir=.dico) + assign("txt_superior" , "Superieur" , envir=.dico) + assign("txt_superior_to" , "superieur a" , envir=.dico) + assign("txt_supports_alternative" , "En faveur de l'hypothese alternative" , envir=.dico) + assign("txt_supports_null" , "En faveur de l'hypothese nulle" , envir=.dico) + assign("txt_suppress_all_outliers" , "Suppression de l'ensemble des outliers" , envir=.dico) + assign("txt_suppress_outliers_manually" , "Suppression manuelle" , envir=.dico) + assign("txt_synthesis_table" , "Tableau de synthese" , envir=.dico) + assign("txt_teaching_material" , "Materiel pedagogique" , envir=.dico) + assign("txt_tetra_polyc_corr_matrix_or_mixt" , "Matrice de correlation tetrachorique/polychorique ou mixte" , envir=.dico) + assign("txt_this_tests_if" , "Cela teste si" , envir=.dico) + assign("txt_threshold" , "Seuil" , envir=.dico) + assign("txt_time_1" , "temps 1" , envir=.dico) + assign("txt_time1" , "temps1" , envir=.dico) + assign("txt_time_2" , "temps 2" , envir=.dico) + assign("txt_time2" , "temps2" , envir=.dico) + assign("txt_tolerance" , "Tolerance" , envir=.dico) + assign("txt_total_sample_not_fixed" , "Effectif total non fixe" , envir=.dico) + assign("txt_troncature_num" , "Troncature:TXT" , envir=.dico) + assign("txt_truncated_means" , "moyennes tronquees" , envir=.dico) + assign("txt_t_test_choice" , "Choix du test t" , envir=.dico) + assign("txt_tucker_lewis_fiability_factor" , "facteur de fiabilite de Tucker Lewis - TLI" , envir=.dico) + assign("txt_two_independant_samples" , "Deux echantillons independants" , envir=.dico) + assign("txt_two_paired_samples" , "Deux echantillons apparies" , envir=.dico) + assign("txt_txt_file" , "Fichier txt" , envir=.dico) + assign("txt_type" , "Type" , envir=.dico) + assign("txt_understanding_alpha_and_power" , "Comprendre alpha et la puissance" , envir=.dico) + assign("txt_understanding_bayesian_inference" , "Comprendre une inference bayesienne" , envir=.dico) + assign("txt_understanding_central_limit_theorem" , "Comprendre le theorem central limit" , envir=.dico) + assign("txt_understanding_confidance_interval" , "Comprendre un intervalle de confiance" , envir=.dico) + assign("txt_understanding_corr_2" , "Comprendre une correlation 2" , envir=.dico) + assign("txt_understanding_corr" , "Comprendre la correlation" , envir=.dico) + assign("txt_understanding_heterogenous_variance_effects" , "Comprendre les effets de variances heterogenes" , envir=.dico) + assign("txt_understanding_likelihood" , "Comprendre le maximum de vraisemblance" , envir=.dico) + assign("txt_understanding_negative_positive_predic_power" , "Comprendre le pouvoir predictif positif et le pouvoir predictif negatif" , envir=.dico) + assign("txt_understanding_prev_sens_specificity_2" , "Comprendre la prevalence, la sensibilite et la specificite 2" , envir=.dico) + assign("txt_understanding_prev_sens_specificity" , "Comprendre la prevalence, la sensibilite et la specificite" , envir=.dico) + assign("txt_upper_bound_rmsea" , "limite superieure du RMSEA" , envir=.dico) + assign("txt_user_exited_easieR" , "Vous avez quitte easieR" , envir=.dico) + assign("txt_values" , "valeurs" , envir=.dico) + assign("txt_value" , "valeur" , envir=.dico) + assign("txt_variable_descriptive_statistics" , "Statistiques descriptives de la variable" , envir=.dico) + assign("txt_variables_coeff_matrix" , "Matrice de coefficients variables" , envir=.dico) + assign("txt_variables_contribution_to_model" , "Contribution des variables au modele" , envir=.dico) + assign("txt_variables_from_step" , "Variable(s) de cette etape" , envir=.dico) + assign("txt_verify_packages_install" , "Verifier l installation des packages" , envir=.dico) + assign("txt_view_data" , "voir des donnees" , envir=.dico) + assign("txt_VIF" , "FIV" , envir=.dico) + assign("txt_warning" , "Avertissement" , envir=.dico) + assign("txt_wilcoxon_by_group" , "Wilcoxon par groupe" , envir=.dico) + assign("txt_without_outliers" , "Donnees sans valeur influente" , envir=.dico) + assign("txt_without_welch_correction" , "sans correction de Welch" , envir=.dico) + assign("txt_without_yates_correction" , "Sans correction de Yates" , envir=.dico) + assign("txt_with_welch_correction" , "avec correction de Welch" , envir=.dico) + assign("txt_with_yates_correction" , "Avec correction de Yates" , envir=.dico) + assign("txt_working_dir" , "Repertoire de travail" , envir=.dico) + assign("txt_x_axis_variables" , "Variable-s en abcisse" , envir=.dico) + assign("txt_XY_correlation" , "Correlation entre XY" , envir=.dico) + assign("txt_XY_NUM_correlation" , "Correlation entre XY:TXT" , envir=.dico) + assign("txt_XZ_correlation" , "Correlation entre XZ" , envir=.dico) + assign("txt_XZ_NUM_correlation" , "Correlation entre XZ:TXT" , envir=.dico) + assign("txt_y_axis_variables" , "Variable-s en ordonnee" , envir=.dico) + assign("txt_yes" , "oui" , envir=.dico) + assign("txt_your_data" , "Vos donnees" , envir=.dico) + assign("txt_YZ_correlation" , "Correlation entre YZ" , envir=.dico) + assign("txt_YZ_NUM_correlation" , "Correlation entre YZ:TXT" , envir=.dico) + assign("ask_probability_correction" , "Which p adjustment do you want ? If you do not want any p adjust, choose +none+" , envir=.dico) + assign("ask_contrasts_must_be_ortho" , "The contrasts must be orthogonal. Do you want to continue ?" , envir=.dico) + assign("desc_bayesian_factors_chosen_in" , "Facteurs bayesiens is choosen in " , envir=.dico) + assign("desc_cross_validation_issues" , "cross validation is encountering some issues" , envir=.dico) + assign("desc_easier_metapackage" , "easieR: An R metapackage. Retrieved from https://github.com/NicolasStefaniak/easieR" , envir=.dico) + assign("desc_first_time_easier" , " If you are using easieR for the first time, please use the function ez.install in order to ensure that easieR will work properly.n Si vous utilisez easieR pour la 1e fois, veuillez utiliser la fonction ez.install pour vous assurer de bon fonctionnement de easieR." , envir=.dico) + assign("ask_chose_variables" , "veuillez choisir la ou les variables " , envir=.dico) + assign("ask_correlations_type" , "Type de correlations ?" , envir=.dico) + assign("ask_dependant_variable_name" , "Quel est le nom de la variable dependante?" , envir=.dico) + assign("ask_factors_number" , "Nombre de facteurs ?" , envir=.dico) + assign("ask_filename" , "Quel nom voulez-vous donner au fichier?" , envir=.dico) + assign("ask_independant_variable_name" , "Quel est le nom de la variable independante?" , envir=.dico) + assign("ask_is_long_format_correct" , "Est-ce que la structure dans un format long de vos donnees est correcte ?" , envir=.dico) + assign("ask_model" , "Modele ?" , envir=.dico) + assign("ask_ordinal_variables" , "Variables ordinales ?" , envir=.dico) + assign("ask_save_results" , "Enregistrer les resultats ?" , envir=.dico) + assign("ask_save" , "Voulez-vous sauvegarder ?" , envir=.dico) + assign("ask_specify_contrasts" , "Veuillez spĂ©cifier les contrastes." , envir=.dico) + assign("ask_variables" , "Quelles sont les variables a selectionner ?" , envir=.dico) + assign("ask_variables_type" , "Nature des variables ?" , envir=.dico) + assign("ask_what_to_do" , "Que voulez-vous faire ?" , envir=.dico) + assign("ask_which_analysis" , "Quelle analyse voulez-vous?" , envir=.dico) + assign("desc_all_contrasts_description" , "Les contrastes a priori correspondent aux contrastes qui permettent de tester des hypotheses a priori.\nLes contrastes 2 a 2 permettent de faire toutes les comparaisons 2 a 2 en appliquant ou non une correction a la probabilite" , envir=.dico) + assign("desc_contrasts_must_be_coeff_matrices_in_list" , "Les contrates doivent etre des matrices de coefficients placees dans une list dont le nom de chaque niveau correspond a un facteur" , envir=.dico) + assign("desc_percentage_outliers" , "% d'observations considerees comme influentes" , envir=.dico) + assign("desc_robusts_statistics_could_not_be_computed_verify_WRS" , "Les statistiques robustes n'ont pas pu etre realisees. Verifiez l'installation du package WRS" , envir=.dico) + assign("desc_some_participants_have_missing_values_on_repeated_measures" , "Certains participants ont des valeurs manquantes sur les facteurs en mesures repetees. Ils vont etre supprimes des analyses" , envir=.dico) + assign("txt_absence_of_difference_between_groups_test_on" , "Test de l'absence de difference entre les groupes sur " , envir=.dico) + assign("txt_anova_on_medians" , "Anova sur les medianes" , envir=.dico) + assign("txt_anova_on_m_estimator" , "ANOVA sur M estimator" , envir=.dico) + assign("txt_bayesian_factors" , "Facteurs bayesiens" , envir=.dico) + assign("txt_BP_correlation" , "Correlation de Bravais-Pearson" , envir=.dico) + assign("txt_center" , "centrer" , envir=.dico) + assign("txt_cohen_d" , "D de Cohen" , envir=.dico) + assign("txt_correlations" , "Correlations" , envir=.dico) + assign("txt_correlations_matrix" , "Matrice de correlations" , envir=.dico) + assign("txt_descriptive_statistics_of_interaction_between_x" , "Statistiques descriptives de l'interaction entre" , envir=.dico) + assign("txt_descriptive_statistics" , "Statistiques descriptives" , envir=.dico) + assign("txt_empirical_chi_square_proba_value" , "valeur de la probabilite du chi carre empirique" , envir=.dico) + assign("txt_factor" , "facteur." , envir=.dico) + assign("txt_friedman_anova" , "Anova de Friedman" , envir=.dico) + assign("txt_import_results" , "importer des resultats" , envir=.dico) + assign("txt_interface_objects_in_memory" , "Interface - objets en memoire, nettoyer la memoire, repertoire de travail, langue" , envir=.dico) + assign("txt_intraclass_correlation" , "Correlation intra-classe" , envir=.dico) + assign("txt_kruskal_wallis_pairwise" , "Test de Kruskal-Wallis - Comparaison deux a deux" , envir=.dico) + assign("txt_kruskal_wallis_test" , "Test de Kruskal-Wallis" , envir=.dico) + assign("txt_latent_variables_intercept" , "Intercept des variables latentes [int.lv.free=FALSE]" , envir=.dico) + assign("txt_observed_variables_intercept" , "Intercept des variables observees [int.ov.free=FALSE]" , envir=.dico) + assign("txt_logistic_regressions" , "Regressions logistiques" , envir=.dico) + assign("txt_mauchly_test_sphericity_covariance_matrix" , "Test de Mauchly testant la sphericite de la matrice de covariance" , envir=.dico) + assign("txt_none" , "aucun" , envir=.dico) + assign("txt_non_param_analysis" , "Analyse non parametrique" , envir=.dico) + assign("txt_normality_tests" , "Tests de normalite" , envir=.dico) + assign("txt_pairwise_comparisons" , "Comparaisons 2 a 2" , envir=.dico) + assign("txt_pairwise" , "pairwise" , envir=.dico) + assign("txt_partial_corr_BP" , "Correlation partielle de Bravais-Pearson" , envir=.dico) + assign("txt_preprocess_sort_select_operations" , "Pretraitements (tri, selection, operations mathematiques, Traitement des valeurs manquantes)" , envir=.dico) + assign("txt_press_enter_to_continue" , "Appuyez sur [entree] pour continuer" , envir=.dico) + assign("txt_regressions" , "regressions" , envir=.dico) + assign("txt_repeated_measures" , "Mesures repetees" , envir=.dico) + assign("txt_sample_size" , "taille de l'echantillon" , envir=.dico) + assign("txt_test_model" , "Modele teste" , envir=.dico) + assign("txt_variables" , "variables" , envir=.dico) + assign("txt_variable" , "variable" , envir=.dico) + assign("desc_corr_group_analysis_spec" , "Si vous souhaitez realiser l'analyse pour differents sous-echantillons en fonction d'un critere categoriel (i.e; realiser une analyse par groupe) \n choisissez oui. Dans ce cas, l'analyse est realisee sur l'echantillon complet et sur les sous-echantillons. \n Si vous desirez l'analyse pour l'echantillon complet uniquement, chosissez non. \n l'analyse par groupe ne s'appliquent pas aux statistiques robustes." , envir=.dico) + assign("desc_outliers_removal_implications" , "Supprimer l'ensemble des outliers supprime l'ensemble des valeurs au-delĂ  p(chi.deux)< 0.001. Supprimer une observation Ă  la fois permet de faire une analyse detaillee de chaque observation consideree comme influente en partant de la valeur la plus extreme. La procedure s'arrete quand plus aucune observation n'est consideree comme influente" , envir=.dico) + assign("txt_bilateral" , "Bilateral" , envir=.dico) + assign("desc_no_compatible_object_in_mem_for_aov" , "il n'y a pas d'objet compatible avec aov.plus dans la memoire de R. Vous devez realiser une analyse de variance au prealable" , envir=.dico) + assign("desc_this_function_means_and_sd_adjusted_interaction_effect_possible" , "Cette fonction permet de fournir les moyennes et erreurs-types ajustees ainsi que le graphique correspondant. Avec le choix post hoc sur les interactions, vous pouvez tester les effets d'interaction 2 a 2 et les effet simples." , envir=.dico) + assign("txt_anova_plus" , "Anova plus" , envir=.dico) + assign("desc_center_and_center_reduce_explaination" , "Centrer permet d'avoir une moyenne a zero en maintenant l'ecart-type. Centrer reduire correspond a la formule du z. La moyenne est de 0 et l'ecart-type vaut 1. La probabilite inferieure correspond a la probabilite d'avoir un z inferieur ou egal au z. La probabilite superieure correspond a la probabilite d'avoir un z superieur ou egal au z" , envir=.dico) + assign("desc_proba_sum_is_not_one_or_not_enough_proba" , "La somme des probabilites est differente de 1 ou le nombre de probabilites ne correspond pas au nombre de modalites de la variable. Veuillez entrer un vecteur de probabilites valide" , envir=.dico) + assign("desc_if_non_fixed_sample_poisson_law" , "Si l'effectif total est non fixe, on fait l'hypothese que les observations surviennent en respectant une loi de poisson. La repartition sur les niveaux d'un facteur surviennent avec une probabilite fixe. La distribution est une distribution poisson" , envir=.dico) + assign("desc_distribution_is_joint_multinomial" , "L'option *Effectif total fixe* doit etre choisi si on fait l'hypohese nulle que la repartition dans chacune des cellules du tableau est fixee. La distribution est une distribution multinomiale jointe" , envir=.dico) + assign("desc_distribution_is_independant_multinomial" , "L'option Effectif total fixe pour les lignes* doit etre choisi si les effectifs pour chaque ligne est identique, comme lorsqu'on veut s'assurer d'un appariement entre groupes. La distribution est une distribution multinomiale independante" , envir=.dico) + assign("desc_corr_detailed_analysis" , "l'analyse detaillee permet d'avoir les statistiques descriptives, les tests de normalite, le nuage de points, \n des statistiques robustes, l'ensemble des coefficients de correlations. \n la matrice de correlation permet de contrĂ´ler l'erreur de 1e espece et est adaptee pour un grand nombre de correlations \n la comparaison de correlations permet de comparer 2 correlations dependantes ou independantes \n Le choix + autre correlations + permet d'avoir les correlation tetrachoriques et polychoriques" , envir=.dico) + assign("desc_corr_values_must_be_between_min_1_and_1" , "Les valeurs des correlations doivent etre comprises entre -1 et 1/n et les effectifs doivent etre des entiers positifs" , envir=.dico) + assign("desc_you_can_choose_contrasts_you_want" , "Vous pouvez choisir les contrastes que vous souhaitez. Neanmoins les regles concernant l'application des contrastes doivent etre respectees. Les contrastes peuvent etre specifies manuellement. Dans ce cas, veuillez choisir specifier les contrastes" , envir=.dico) + assign("desc_square_matrix_rectangular_matrix" , "Une matrice carree est une matrice avec toutes les Correlations 2 a 2. Une matrice rectangulaire est une matrice dans laquelle un premier ensemble de variables est mis en correlations avec un second jeu de variables" , envir=.dico) + assign("desc_complete_dataset_vs_identification_outliers_vs_without_outliers" , "les donnees completes representent l'analyse classique sur toutes les donnees utilisables, l'identification des valeurs influentes permet d'identifier les observations qui sont considerees statistiquement comme influencant les resultats. les analyses sur les donnees sans les valeurs influentes realise l'analyse apres suppression des valeurs influentes. Cette option stocke dans la memoire de R une nouvelle base de donnees sans valeur influente dans un objet portant le nom *nettoyees*" , envir=.dico) + assign("desc_welcome_in_easieR" , "Welcome in easieR - For more information, please visit :https://theeasierproject.wordpress.com/" , envir=.dico) + assign("ask_variables_type_for_anova" , "Veuillez preciser le(s) type(s) de variable(s) que vous souhaitez inclure dans l'analyse.\nVous pouvez en choisir plusieurs (e.g., pour anova mixte ou des ancova" , envir=.dico) + assign("ask_correction_anova_contrasts" , "Correction ?" , envir=.dico) + assign("txt_independant_groups" , "Groupes independants" , envir=.dico) + assign("txt_covariables" , "Covariables" , envir=.dico) + assign("txt_cfa_information_default" , "information [information=default]" , envir=.dico) + assign("txt_cfa_continuity_correction_zero_keep_margins_default" , "correction de continuite [zero.keep.margins=default]" , envir=.dico) + assign("txt_cfa_estimator_ml_default" , "estimateur [estimator=ml])" , envir=.dico) + assign("txt_cfa_groups_null_default" , "groupes [group=NULL]" , envir=.dico) + assign("txt_cfa_test_standard_default" , "test [test=standard]" , envir=.dico) + assign("txt_cfa_standard_error_default" , "erreur standard [se=standard]" , envir=.dico) + assign("txt_cfa_observed_variabes_standardization_true_default" , "standardisation des variables observees [std.ov=T]" , envir=.dico) + assign("txt_cfa_latent_variables_indicators_estimates_true_default" , "Estimation des indicateurs des variables latentes [std.lv=FALSE]" , envir=.dico) + assign("desc_wls_corresponds_to_adf_plus_explaination_other_estimators" , "[WLS] correspond a [ADF]. Les estimateurs avec les extensions [M],[MV],[MVSF],[R] sont des versions robustes des estimateurs classiques [MV],[WLS], [DWLS], [ULS]" , envir=.dico) + assign("ask_observed_variables_intercept_zero" , "Intercept VO=0 ?" , envir=.dico) + assign("ask_latent_variables_intercept_zero" , "Intercept VL=0 ?" , envir=.dico) + assign("ask_how_to_treat_exaequo_rank" , "Comment voulez-vous traiter les ex-aequo ? La methode *average* fait la moyenne entre les ex aequo (le plus habituel), *first* attribue le premier rang ex aequo a la premiere valeur dans les donnees, *laste* a la derniere, *min* attribue la valeur minimale a l'ensemble des ex aequo et *max* la valeur maximale." , envir=.dico) + assign("desc_for_ordinal_and_dicho_varible_prefer_min_res" , "Pour les variables ordinales et dichomiques, preferez la methode du minimum des residus - minres - ou des moindres carres ponderes - wls. Pour les variables continues, le maximum de vraisemblance si la normalite est respectee - ml" , envir=.dico) + assign("desc_saturation_criterion_show_only_above_threshold" , "Le critere de saturation permet de n'afficher dans le tableau de resultats que les saturation superieure au seuil fixe" , envir=.dico) + assign("desc_to_find_new_analysis_search_in_english" , "Pour trouver une nouvelle analyse, il est necessaire de faire votre recherche en anglais. Vous pouvez utiliser plusieurs mots dans la recherche. Une page html reprenant l'ensemble des packages faisant reference a l'analyse recherchee va s'ouvrir." , envir=.dico) + assign("txt_division" , "division" , envir=.dico) + assign("desc_if_you_select_both_operations_value_will_be_added_to_chose_cols" , "Si vous selectionnez les deux options en meme temps, la valeur specifiee sera ajoutee a l'ensemble des colonnes choisies et ensuite les colonnes choisies seront additionnees. Pour additionner une valeur specifique au total, veuillez choisir l'option addition de colonnes uniquement." , envir=.dico) + assign("desc_if_you_select_both_operations_value_will_be_multiplied_to_chose_cols" , "Si vous selectionnez les deux options en meme temps, la valeur specifiee sera multipliee a l'ensemble des colonnes choisies et ensuite les colonnes choisies seront multipliees entre elles. Pour multiplier une valeur specifique au total, veuillez choisir l'option multipication de colonnes uniquement." , envir=.dico) + assign("ask_chose_values_on_left_of_minus_symbol" , "Veuillez selectionner les valeurs situees a gauche du symbole *moins*. Si plusieurs variables sont selectionnees, les regles du calcul matriciel sont appliques." , envir=.dico) + assign("desc_one_or_same_number_cols_on_both_sides_only" , "Il ne doit y avoir qu'une colonne ou le nombre de colonnes a droite du symbole *moins* doit etre egal au nombre de colonnes a gauche du symbole *moins*" , envir=.dico) + assign("ask_specify_exponant_value" , "Veuillez preciser la valeur de l'exposant. NOTE : Pour les racines, l'exposant est l'inverse la valeur. Par exemple, La racine carree vaut 1/2, la racine cubique 1/3... " , envir=.dico) + assign("desc_expression_must_be_correct_example" , "L'expression doit etre correcte. Vous pouvez utiliser directement le nom des variables les operateurs sont +,-,*,/,^,(,). Une expression correcte serait :" , envir=.dico) + assign("ask_chose_relation_between_vars_regressions_log" , "Veuillez choisir le(s) type(s) de relations entre les variables. Les effets additifs prennent la forme de y=X1+X2 tandis que les effets d'interaction prennent la forme de Y=X1+X2+X1:X2" , envir=.dico) + assign("ask_variables_order_for_max_likelihood" , "L'ordre d'entree des variables est important pour le calcul du maximum de vraisemblance. Veuillez preciser l'ordre d'entree des variables" , envir=.dico) + assign("ask_integrate_probabilities_to_dataset" , "voulez-vous integrer les probabilites a votre base de donnees ?" , envir=.dico) + assign("ask_specify_other_options_regressions" , "Voulez-vous preciser d'autres options ? Vous pouvez en selectionner plusieurs. Les methodes de selection permettent de selectionner le meilleur modele sur la base de criteres statistiques. Les modeles hierarchiques permettent de comparer plusieurs modeles. Les validations croisees permettent de verifier si un modele n'est pas dependant des donnees. Cette option est a utiliser notamment avec les methodes de selection. L'analyse par groupe permet de realiser la meme regression pour des sous-groupes. Les mesures d'influences sont les autres mesures habituellement utilisees pour identifier les valeurs influentes." , envir=.dico) + assign("desc_possible_apply_multiple_selection_criterion" , "Il est possible d'appliquer plusieurs criteres de selection simultanement, impliquant ou non plusieurs variables. Veuillez preciser le nombre de variables sur lesquelles vous desirez appliquer un ou plusieurs criteres de selection. Veuillez choisir les variables sur lesquelles vous deirez appliquer une selection" , envir=.dico) + assign("desc_skew_and_kurtosis_between_1_and_3" , "Type de skew et kurtosis, doit se situer entre 1 et 3:TXT" , envir=.dico) + assign("desc_with_two_equal_means_ratio_must_be_5_percent" , "Avec deux moyennes egales, ou pratiquement egales, le taux d'erreurs doit etre de 5%. Modifiez progressivement l'ecart entre les ecart-types et voyez comment le taux d'erreur alpha va etre modifie" , envir=.dico) + assign("desc_bilateral_superior_inferior_test_t" , "Une analyse bilaterale teste l'existence d'une difference. Le choix superieur teste si la moyenne est strictement superieure \n Le choix inferieur teste l'existence d'une difference strictement inferieure" , envir=.dico) + assign("txt_numeric_variables" , "Variables numĂ©riques" , envir=.dico) + assign("txt_select_language" , "Choisir la langue" , envir=.dico) + assign("txt_dot_adjusted" , ".ajustee" , envir=.dico) + assign("txt_bca_inferior_limit" , "Bca lim inf" , envir=.dico) + assign("txt_bca_inferior_limit" , "Bca.lim.inf" , envir=.dico) + assign("txt_bca_superior_limit" , " Bca.lim.sup" , envir=.dico) + assign("txt_bca_superior_limit" , "Bca lim sup" , envir=.dico) + assign("txt_bca_superior_limit" , "Bca.lim.sup" , envir=.dico) + assign("txt_centered_dot_reduced" , "centrer.reduite" , envir=.dico) + assign("txt_chi_dot_squared" , "chi.2" , envir=.dico) + assign("txt_chi_dot_squared_model" , "chi.2.modele" , envir=.dico) + assign("txt_chi_dot_squared" , "chi.carre" , envir=.dico) + assign("txt_chi_dot_squared" , "chi.deux" , envir=.dico) + assign("txt_chi_dot_squared_adjustment" , "chi.deux d'ajustement" , envir=.dico) + assign("txt_pairwise_comparison" , "comparaison 2 a 2" , envir=.dico) + assign("txt_continuous" , "continues" , envir=.dico) + assign("txt_greenhouse_geisser_huynn_feldt_correction" , "Correction : Greenhouse-Geisser & Hyunh-Feldt" , envir=.dico) + assign("txt_df" , "ddl" , envir=.dico) + assign("txt_df1" , "ddl1" , envir=.dico) + assign("txt_df_parenthesis_1" , "Ddl(1)" , envir=.dico) + assign("txt_df2" , "ddl2" , envir=.dico) + assign("txt_df_parenthesis_2" , "Ddl(2)" , envir=.dico) + assign("txt_df_denom" , "ddl.denom" , envir=.dico) + assign("txt_df_parenthesis_denom" , "Ddl (dnom)" , envir=.dico) + assign("txt_df_effect" , "ddl.effet" , envir=.dico) + assign("txt_df_num" , "ddl.num" , envir=.dico) + assign("txt_df_parenthesis_num" , "Ddl (num)" , envir=.dico) + assign("txt_df_predictor" , "ddl predicteur" , envir=.dico) + assign("txt_df_residual" , "ddl.resid" , envir=.dico) + assign("txt_df_residuals" , "ddl.residuels" , envir=.dico) + assign("txt_delta_r_squared" , "Delta R.deux" , envir=.dico) + assign("txt_error" , "Erreur" , envir=.dico) + assign("txt_error_BP" , "Erreur.BP" , envir=.dico) + assign("txt_error_spearman" , "Erreur.Spearman" , envir=.dico) + assign("txt_error_dot_standard_short" , "erreur.st" , envir=.dico) + assign("txt_error_dot_standard" , "erreur.standard" , envir=.dico) + assign("txt_error_dot_standard" , "Erreur.standard" , envir=.dico) + assign("txt_space" , "espace" , envir=.dico) + assign("txt_estimator" , "estimateur" , envir=.dico) + assign("txt_global_model_estimate" , "Estimation du modele global" , envir=.dico) + assign("txt_hf_p_value" , "HF.valeur.p" , envir=.dico) + assign("txt_ci_inferior" , "IC Inf" , envir=.dico) + assign("txt_ci_inferior_limit" , "IC lim inf" , envir=.dico) + assign("txt_ci_superior_limit" , "IC lim sup" , envir=.dico) + assign("txt_ci_superior" , "IC Sup" , envir=.dico) + assign("txt_large" , "large" , envir=.dico) + assign("txt_large_half" , "large - 0.5" , envir=.dico) + assign("txt_inferior_limit" , "lim.inf" , envir=.dico) + assign("txt_ci_inferior_limit_dot" , "lim.inf.IC" , envir=.dico) + assign("txt_ci_inferior_limit_dot" , "Lim.inf.IC" , envir=.dico) + assign("txt_ci_superior_limit" , "lim.sup" , envir=.dico) + assign("txt_ci_superior_limit_dot" , "lim.sup.IC" , envir=.dico) + assign("txt_ci_superior_limit_dot" , "Lim.sup.IC" , envir=.dico) + assign("txt_r_squared_matrix" , "matrice des r.deux" , envir=.dico) + assign("txt_truncated_m" , "M.tronquee" , envir=.dico) + assign("txt_multiplied_by" , "multiplie.par" , envir=.dico) + assign("txt_dot_cleaned" , ".nettoyees" , envir=.dico) + assign("txt_cleaned" , "nettoyees" , envir=.dico) + assign("txt_bootstrap_dot_number" , "Nombre.bootstrap" , envir=.dico) + assign("txt_odd_ratio_dot" , "Odd.ratio" , envir=.dico) + assign("desc_install_bad_packages" , "Package.mal.installes" , envir=.dico) + assign("desc_install_correct_packages" , "packages.installes.correctement" , envir=.dico) + assign("txt_critical_p_corrected" , "p.critique.corrigee" , envir=.dico) + assign("txt_percentile_inferior_limit_dot" , "Percentile.lim.inf" , envir=.dico) + assign("txt_percentile_superior_limit_dot" , "Percentile.lim.sup" , envir=.dico) + assign("txt_percentage_removed_obs" , "Pourcentage.obs.retirees" , envir=.dico) + assign("txt_percent_removed_obs" , "Pourcent.obs.retirees" , envir=.dico) + assign("txt_r_dot_square" , "r.carre" , envir=.dico) + assign("txt_r_square" , "R carre" , envir=.dico) + assign("txt_r_dot_square" , "R.carre" , envir=.dico) + assign("txt_r_dot_two" , "r.deux" , envir=.dico) + assign("txt_r_dot_two" , "R.deux" , envir=.dico) + assign("txt_r_dot_two_adjusted" , "R.deux.aj" , envir=.dico) + assign("txt_log_regression_dot" , "Regressions.logistique" , envir=.dico) + assign("txt_multiple_regressions_dot" , "regressions.multiples" , envir=.dico) + assign("txt_multiple_regressions_dot" , "Regressions.multiples" , envir=.dico) + assign("txt_rho_dot_square" , "rho.deux" , envir=.dico) + assign("txt_critical_dot_threshold" , "seuil.critique" , envir=.dico) + assign("txt_critical_dot_threshold" , "Seuil.critique" , envir=.dico) + assign("txt_spearman_df" , "Spearman.ddl" , envir=.dico) + assign("txt_specificity" , "specifite" , envir=.dico) + assign("txt_ultrawide" , "ultra large" , envir=.dico) + assign("txt_ultrawide" , "ultralarge" , envir=.dico) + assign("txt_ultrawide_val" , "ultra large - 0.707" , envir=.dico) + assign("txt_absolute_dot_val" , "valeur.absolue." , envir=.dico) + assign("txt_contrast_dot_val" , "Valeur.contraste" , envir=.dico) + assign("txt_critical_dot_val" , "Valeur.critique" , envir=.dico) + assign("txt_p_dot_val" , "valeur.p" , envir=.dico) + assign("txt_p_dot_val_lilliefors" , "valeur.p Llfrs" , envir=.dico) + assign("txt_p_dot_val_sw" , "valeur.p SW" , envir=.dico) + assign("txt_test_dot_val" , "Valeur.test" , envir=.dico) + assign("txt_z_dot_val" , "valeur.Z" , envir=.dico) + assign("txt_value" , "value" , envir=.dico) + assign("txt_vector_length_zero" , "vector of length zero" , envir=.dico) + assign("txt_kendall_w" , "W.de.Kendall" , envir=.dico) + assign("txt_synthesis" , "Synthèse" , envir=.dico) + assign("txt_truncated_mean_0_2" , "Test sur la moyenne tronquĂ©e Ă  0.2" , envir=.dico) + assign("txt_cramer_v_square" , "V.carre" , envir=.dico) + assign("txt_effect_size_dot" , "Taille.effet" , envir=.dico) + assign("txt_gg_p_value" , "GG.valeur.p" , envir=.dico) + assign("txt_var_explained_dot" , "Var.expliquee" , envir=.dico) + assign("txt_V_sq_" , "V.carre" , envir=.dico) +} \ No newline at end of file diff --git a/R/maths.R b/R/maths.R index 2edf8d8..5bdb954 100644 --- a/R/maths.R +++ b/R/maths.R @@ -11,14 +11,14 @@ maths <- if(length(data1)==0) {return(preprocess())} data1[[1]]->nom1 data1[[2]]->data - if(info=="TRUE") writeLines(ask_which_mathematical_operation) - dlgList(c(txt_additions,txt_multiplication, txt_division, txt_substraction,txt_col_mean, txt_exponant_or_root, - txt_logarithm, txt_exponential,txt_absolute_value,txt_complex_model), preselect=txt_additions, multiple = FALSE, title=ask_which_operation)$res->choix + if(info=="TRUE") writeLines(.dico[["ask_which_mathematical_operation"]]) + dlgList(c(.dico[["txt_additions"]],.dico[["txt_multiplication"]], .dico[["txt_division"]], .dico[["txt_substraction"]],.dico[["txt_col_mean"]], .dico[["txt_exponant_or_root"]], + .dico[["txt_logarithm"]], .dico[["txt_exponential"]],.dico[["txt_absolute_value"]],.dico[["txt_complex_model"]]), preselect=.dico[["txt_additions"]], multiple = FALSE, title=.dico[["ask_which_operation"]])$res->choix if(length(choix)==0) return(preprocess()) variable<-function(multiple=TRUE){ - X<-dlgList(c(names(data), txt_cancel), multiple = multiple, title=txt_variables)$res - if(any(sapply(data[,X], class)=="factor")) {writeLines(desc_at_least_one_var_is_not_num) + X<-dlgList(c(names(data), .dico[["txt_cancel"]]), multiple = multiple, title=.dico[["txt_variables"]])$res + if(any(sapply(data[,X], class)=="factor")) {writeLines(.dico[["desc_at_least_one_var_is_not_num"]]) writeLines(str(data)) return(maths())} return(X)} @@ -26,10 +26,10 @@ maths <- valeur<-function(info=TRUE, out=NULL){ # info : logique pour determiner les informations relatives aux parametres doivent s'afficher dans la console # out : valeur renvoyee si valeur non numerique ou annulation - if(info) writeLines(ask_value_for_operation) + if(info) writeLines(.dico[["ask_value_for_operation"]]) msg<-"no" while(msg=="no" ){ - valeur1 <- dlgInput(ask_which_value_for_operation, out)$res + valeur1 <- dlgInput(.dico[["ask_which_value_for_operation"]], out)$res if(length(valeur1)!=0){ strsplit(valeur1, ":")->valeur1 if(class(valeur1)=="list") { tail(valeur1[[1]],n=1)->valeur1} @@ -37,83 +37,83 @@ maths <- if(valeur1=="e") valeur1<-exp(1) as.numeric(valeur1)->valeur1 msg<-"yes"} else return(out) - if(is.na(valeur1) ) { dlgMessage(ask_cancel_entered_value_not_num, "yesno")$res->msg + if(is.na(valeur1) ) { dlgMessage(.dico[["ask_cancel_entered_value_not_num"]], "yesno")$res->msg if(msg=="yes") return(out)} } return(valeur1) } nom<-function(data,info, nom1){ - if(info=="TRUE") writeLines(ask_new_variable_name) - variable<-dlgInput(ask_variable_name,"nouvelle.variable")$res + if(info=="TRUE") writeLines(.dico[["ask_new_variable_name"]]) + variable<-dlgInput(.dico[["ask_variable_name"]],"nouvelle.variable")$res if(length(variable)==0) variable<-"nouvelle.variable" strsplit(variable, ":")->variable tail(variable[[1]],n=1)->variable if(grepl("[^[:alnum:]]", variable)) { - writeLines(desc_unauthorized_char_replaced) + writeLines(.dico[["desc_unauthorized_char_replaced"]]) gsub("[^[:alnum:]]", ".", variable)->variable } names(data)<-c(names(data)[1:(length(data)-1)], variable) assign(nom1, data, envir=.GlobalEnv) - Resultats<-paste(desc_the_variable_upper, variable, desc_has_been_added_to, nom1) + Resultats<-paste(.dico[["desc_the_variable_upper"]], variable, .dico[["desc_has_been_added_to"]], nom1) return(Resultats)} - if(choix==txt_additions) { - if(info=="TRUE") writeLines(desc_if_you_select_both_operations_value_will_be_added_to_chose_cols) - dlgList(c(txt_add_of_cols,txt_add_of_specific_value), preselect=txt_add_of_cols, multiple = TRUE, title=ask_which_operation)$res->choix2 + if(choix==.dico[["txt_additions"]]) { + if(info=="TRUE") writeLines(.dico[["desc_if_you_select_both_operations_value_will_be_added_to_chose_cols"]]) + dlgList(c(.dico[["txt_add_of_cols"]],.dico[["txt_add_of_specific_value"]]), preselect=.dico[["txt_add_of_cols"]], multiple = TRUE, title=.dico[["ask_which_operation"]])$res->choix2 if(length(choix2)==0) return(maths()) - if(any(choix2== txt_add_of_specific_value)){ + if(any(choix2== .dico[["txt_add_of_specific_value"]])){ variable()->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) valeur(info=info)->valeur1 if(is.null(valeur1)) return(maths()) data.frame(data, data[,X]+valeur1)->data - if(valeur1>0) names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, txt_plus, valeur1, sep=".") else names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, txt_minus, abs(valeur1), sep=".") + if(valeur1>0) names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, .dico[["txt_plus"]], valeur1, sep=".") else names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, .dico[["txt_minus"]], abs(valeur1), sep=".") assign(nom1, data, envir=.GlobalEnv) - paste(valeur1, desc_has_been_added_to_variable, X)->Resultats + paste(valeur1, .dico[["desc_has_been_added_to_variable"]], X)->Resultats } - if(any(choix2== txt_add_of_cols)) { - if(info=="TRUE") writeLines(ask_variables_to_add) + if(any(choix2== .dico[["txt_add_of_cols"]])) { + if(info=="TRUE") writeLines(.dico[["ask_variables_to_add"]]) variable()->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) X->X1 X2<-X1[1] X1[-1]->X1 while(length(X1)!=0){paste(X2,"+",X1[1])->X2 X1[-1]->X1} rowSums(data[,X])->data$nouvelle_variable - if(info=="TRUE") writeLines(desc_you_can_still_add) + if(info=="TRUE") writeLines(.dico[["desc_you_can_still_add"]]) valeur(info=info, out=0)->valeur1 if(valeur1!=0) {data$nouvelle_variable+valeur1->data$nouvelle_variable paste(X2, "+", valeur1)->X2} - writeLines(paste(desc_you_did_this_operation, X2)) - writeLines(ask_add_value_to_total) + writeLines(paste(.dico[["desc_you_did_this_operation"]], X2)) + writeLines(.dico[["ask_add_value_to_total"]]) nom(data=data, info=info,nom1=nom1)->Resultats } } - if(choix==txt_multiplication){ - if(info=="TRUE") writeLines(desc_if_you_select_both_operations_value_will_be_multiplied_to_chose_cols) - dlgList(c(txt_cols_multiplication,txt_specific_val_multiplication), preselect=txt_cols_multiplication, multiple = TRUE, title=ask_which_operation)$res->choix2 + if(choix==.dico[["txt_multiplication"]]){ + if(info=="TRUE") writeLines(.dico[["desc_if_you_select_both_operations_value_will_be_multiplied_to_chose_cols"]]) + dlgList(c(.dico[["txt_cols_multiplication"]],.dico[["txt_specific_val_multiplication"]]), preselect=.dico[["txt_cols_multiplication"]], multiple = TRUE, title=.dico[["ask_which_operation"]])$res->choix2 if(length(choix2)==0) return(maths()) - if(any(choix2== txt_specific_val_multiplication)){ - if(info=="TRUE") writeLines(ask_variables_to_multiply) + if(any(choix2== .dico[["txt_specific_val_multiplication"]])){ + if(info=="TRUE") writeLines(.dico[["ask_variables_to_multiply"]]) variable()->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) valeur(info=info, out=NULL)->valeur1 if(is.null(valeur1)) return(maths()) data.frame(data, data[,X]*valeur1)->data - names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, txt_multiplied_by, valeur1, sep=".") + names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, .dico[["txt_multiplied_by"]], valeur1, sep=".") assign(nom1, data, envir=.GlobalEnv) - paste(valeur1, desc_has_multiplied_variables, X)->Resultats + paste(valeur1, .dico[["desc_has_multiplied_variables"]], X)->Resultats } - if(any(choix2== txt_cols_multiplication)) { + if(any(choix2== .dico[["txt_cols_multiplication"]])) { variable()->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) X->X1 X2<-X1[1] @@ -124,65 +124,65 @@ maths <- for(i in 1:(length(X)-1)) nouvelle*data[,X[i+1]]->nouvelle data.frame(data, nouvelle)->data - if(info=="TRUE") writeLines(desc_you_can_still_multiply) + if(info=="TRUE") writeLines(.dico[["desc_you_can_still_multiply"]]) valeur(info=info, out=1)->valeur1 if(valeur1!=1) {data$nouvelle*valeur1->data$nouvelle paste(X2, "*", valeur1)->X2} - writeLines(paste(desc_you_did_this_operation, X2)) + writeLines(paste(.dico[["desc_you_did_this_operation"]], X2)) nom(data=data, info=info,nom1=nom1)->Resultats } } - if(choix==txt_division){ - if(info=="TRUE") writeLines(ask_numerator_variable_or_value) - numer<-dlgList(c(txt_value, txt_variable), multiple = FALSE, title=txt_numerator)$res + if(choix==.dico[["txt_division"]]){ + if(info=="TRUE") writeLines(.dico[["ask_numerator_variable_or_value"]]) + numer<-dlgList(c(.dico[["txt_value"]], .dico[["txt_variable"]]), multiple = FALSE, title=.dico[["txt_numerator"]])$res if(length(numer)==0) return(maths()) - if(numer==txt_value) valeur(info=info, out=1)->X else{ - if(info=="TRUE") writeLines(ask_numerator_variable) + if(numer==.dico[["txt_value"]]) valeur(info=info, out=1)->X else{ + if(info=="TRUE") writeLines(.dico[["ask_numerator_variable"]]) variable(multiple=FALSE)->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) data[,X]->X } - if(info=="TRUE") writeLines(ask_denominator_variable_or_value) - denom<-dlgList(c(txt_value, txt_variable), multiple = FALSE, title=txt_denominator)$res + if(info=="TRUE") writeLines(.dico[["ask_denominator_variable_or_value"]]) + denom<-dlgList(c(.dico[["txt_value"]], .dico[["txt_variable"]]), multiple = FALSE, title=.dico[["txt_denominator"]])$res if(length(denom)==0) return(maths()) - if(denom==txt_value) valeur(info=info, out=1)->Y else{ - if(info=="TRUE") writeLines(ask_denominator_variable) + if(denom==.dico[["txt_value"]]) valeur(info=info, out=1)->Y else{ + if(info=="TRUE") writeLines(.dico[["ask_denominator_variable"]]) variable(multiple=FALSE)->Y - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) data[,Y]->Y - if(any(Y)==0) writeLines(desc_at_least_one_denom_is_zero) + if(any(Y)==0) writeLines(.dico[["desc_at_least_one_denom_is_zero"]]) } X/Y->data$nouvelle_variable nom(data=data, info=info,nom1=nom1)->Resultats } - if(choix==txt_substraction) { - if(info=="TRUE") writeLines(ask_chose_values_on_left_of_minus_symbol) - if(info=="TRUE") writeLines(ask_positive_val_variable_or_value) - numer<-dlgList(c(txt_value, txt_variable), multiple = FALSE, title=txt_positive_values)$res + if(choix==.dico[["txt_substraction"]]) { + if(info=="TRUE") writeLines(.dico[["ask_chose_values_on_left_of_minus_symbol"]]) + if(info=="TRUE") writeLines(.dico[["ask_positive_val_variable_or_value"]]) + numer<-dlgList(c(.dico[["txt_value"]], .dico[["txt_variable"]]), multiple = FALSE, title=.dico[["txt_positive_values"]])$res if(length(numer)==0) return(maths()) - if(numer==txt_value) valeur(info=info, out=0)->X else{ + if(numer==.dico[["txt_value"]]) valeur(info=info, out=0)->X else{ if(info=="TRUE") writeLines("Veuillez selectionner la -les- variable(s) a gauche du symbole *moins*") variable(multiple=TRUE)->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) data[,X]->X1 data.frame(X1)->X1 } - if(info=="TRUE") writeLines(ask_minus_left_hand_variables) - denom<-dlgList(c(txt_value, txt_variable), multiple = FALSE, title=txt_negative_values)$res + if(info=="TRUE") writeLines(.dico[["ask_minus_left_hand_variables"]]) + denom<-dlgList(c(.dico[["txt_value"]], .dico[["txt_variable"]]), multiple = FALSE, title=.dico[["txt_negative_values"]])$res if(length(denom)==0) return(maths()) - if(denom==txt_value) valeur(info=info, out=0)->Y else{ - if(info=="TRUE") writeLines(ask_minus_right_hand_variables) + if(denom==.dico[["txt_value"]]) valeur(info=info, out=0)->Y else{ + if(info=="TRUE") writeLines(.dico[["ask_minus_right_hand_variables"]]) Y<-NULL while(is.null(Y)){ variable(multiple=TRUE)->Y - if(length(Y)==0|| any(Y==txt_cancel)) return(maths()) + if(length(Y)==0|| any(Y==.dico[["txt_cancel"]])) return(maths()) data[,Y]->Y1 data.frame(Y1)->Y1 if(length(X1)!=1 & length(Y1)!=1 & length(X1)!=length(Y1)) { - writeLines(desc_one_or_same_number_cols_on_both_sides_only) + writeLines(.dico[["desc_one_or_same_number_cols_on_both_sides_only"]]) Y<-NULL} else Y<-Y } } @@ -191,73 +191,73 @@ maths <- data<-data.frame(data, new.var) assign(nom1, data, envir=.GlobalEnv) #nom(data=data, info=info,nom1=nom1)->Resultats - Resultats<-desc_operation_succesful + Resultats<-.dico[["desc_operation_succesful"]] } - if(choix==txt_col_mean) { - if(info=="TRUE") writeLines(ask_variables_to_mean) + if(choix==.dico[["txt_col_mean"]]) { + if(info=="TRUE") writeLines(.dico[["ask_variables_to_mean"]]) X<-variable() - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) rowMeans(data[,X])->data$nouvelle_variable nom(data=data, info=info,nom1=nom1)->Resultats } - if(choix== txt_exponant_or_root){ - if(info=="TRUE") writeLines(ask_variables_to_exp) + if(choix== .dico[["txt_exponant_or_root"]]){ + if(info=="TRUE") writeLines(.dico[["ask_variables_to_exp"]]) variable(multiple=TRUE)->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) - if(info=="TRUE") writeLines(ask_specify_exponant_value) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) + if(info=="TRUE") writeLines(.dico[["ask_specify_exponant_value"]]) valeur(info=info)->Y - if(class(Y)!="numeric") {writeLines(desc_entered_value_not_num) + if(class(Y)!="numeric") {writeLines(.dico[["desc_entered_value_not_num"]]) return(maths())} data.frame(data, data[,X]^Y)->data - names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, txt_exponant, Y, sep=".") + names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(X, .dico[["txt_exponant"]], Y, sep=".") assign(nom1, data, envir=.GlobalEnv) - paste(desc_the_variable_lower, X, desc_has_been_put_to_the_power_of, Y)->Resultats + paste(.dico[["desc_the_variable_lower"]], X, .dico[["desc_has_been_put_to_the_power_of"]], Y)->Resultats } - if(choix== txt_logarithm){ - if(info=="TRUE") writeLines(ask_variables_to_log) + if(choix== .dico[["txt_logarithm"]]){ + if(info=="TRUE") writeLines(.dico[["ask_variables_to_log"]]) variable(multiple=TRUE)->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) - if(info=="TRUE") writeLines(ask_log_base) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) + if(info=="TRUE") writeLines(.dico[["ask_log_base"]]) valeur(info=info)->Y - if(class(Y)!="numeric") {writeLines(desc_entered_value_not_num) + if(class(Y)!="numeric") {writeLines(.dico[["desc_entered_value_not_num"]]) return(maths())} - if(Y<0) {writeLines(desc_neg_log_impossible) + if(Y<0) {writeLines(.dico[["desc_neg_log_impossible"]]) return(maths()) } data.frame(data, log(data[,X], base=Y))->data names(data)[(length(data)-(length(X)-1)):length(data)]<-paste("log.", X, sep=".") assign(nom1, data, envir=.GlobalEnv) - paste(desc_log_with_base, Y, desc_has_been_applied_to_variable, X)->Resultats + paste(.dico[["desc_log_with_base"]], Y, .dico[["desc_has_been_applied_to_variable"]], X)->Resultats } - if(choix== txt_exponential){ - if(info=="TRUE") writeLines(ask_variables_used_for_exponential) + if(choix== .dico[["txt_exponential"]]){ + if(info=="TRUE") writeLines(.dico[["ask_variables_used_for_exponential"]]) variable(multiple=TRUE)->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) data.frame(data, exp(data[,X]))->data names(data)[(length(data)-(length(X)-1)):length(data)]<-paste("exp.", X, sep=".") assign(nom1, data, envir=.GlobalEnv) - paste(desc_exponential_has_been_applied_to_var, X)->Resultats + paste(.dico[["desc_exponential_has_been_applied_to_var"]], X)->Resultats } - if(choix== txt_absolute_value){ - if(info=="TRUE") writeLines(ask_variables_to_abs) + if(choix== .dico[["txt_absolute_value"]]){ + if(info=="TRUE") writeLines(.dico[["ask_variables_to_abs"]]) variable(multiple=TRUE)->X - if(length(X)==0|| any(X==txt_cancel)) return(maths()) + if(length(X)==0|| any(X==.dico[["txt_cancel"]])) return(maths()) data.frame(data, abs(data[,X]))->data - names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(txt_absolute_dot_val, X, sep=".") + names(data)[(length(data)-(length(X)-1)):length(data)]<-paste(.dico[["txt_absolute_dot_val"]], X, sep=".") assign(nom1, data, envir=.GlobalEnv) - paste(desc_abs_val_applied_to_var, X)->Resultats + paste(.dico[["desc_abs_val_applied_to_var"]], X)->Resultats } - if(choix== txt_complex_model){ - writeLines(desc_expression_must_be_correct_example) + if(choix== .dico[["txt_complex_model"]]){ + writeLines(.dico[["desc_expression_must_be_correct_example"]]) print(paste(names(data)[1],"^2+5"), quote=FALSE) print(names(data)) - valeur1 <- dlgInput(ask_model)$res + valeur1 <- dlgInput(.dico[["ask_model"]])$res if(length(valeur1)==0) return(maths()) strsplit(valeur1, ":")->valeur1 tail(valeur1[[1]],n=1)->valeur1 try(eval(parse(text=valeur1), envir=data), silent=TRUE)->nouvelle - if(class(nouvelle)=='try-error') {writeLines(desc_model_contains_error) + if(class(nouvelle)=='try-error') {writeLines(.dico[["desc_model_contains_error"]]) return(maths())} else nouvelle->data$nouvelle nom(data=data,info=info, nom1=nom1)->Resultats diff --git a/R/preprocess.R b/R/preprocess.R index fa71ebb..165b5c4 100644 --- a/R/preprocess.R +++ b/R/preprocess.R @@ -1,19 +1,19 @@ preprocess <- function(){ - choix<-dlgList(c(txt_ranks_upper, txt_imput_missing_values, - txt_select_obs,txt_select_variables,txt_center_or_center_reduce,txt_order, - txt_mathematical_operations_on_variables,txt_dynamic_crossed_table, - txt_long_or_large_format), multiple = F, preselect=txt_ranks_lower, title=ask_what_to_do)$res + choix<-dlgList(c(.dico[["txt_ranks_upper"]], .dico[["txt_imput_missing_values"]], + .dico[["txt_select_obs"]],.dico[["txt_select_variables"]],.dico[["txt_center_or_center_reduce"]],.dico[["txt_order"]], + .dico[["txt_mathematical_operations_on_variables"]],.dico[["txt_dynamic_crossed_table"]], + .dico[["txt_long_or_large_format"]]), multiple = F, preselect=.dico[["txt_ranks_lower"]], title=.dico[["ask_what_to_do"]])$res if(length(choix)==0) return(easieR()) - if (choix==txt_ranks_upper) ez.rank()->Resultats - if (choix==txt_imput_missing_values) ez.imp()->Resultats - if (choix==txt_select_obs) selectionO()->Resultats - if (choix==txt_select_variables) SelectionV()->Resultats - if (choix==txt_center_or_center_reduce) Centrer.red()->Resultats - if (choix==txt_order) trier()->Resultats - if (choix==txt_mathematical_operations_on_variables) maths()->Resultats - if (choix==txt_long_or_large_format) ez.reshape()->Resultats - if (choix==txt_dynamic_crossed_table) { + if (choix==.dico[["txt_ranks_upper"]]) ez.rank()->Resultats + if (choix==.dico[["txt_imput_missing_values"]]) ez.imp()->Resultats + if (choix==.dico[["txt_select_obs"]]) selectionO()->Resultats + if (choix==.dico[["txt_select_variables"]]) SelectionV()->Resultats + if (choix==.dico[["txt_center_or_center_reduce"]]) Centrer.red()->Resultats + if (choix==.dico[["txt_order"]]) trier()->Resultats + if (choix==.dico[["txt_mathematical_operations_on_variables"]]) maths()->Resultats + if (choix==.dico[["txt_long_or_large_format"]]) ez.reshape()->Resultats + if (choix==.dico[["txt_dynamic_crossed_table"]]) { try(library('rpivotTable'), silent=T)->test2 if(class(test2)== 'try-error') return(ez.install()) return( rpivotTable(choix.data(nom=F))) diff --git a/R/regressions.R b/R/regressions.R index 7548ef4..44eaf18 100644 --- a/R/regressions.R +++ b/R/regressions.R @@ -1,55 +1,55 @@ regressions <- - function(data=NULL, modele=NULL, Y=NULL, X_a=NULL, X_i=NULL, outlier=txt_complete_dataset, inf=F, CV=F, select.m="none", method="p", step=NULL, group=NULL, criteria=0.15 , scale=T, dial=T, info=T, + function(data=NULL, modele=NULL, Y=NULL, X_a=NULL, X_i=NULL, outlier=.dico[["txt_complete_dataset"]], inf=F, CV=F, select.m="none", method="p", step=NULL, group=NULL, criteria=0.15 , scale=T, dial=T, info=T, sauvegarde=F, n.boot=NULL, param="param", rscale=0.353, html=TRUE){ - - - + + + regressions.in<-function(data=NULL, modele=NULL, Y=NULL, X_a=NULL, X_i=NULL, outlier=NULL, inf=F, CV=F, select.m="none", method="p", step=NULL, group=NULL, criteria=NULL , scale=T, dial=T, info=T, sauvegarde=F, n.boot=NULL, param=NULL, rscale=0.353){ options (warn=-1) Resultats<-list() if(is.null(data) | is.null(modele)) {dial<-TRUE}else dial<-F - + data<-choix.data(data=data, info=info, nom=T) if(length(data)==0) return(NULL) nom<-data[[1]] data<-data[[2]] - - + + if(dial && is.null(modele)){ - if(info) writeLines(ask_chose_relation_between_vars_regressions_log) - dlgList(c(txt_additive_effects, txt_interaction_effects, txt_specify_model), preselect=txt_regressions, multiple = TRUE, title=ask_which_regression_type)$res->link + if(info) writeLines(.dico[["ask_chose_relation_between_vars_regressions_log"]]) + dlgList(c(.dico[["txt_additive_effects"]], .dico[["txt_interaction_effects"]], .dico[["txt_specify_model"]]), preselect=.dico[["txt_regressions"]], multiple = TRUE, title=.dico[["ask_which_regression_type"]])$res->link if(length(link)==0) return(NULL) } else link<-"none" - + if(length(Y)>1){ - msgBox(desc_only_one_dependant_variable_alllowed) + msgBox(.dico[["desc_only_one_dependant_variable_alllowed"]]) Y<-NULL } - if(any(link %in% c(txt_additive_effects, txt_interaction_effects))){ - msg3<-ask_chose_dependant_variable - Y<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=FALSE, title=txt_dependant_variable, out=NULL) + if(any(link %in% c(.dico[["txt_additive_effects"]], .dico[["txt_interaction_effects"]]))){ + msg3<-.dico[["ask_chose_dependant_variable"]] + Y<-.var.type(X=Y, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=FALSE, title=.dico[["txt_dependant_variable"]], out=NULL) if(is.null(Y)) { regressions.in()->Resultats return(Resultats)} data<-Y$data Y<-Y$X - - if(any(link==txt_additive_effects) || !is.null(X_a)| any(X_a %in% names(data)==F)) { - msg3<-ask_chose_dependant_variable - X_a<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=txt_additive_model_variables, out=Y) + + if(any(link==.dico[["txt_additive_effects"]]) || !is.null(X_a)| any(X_a %in% names(data)==F)) { + msg3<-.dico[["ask_chose_dependant_variable"]] + X_a<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=.dico[["txt_additive_model_variables"]], out=Y) if(is.null(X_a)) { regressions.in()->Resultats return(Resultats)} data<-X_a$data X_a<-X_a$X - + }else X_a<-NULL - - if(any(link==txt_interaction_effects) || !is.null(X_i) & (length(X_i)<2 | any(X_i %in% names(data)==F))) { - msg3<-ask_chose_interaction_model_predictors + + if(any(link==.dico[["txt_interaction_effects"]]) || !is.null(X_i) & (length(X_i)<2 | any(X_i %in% names(data)==F))) { + msg3<-.dico[["ask_chose_interaction_model_predictors"]] X_i<-c() while(length(X_i)<2){ - X_i<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=txt_interactive_model_variables, out=c(X_a,Y)) + X_i<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=.dico[["txt_interactive_model_variables"]], out=c(X_a,Y)) if(is.null(X_i)) { regressions.in()->Resultats return(Resultats)} @@ -57,74 +57,74 @@ regressions <- X_i<-X_i$X } }else X_i<-NULL - - - + + + paste0(Y," ~ ")->modele if(!is.null(X_a )) { X_a.mod<-X_a[1] if(length(X_a)>1) for(i in 2 : length(X_a)) paste0(X_a.mod, "+", X_a[i])-> X_a.mod } else X_a.mod<-NULL - + if(!is.null(X_i)){ X_i.mod<-X_i[1] if(length(X_i)>1) for(i in 2 : length(X_i)) paste0(X_i.mod, "*", X_i[i])-> X_i.mod } else X_i.mod<-NULL - + if(!is.null(X_a.mod) & !is.null(X_i.mod)) { paste0(modele, X_a.mod, "+", X_i.mod)->modele } else paste0(modele, X_a.mod, X_i.mod)->modele - + } - - if(any(link==txt_specify_model)) { + + if(any(link==.dico[["txt_specify_model"]])) { if(is.null(modele)) modele<-" " modele<-fix(modele)} modele<-as.formula(modele) variables<-terms(modele) variables<-as.character( attributes(variables)$variables)[-1] - - + + model.test<-try(model.matrix(modele, data), silent=T) if(any(class(model.test)=='try-error')) { - msgBox(desc_incorrect_model) + msgBox(.dico[["desc_incorrect_model"]]) return(regressions.in()) } - - + + data[complete.cases(data[,variables]),]->data - msg.options1<-desc_param_test_is_classical_reg_robusts_are_m_estimator - + msg.options1<-.dico[["desc_param_test_is_classical_reg_robusts_are_m_estimator"]] + options<-.ez.options(options=c('choix',"outlier"), n.boot=n.boot,param=T, non.param=F, robust=T, Bayes=T, msg.options1=msg.options1, msg.options2=msg.options2, info=info, dial=dial, choix=param,sauvegarde=sauvegarde, outlier=outlier, rscale=rscale) if(is.null(options)) return(regressions.in()) - + reg.options<- .regressions.options(data=data, modele=modele, CV=CV, inf=inf, select.m=select.m, method=method, criteria=criteria, step=step, group=group, scale=scale, dial=dial,info=info) if(is.null(reg.options)) return(regressions.in()) - - + + Resultats$data<-data Resultats$nom<-nom Resultats$modele<-modele Resultats$options<-options Resultats$reg.options<-reg.options return(Resultats) - + } - + regressions.out<-function(dtrgeasieR=NULL, modele=NULL, VC=F, select.m="none", method=NULL, step=NULL, group=NULL, criteria=NULL , scale=T, sauvegarde=F, n.boot=NULL, param=NULL, rscale=0.353){ - + Resultats<-list() dtrgeasieR<<-dtrgeasieR variables<-terms(as.formula(modele)) variables<-as.character( attributes(variables)$variables)[-1] pred<-attributes(terms(as.formula(modele)))$term.labels - - Resultats[[txt_descriptive_statistics]]<-.stat.desc.out(X=variables, groupes=NULL, data=dtrgeasieR, tr=.1, type=3, plot=T) - - if(scale==T || scale==txt_center) { - Resultats$info<-desc_centered_data_schielzeth_recommandations + + Resultats[[.dico[["txt_descriptive_statistics"]]]]<-.stat.desc.out(X=variables, groupes=NULL, data=dtrgeasieR, tr=.1, type=3, plot=T) + + if(scale==T || scale==.dico[["txt_center"]]) { + Resultats$info<-.dico[["desc_centered_data_schielzeth_recommandations"]] if(length(pred)>1) { which(!sapply(dtrgeasieR[,pred[which(pred %in% variables)]],class)%in%c("factor", "character"))->centre centre<-pred[centre]}else{centre<-NULL} if(!is.null(centre)){ @@ -132,11 +132,11 @@ regressions <- sapply(X=dtrgeasieR[,centre], fun<-function(X){X-mean(X, na.rm=T)})->dtrgeasieR[,centre] } } - + } - - - + + + mod<-list() modele1<-as.formula(paste0(variables[1], "~", pred[1])) lm( modele1,na.action=na.exclude, data=dtrgeasieR)->lm.r1 @@ -147,112 +147,112 @@ regressions <- } assign("lm.r1",lm.r1, env= .GlobalEnv) resid(lm.r1)->dtrgeasieR$residu - Resultats[[txt_normality_tests]]<-.normalite(data=dtrgeasieR, X='residu', Y=NULL) + Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=dtrgeasieR, X='residu', Y=NULL) if(length(pred)>1) { cont<-variables[which(!sapply(dtrgeasieR[,variables],class)%in%c("factor","character"))] - Resultats[[txt_multivariate_normality]]<-.normalite(data=dtrgeasieR, X=cont, Y=NULL) + Resultats[[.dico[["txt_multivariate_normality"]]]]<-.normalite(data=dtrgeasieR, X=cont, Y=NULL) ols_plot_resid_fit(lm.r1) FIV<-ols_coll_diag(lm.r1) # calcul du facteur d inflation de la variance - names(FIV$vif_t)<-c(txt_variables, txt_tolerance, txt_VIF) - Resultats[[txt_multicolinearity_tests]]<-FIV$vif_t - - - - + names(FIV$vif_t)<-c(.dico[["txt_variables"]], .dico[["txt_tolerance"]], .dico[["txt_VIF"]]) + Resultats[[.dico[["txt_multicolinearity_tests"]]]]<-FIV$vif_t + + + + if(any(FIV$`Test de multicolinearite`$Tolerance==0)) { - msgBox(desc_instable_model_high_multicolinearity) + msgBox(.dico[["desc_instable_model_high_multicolinearity"]]) return(Resultats) } - - Resultats[[txt_linearity_graph_between_predictors_and_dependant_variable]]<-ols_plot_comp_plus_resid(lm.r1) - Resultats[[txt_proper_values_index]]<-FIV$eig_cindex + + Resultats[[.dico[["txt_linearity_graph_between_predictors_and_dependant_variable"]]]]<-ols_plot_comp_plus_resid(lm.r1) + Resultats[[.dico[["txt_proper_values_index"]]]]<-FIV$eig_cindex dwt(lm.r1, simulate=TRUE, method= "normal", reps=500)->DWT.results DWT.results<-round(data.frame(txt_autocorrelation=DWT.results[[1]], txt_dw_statistic=DWT.results[[2]],txt_p_dot_val=DWT.results[[3]]),4) - names(DWT.results)<-c(txt_autocorrelation,txt_dw_statistic,txt_p_dot_val ) - Resultats[[txt_durbin_watson_test_autocorr]]<-DWT.results - + names(DWT.results)<-c(.dico[["txt_autocorrelation"]],.dico[["txt_dw_statistic"]],.dico[["txt_p_dot_val"]] ) + Resultats[[.dico[["txt_durbin_watson_test_autocorr"]]]]<-DWT.results + var.err<-ols_test_breusch_pagan(lm.r1, rhs=T) - - Resultats[[txt_breusch_pagan_test]]<-data.frame(chi=var.err$bp, + + Resultats[[.dico[["txt_breusch_pagan_test"]]]]<-data.frame(chi=var.err$bp, ddl=length(var.err$preds), valeur.p=var.err$p) - - try(ceresPlots(lm.r1, main=txt_ceres_graph_linearity), silent=T) + + try(ceresPlots(lm.r1, main=.dico[["txt_ceres_graph_linearity"]]), silent=T) } if(select.m!="none"){ - - if(select.m == txt_forward_step_ascending | select.m =="forward") select.m<-"Forward" - if(select.m == txt_backward_step_descending | select.m =="backward") select.m<-txt_backward - if(select.m == txt_bidirectionnal | select.m == "bidirectional") select.m<-"Both" - - if(method %in% c("F", txt_f_value, "p", txt_probability_value)){ + + if(select.m == .dico[["txt_forward_step_ascending"]] | select.m =="forward") select.m<-"Forward" + if(select.m == .dico[["txt_backward_step_descending"]] | select.m =="backward") select.m<-.dico[["txt_backward"]] + if(select.m == .dico[["txt_bidirectionnal"]] | select.m == "bidirectional") select.m<-"Both" + + if(method %in% c("F", .dico[["txt_f_value"]], "p", .dico[["txt_probability_value"]])){ if(select.m=="Forward") ols.out <- ols_step_forward_p(lm.r1,penter = criteria, details=F) if(select.m=="Backward") ols.out <- ols_step_backward_p(lm.r1, prem=criteria, details=F) if(select.m=="Both") ols.out <- ols_step_both_p(lm.r1,pent=criteria, details=F) } - - if(method %in% c(txt_aic_criterion,"AIC")){ + + if(method %in% c(.dico[["txt_aic_criterion"]],"AIC")){ if(select.m=="Forward") ols.out <- ols_step_forward_aic(lm.r1, details=F) if(select.m=="Backward") ols.out <- ols_step_backward_aic(lm.r1, details=F) - if(select.m=="Both") ols.out <- ols_step_both_aic(lm.r1, details=F) - } - + if(select.m=="Both") ols.out <- ols_step_both_aic(lm.r1, details=F) + } + ols.frame<-data.frame(ols.out$metrics) reg.out<-ols_regress(ols.out$model) c(summary(ols.out$model)$sigma, summary(ols.out$model)$r.squared, summary(ols.out$model)$fstatistic)->significativite_modele # fournit les residus, le R.deux et le F pf(summary(ols.out$model)$fstatistic[1], summary(ols.out$model)$fstatistic[2],summary(ols.out$model)$fstatistic[3], lower.tail=F)->p.value #permet de savoir si le F est significatif - c(significativite_modele , p.value)->modele.F # on combine les precedents + c(significativite_modele , p.value)->modele.F # on combine les precedents modele.F<-round(modele.F,3) # on arrondit les nombres a la 3e decimale - names(modele.F)<-c(txt_residual_error, txt_r_dot_two, "F", - txt_df_parenthesis_num, txt_df_parenthesis_denom,txt_p_dot_val)# attribue le nom aux colonne - + names(modele.F)<-c(.dico[["txt_residual_error"]], .dico[["txt_r_dot_two"]], "F", + .dico[["txt_df_parenthesis_num"]], .dico[["txt_df_parenthesis_denom"]],.dico[["txt_p_dot_val"]])# attribue le nom aux colonne + coef.table<-data.frame(b = round(reg.out$betas, 3), - "Erreur Std."= format(round(reg.out$std_errors, 3)), + "Erreur Std."= format(round(reg.out$std_errors, 3)), "Beta" = c(" ", round(reg.out$sbetas, 3)), t=round(reg.out$tvalues, 3), valeur.p =round(reg.out$pvalues, 3), lower=round(reg.out$conf_lm[, 1], 3), upper =round(reg.out$conf_lm[, 2], 3)) - names(coef.table)<-c("b",txt_error_dot_standard,"beta","t", - txt_p_dot_val,txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot) - - select.name<-paste0(txt_selection_method,"-", method) - Resultats$selection$Selection<-ols.frame + names(coef.table)<-c("b",.dico[["txt_error_dot_standard"]],"beta","t", + .dico[["txt_p_dot_val"]],.dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]]) + + select.name<-paste0(.dico[["txt_selection_method"]],"-", method) + Resultats$selection$Selection<-ols.frame Resultats$selection$ANOVA<-modele.F - Resultats$selection[[txt_coeff_table]]<-coef.table - names(Resultats)[length(Resultats)]<-select.name - - - - - - if(any(param=="Bayes")|any(param==txt_bayesian_factors)){ - + Resultats$selection[[.dico[["txt_coeff_table"]]]]<-coef.table + names(Resultats)[length(Resultats)]<-select.name + + + + + + if(any(param=="Bayes")|any(param==.dico[["txt_bayesian_factors"]])){ + BF.out<-try(regressionBF(modele, data=dtrgeasieR,progress=F, rscaleCont=rscale), silent=T) if(class(BF.out)!='try-error') { try(plot(BF.out) , silent=T) BF.out<-extractBF(BF.out) BF.out<-head(BF.out[order(BF.out[,1], decreasing=T), ]) BF.out<-BF.out[,1:2] - Resultats[[txt_selection_method_bayesian_factor]]<-BF.out - } else Resultats[[txt_selection_method_bayesian_factor]]<-desc_selection_for_bayesian_factor_does_not_apply_to_complex_models + Resultats[[.dico[["txt_selection_method_bayesian_factor"]]]]<-BF.out + } else Resultats[[.dico[["txt_selection_method_bayesian_factor"]]]]<-.dico[["desc_selection_for_bayesian_factor_does_not_apply_to_complex_models"]] } # rm( "dtrgeasieR", envir = .GlobalEnv) } - + if(!is.null(step)){ - + as.formula(paste0(variables[1]," ~ ",step[[1]][1]))->modele.H list()->modele.H1 list()->formule.H1 for(i in 1:length(step)){ - + for(j in 1:length(step[[i]])){update(modele.H, as.formula(paste0(".~. + ",step[[i]][j])))->modele.H} formule.H1[[i]]<-modele.H lm(modele.H, data=dtrgeasieR, na.action=na.exclude )->lm.H lm.H->modele.H1[[i]]} - - if(any(param=="param")|any(param==txt_param_tests)) { + + if(any(param=="param")|any(param==.dico[["txt_param_tests"]])) { hier<-paste0("anova(modele.H1[[1]],modele.H1[[2]]") if(length(modele.H1)>2){ for(i in 3: length(modele.H1)){ @@ -261,29 +261,29 @@ regressions <- } hier<-paste0(hier,")") hier<-eval(parse(text=hier)) - attributes(hier)$heading[1]<-txt_hierarchical_models_variance_analysis_table - names(hier)<-c(txt_df_residual, "SC.resid",txt_df_effect, "SC", "F", txt_p_dot_val) - Resultats[[txt_hierarchical_model_analysis]]<-hier - - - + attributes(hier)$heading[1]<-.dico[["txt_hierarchical_models_variance_analysis_table"]] + names(hier)<-c(.dico[["txt_df_residual"]], "SC.resid",.dico[["txt_df_effect"]], "SC", "F", .dico[["txt_p_dot_val"]]) + Resultats[[.dico[["txt_hierarchical_model_analysis"]]]]<-hier + + + c(summary(modele.H1[[1]])$sigma, summary(modele.H1[[1]])$r.squared, summary(modele.H1[[1]])$fstatistic)->significativite_modele # fournit les residus, le R.deux et le F pf(summary(modele.H1[[1]])$fstatistic[1], summary(modele.H1[[1]])$fstatistic[2],summary(modele.H1[[1]])$fstatistic[3], lower.tail=F)->p.value #permet de savoir si le F est significatif c(significativite_modele , p.value)->modele_avec_outliers - + for(i in 2:(length(modele.H1))){ c(summary(modele.H1[[i]])$sigma, summary(modele.H1[[i]])$r.squared, summary(modele.H1[[i]])$fstatistic)->significativite_modele # fournit les residus, le R.deux et le F pf(summary(modele.H1[[i]])$fstatistic[1], summary(modele.H1[[i]])$fstatistic[2],summary(modele.H1[[i]])$fstatistic[3], lower.tail=F)->valeur.p #permet de savoir si le F est significatif rbind(modele_avec_outliers, c(significativite_modele ,valeur.p))->modele_avec_outliers } round(modele_avec_outliers,3)->modele_avec_outliers - c(txt_residual_error, txt_r_dot_two, "F", txt_df_parenthesis_1, txt_df_parenthesis_2,txt_p_dot_val)->dimnames(modele_avec_outliers)[[2]] - paste(txt_step, 1:length(modele_avec_outliers[,1]))->dimnames(modele_avec_outliers)[[1]] - Resultats[[txt_hierarchical_models_complete_model_sig_at_each_step]]<-modele_avec_outliers - + c(.dico[["txt_residual_error"]], .dico[["txt_r_dot_two"]], "F", .dico[["txt_df_parenthesis_1"]], .dico[["txt_df_parenthesis_2"]],.dico[["txt_p_dot_val"]])->dimnames(modele_avec_outliers)[[2]] + paste(.dico[["txt_step"]], 1:length(modele_avec_outliers[,1]))->dimnames(modele_avec_outliers)[[1]] + Resultats[[.dico[["txt_hierarchical_models_complete_model_sig_at_each_step"]]]]<-modele_avec_outliers + } - - if(any(param=="Bayes")|any(param==txt_bayesian_factors)) { + + if(any(param=="Bayes")|any(param==.dico[["txt_bayesian_factors"]])) { BF<-lmBF(formula= as.formula(formule.H1[[1]]), data=dtrgeasieR, rscaleFixed=rscale) BF.modele<-extractBF(BF, onlybf=T) BF.hier<-c(NA) @@ -293,55 +293,55 @@ regressions <- denomBF<-lmBF(formula= as.formula(formule.H1[[i-1]]), data=dtrgeasieR, rscaleFixed=rscale) OddBF<-numBF/denomBF BF.hier<-c(BF.hier, extractBF(OddBF, onlybf=T))} - + BF.hier<-data.frame(desc_fb_ratio_between_models=BF.hier, txt_bayesian_factor_of_model= BF.modele) dimnames(BF.hier)[[1]]<- unlist(as.character(formule.H1)) - Resultats[[txt_bayesian_approach_hierarchical_models]]<-BF.hier + Resultats[[.dico[["txt_bayesian_approach_hierarchical_models"]]]]<-BF.hier } - + } - # txt_param_test, txt_non_param_test,txt_robusts_tests_with_bootstraps, txt_bayesian_factors - if(any(param=="param")|any(param==txt_param_tests)) { - - significativite_modele <-c(summary(lm.r1)$sigma, - summary(lm.r1)$r.squared, + # .dico[["txt_param_test"]], .dico[["txt_non_param_test"]],.dico[["txt_robusts_tests_with_bootstraps"]], .dico[["txt_bayesian_factors"]] + if(any(param=="param")|any(param==.dico[["txt_param_tests"]])) { + + significativite_modele <-c(summary(lm.r1)$sigma, + summary(lm.r1)$r.squared, summary(lm.r1)$fstatistic)# fournit les residus, le R.deux et le F p.value<-pf(summary(lm.r1)$fstatistic[1], summary(lm.r1)$fstatistic[2],summary(lm.r1)$fstatistic[3], lower.tail=F)#permet de savoir si le F est significatif c(significativite_modele , p.value)->modele.F # on combine les precedents round(modele.F,3)->modele.F # on arrondit les nombres a la 3e decimale - c(txt_residual_error, txt_r_dot_two, "F", txt_df_parenthesis_num, txt_df_parenthesis_denom,txt_p_dot_val)->names(modele.F)# attribue le nom aux colonnes + c(.dico[["txt_residual_error"]], .dico[["txt_r_dot_two"]], "F", .dico[["txt_df_parenthesis_num"]], .dico[["txt_df_parenthesis_denom"]],.dico[["txt_p_dot_val"]])->names(modele.F)# attribue le nom aux colonnes modele.F->Resultats$"Estimation du modele global" - + reg.out<-ols_regress(lm.r1) coef.table<-data.frame(b = round(reg.out$betas, 3), - "Erreur Std."= format(round(reg.out$std_errors, 3)), + "Erreur Std."= format(round(reg.out$std_errors, 3)), "Beta" = c(" ", round(reg.out$sbetas, 3)), t=round(reg.out$tvalues, 3), valeur.p =round(reg.out$pvalues, 3), lower=round(reg.out$conf_lm[, 1], 3), upper =round(reg.out$conf_lm[, 2], 3)) - names(coef.table)<-c("b",txt_error_dot_standard,"beta","t", - txt_p_dot_val,txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot) - + names(coef.table)<-c("b",.dico[["txt_error_dot_standard"]],"beta","t", + .dico[["txt_p_dot_val"]],.dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]]) + + + + - - - if(length(pred)>1){ ols.corr<-try(ols_correlations(lm.r1), silent=T) if(any(class(ols.corr)!='try-error')){ - Resultats[[txt_variables_contribution_to_model]]<-ols.corr - Resultats[[txt_added_variables_graph]] <-ols_plot_added_variable(lm.r1) - coef.table[[txt_delta_r_squared]]<-c(" ", round((ols.corr$Part)^2,3)) + Resultats[[.dico[["txt_variables_contribution_to_model"]]]]<-ols.corr + Resultats[[.dico[["txt_added_variables_graph"]]]] <-ols_plot_added_variable(lm.r1) + coef.table[[.dico[["txt_delta_r_squared"]]]]<-c(" ", round((ols.corr$Part)^2,3)) } - + } - Resultats[[txt_beta_table]]<-coef.table + Resultats[[.dico[["txt_beta_table"]]]]<-coef.table } - - if(any(param=="Bayes")|any(param==txt_bayesian_factors)){ - + + if(any(param=="Bayes")|any(param==.dico[["txt_bayesian_factors"]])){ + lmBF(modele1, data=dtrgeasieR)->BF.out BF.table<-extractBF(BF.out)[1:2] if(length(pred)>1) { for(i in 2:length(pred)){ @@ -350,20 +350,20 @@ regressions <- BF.table<-rbind(BF.table, extractBF(BF.out)[1:2]) } } - Resultats[[txt_bayesian_factors]]<-BF.table - + Resultats[[.dico[["txt_bayesian_factors"]]]]<-BF.table + } - - if(any(param==txt_robusts| any(param==txt_robusts_tests_with_bootstraps))){ - + + if(any(param==.dico[["txt_robusts"]]| any(param==.dico[["txt_robusts_tests_with_bootstraps"]]))){ + rlm(formula=modele, data=dtrgeasieR)->modele_robuste summary(modele_robuste)->res_modele_robuste (1-pt(abs(res_modele_robuste$coefficients[,3]), (length(dtrgeasieR[,1])-1-length(pred)), lower.tail=TRUE))*2->proba round(cbind(res_modele_robuste$coefficients, proba),3)->M_estimator data.frame(M_estimator)->M_estimator - noms<-c(txt_b_m_estimator, "SE", "t", txt_p_dot_val) - - + noms<-c(.dico[["txt_b_m_estimator"]], "SE", "t", .dico[["txt_p_dot_val"]]) + + if(n.boot>100){ bootReg<-function(formula, dtrgeasieR, i) { d <- dtrgeasieR[i,] @@ -376,21 +376,21 @@ regressions <- if(is.null(intervalle)){ for(i in 1: length(lm.r1$coefficients)){boot.ci(bootResults, type = "perc", index = i)$percent[,4:5]->resultats rbind(intervalle, resultats)->intervalle} - noms<-c(noms, txt_percentile_inferior_limit_dot, txt_percentile_superior_limit_dot) + noms<-c(noms, .dico[["txt_percentile_inferior_limit_dot"]], .dico[["txt_percentile_superior_limit_dot"]]) } else{ - noms<-c(noms, txt_bca_inferior_limit, txt_bca_superior_limit) + noms<-c(noms, .dico[["txt_bca_inferior_limit"]], .dico[["txt_bca_superior_limit"]]) } data.frame(M_estimator, round(intervalle,4))->M_estimator } names(M_estimator)<-noms - Resultats[[txt_robusts_statistics]]<-M_estimator + Resultats[[.dico[["txt_robusts_statistics"]]]]<-M_estimator } - - - if(CV) desc_cross_validation_issues - + + + if(CV) .dico[["desc_cross_validation_issues"]] + return(Resultats) - + } options (warn=-1) .e <- environment() @@ -421,16 +421,16 @@ regressions <- method<-reg.in.output$reg.options$method criteria<-reg.in.output$reg.options$criteria group<-reg.in.output$reg.options$group - - - - - - - - - if(any(outlier== txt_complete_dataset)){ - Resultats[[txt_complete_dataset]]<-regressions.out(dtrgeasieR=data, modele=modele, VC=VC, select.m=select.m, method=method, step=step, group=group, criteria=criteria , scale=scale, + + + + + + + + + if(any(outlier== .dico[["txt_complete_dataset"]])){ + Resultats[[.dico[["txt_complete_dataset"]]]]<-regressions.out(dtrgeasieR=data, modele=modele, VC=VC, select.m=select.m, method=method, step=step, group=group, criteria=criteria , scale=scale, sauvegarde=sauvegarde, n.boot=n.boot, param=param, rscale=rscale) if(!is.null(group)) { R1<-list() @@ -440,15 +440,15 @@ regressions <- for(i in 1:length(G)){ resg<-regressions.out(dtrgeasieR=G[[i]], modele=modele, VC=VC, select.m=select.m, method=method, step=step, group=group, criteria=criteria , scale=scale, sauvegarde=sauvegarde, n.boot=n.boot, param=param, rscale=rscale) - + R1[[length(R1)+1]]<-resg names(R1)[length(R1)]<-names(G)[i] } - Resultats[[txt_complete_dataset]][[txt_group_analysis]]<-R1 + Resultats[[.dico[["txt_complete_dataset"]]]][[.dico[["txt_group_analysis"]]]]<-R1 } - + } - if(any(outlier==txt_identifying_outliers)|any(outlier==txt_without_outliers)|inf==T){ + if(any(outlier==.dico[["txt_identifying_outliers"]])|any(outlier==.dico[["txt_without_outliers"]])|inf==T){ lm.r1<-lm(modele, data) as.character(attributes(terms(modele))$variables)->variables variables[2:length(variables)]->variables @@ -465,29 +465,29 @@ regressions <- data[which(apply(mesure_influence$is.inf, 1, any)),"est.inf"]<-"*" ols_plot_dfbetas(lm.r1) data[order(data$res.student.p.Bonf), ]->data - writeLines(desc_obs_with_asterisk_are_outliers) + writeLines(.dico[["desc_obs_with_asterisk_are_outliers"]]) View(data) suppression<-"yes" outliers<-data.frame() nettoyees<-data while(suppression=="yes"){ - - cat (ask_press_enter_to_continue) + + cat (.dico[["ask_press_enter_to_continue"]]) line <- readline() sup<-NA while(is.na(sup)){ - sup <- dlgInput(ask_obs_to_remove, 0)$res + sup <- dlgInput(.dico[["ask_obs_to_remove"]], 0)$res if(length(sup)==0) return(regressions()) strsplit(sup, ":")->sup tail(sup[[1]],n=1)->sup as.numeric(sup)->sup - if(is.na(sup)) msgBox(ask_enter_number_of_to_be_removed_variable) + if(is.na(sup)) msgBox(.dico[["ask_enter_number_of_to_be_removed_variable"]]) } if(sup==0) suppression<-"no" else { rbind(outliers, nettoyees[which(dimnames(nettoyees)[[1]]==sup),])->outliers nettoyees[-which(dimnames(nettoyees)[[1]]==sup),]->nettoyees } - + } if(length(outliers)!=0) outliers<-outliers[,variables] assign(nom, data, envir=.GlobalEnv) @@ -497,21 +497,21 @@ regressions <- data[which(data$cook.d<= seuil_cook), ]->nettoyees data[which(data$cook.d>= seuil_cook), ]->outliers cbind(outliers[,variables],outliers$cook.d)->outliers - Resultats$"information"[[desc_outliers_identified_on_4_div_n]] + Resultats$"information"[[.dico[["desc_outliers_identified_on_4_div_n"]]]] } nettoyees->>nettoyees length(data[,1])-length(nettoyees[,1])->N_retire # identifier le nombre d observations retirees sur la base de la distance de cook - if(any(outlier== txt_identifying_outliers)){ + if(any(outlier== .dico[["txt_identifying_outliers"]])){ paste(N_retire/length(data[,1])*100,"%")->Pourcentage_retire # fournit le pourcentage retire - data.frame("N.retire"=N_retire, txt_percent_removed_obs=Pourcentage_retire)->Resultats[[txt_identified_outliers_synthesis]] - if(length(outliers)!=0) Resultats[[txt_identifying_outliers]][[desc_identified_outliers]]<-outliers - + data.frame("N.retire"=N_retire, txt_percent_removed_obs=Pourcentage_retire)->Resultats[[.dico[["txt_identified_outliers_synthesis"]]]] + if(length(outliers)!=0) Resultats[[.dico[["txt_identifying_outliers"]]]][[.dico[["desc_identified_outliers"]]]]<-outliers + } - if(any(outlier== txt_without_outliers)) { - if(N_retire!=0 | all(outlier!=txt_complete_dataset)){ - Resultats[[txt_without_outliers]]<-regressions.out(dtrgeasieR=nettoyees, modele=modele, VC=VC, select.m=select.m, method=method, step=step, group=group, criteria=criteria , scale=scale, + if(any(outlier== .dico[["txt_without_outliers"]])) { + if(N_retire!=0 | all(outlier!=.dico[["txt_complete_dataset"]])){ + Resultats[[.dico[["txt_without_outliers"]]]]<-regressions.out(dtrgeasieR=nettoyees, modele=modele, VC=VC, select.m=select.m, method=method, step=step, group=group, criteria=criteria , scale=scale, sauvegarde=sauvegarde, n.boot=n.boot, param=param, rscale=rscale) - + if(!is.null(group)) { R1<-list() G<-nettoyees[,group] @@ -520,19 +520,19 @@ regressions <- for(i in 1:length(G)){ resg<-regressions.out(dtrgeasieR=G[[i]], modele=modele, VC=VC, select.m=select.m, method=method, step=step, group=group, criteria=criteria , scale=scale, sauvegarde=sauvegarde, n.boot=n.boot, param=param, rscale=rscale) - + R1[[length(R1)+1]]<-resg names(R1)[length(R1)]<-names(G)[i] } - Resultats[[txt_without_outliers]][[txt_group_analysis]]<-R1 + Resultats[[.dico[["txt_without_outliers"]]]][[.dico[["txt_group_analysis"]]]]<-R1 } - - + + } } } - - + + paste(outlier, collapse="','", sep="")->outlier paste(param, collapse="','", sep="")->param as.character(modele)->m1 @@ -550,14 +550,14 @@ regressions <- Resultats$Call<-paste0("regressions(data=", nom, ",modele=", modele, ",outlier=c('", outlier, "'),inf=", inf, ",CV=", CV,",select.m='", select.m,"',step=", ifelse(!is.null(step), step.call,"NULL"), ",group=", ifelse(is.null(group), "NULL", paste0("c('",group,"')")), ",criteria=", criteria, ",scale=", scale, ",dial=T, info=T,sauvegarde=", sauvegarde, ",n.boot=", n.boot, ",param=c('", param, "'),rscale=", round(rscale,3), ")") - - + + .add.history(data=data, command=Resultats$Call, nom=nom) - .add.result(Resultats=Resultats, name =paste(txt_multiple_regressions_dot, Sys.time() )) - if(sauvegarde) if(sauvegarde) save(Resultats=Resultats, choix=txt_multiple_regressions_dot, env=.e) - Resultats[[txt_references]]<-ref1(packages) + .add.result(Resultats=Resultats, name =paste(.dico[["txt_multiple_regressions_dot"]], Sys.time() )) + if(sauvegarde) if(sauvegarde) save(Resultats=Resultats, choix=.dico[["txt_multiple_regressions_dot"]], env=.e) + Resultats[[.dico[["txt_references"]]]]<-ref1(packages) if(html) ez.html(Resultats) - + return(Resultats) } @@ -576,132 +576,132 @@ regressions <- # step : list. Each element of the list is a vector with the effect to test at the specific step (see details) # group : character. Name of the factor variable definying the groups # scale : Logical. Should the predictor be scaled before the analysis (recommended) ? - + Resultats<-list() step1<-terms(as.formula(modele)) - + step2<-as.character( attributes(step1)$variables)[-1] step1<-attributes(step1)$term.labels if(dial || !is.logical(scale)){ - if(info) writeLines(ask_center_numeric_variables) - scale<-dlgList(c(txt_center, txt_non_centered), multiple = FALSE, title=ask_center)$res + if(info) writeLines(.dico[["ask_center_numeric_variables"]]) + scale<-dlgList(c(.dico[["txt_center"]], .dico[["txt_non_centered"]]), multiple = FALSE, title=.dico[["ask_center"]])$res if(length(scale)==0) return(NULL) - scale<-ifelse(scale==txt_center,T,F) + scale<-ifelse(scale==.dico[["txt_center"]],T,F) } Resultats$scale<-scale if(dial || !is.logical(inf) || !is.logical(CV)) { - writeLines(ask_specify_other_options_regressions) - autres.options<-c(txt_cross_validation,txt_influence_method, txt_none) - if(dim(model.matrix(modele, data))[2]>2) autres.options<-c(txt_selection_methods, txt_hierarchical_models, autres.options) - if(length(step2)2) autres.options<-c(.dico[["txt_selection_methods"]], .dico[["txt_hierarchical_models"]], autres.options) + if(length(step2)groupe.check if(any(is.na(groupe.check)) || min(groupe.check)<(length(dimnames(model.matrix(as.formula(modele), data))[[2]])+10)) { - msgBox(desc_at_least_10_obs_needed) + msgBox(.dico[["desc_at_least_10_obs_needed"]]) return(groupe.check) } } - - if(any(autres.options==txt_selection_methods) || select.m!="none" & length(select.m)!=1 | !select.m%in%c("none","forward", "backward", "bidirectional",txt_forward_step_ascending, - txt_backward_step_descending, txt_bidirectionnal)){ - if(info) writeLines(ask_chose_selection_method) - select.m<- dlgList(c(txt_forward_step_ascending,txt_backward_step_descending, txt_bidirectionnal), - preselect=NULL, multiple = FALSE, title=txt_method_choice)$res + + if(any(autres.options==.dico[["txt_selection_methods"]]) || select.m!="none" & length(select.m)!=1 | !select.m%in%c("none","forward", "backward", "bidirectional",.dico[["txt_forward_step_ascending"]], + .dico[["txt_backward_step_descending"]], .dico[["txt_bidirectionnal"]])){ + if(info) writeLines(.dico[["ask_chose_selection_method"]]) + select.m<- dlgList(c(.dico[["txt_forward_step_ascending"]],.dico[["txt_backward_step_descending"]], .dico[["txt_bidirectionnal"]]), + preselect=NULL, multiple = FALSE, title=.dico[["txt_method_choice"]])$res if(length(select.m)==0) return(.regressions.options(data=data, modele=modele)) } if(!is.null(method)){ - if(any(autres.options==txt_selection_methods) || (select.m!="none" && !method%in%c("AIC", "p", "F", txt_f_value,txt_probability_value, txt_aic_criterion)) ){ - if(info) writeLines(ask_selection_method) - method<- dlgList(c(txt_f_value,txt_probability_value, txt_aic_criterion), - preselect=c(txt_f_value), multiple = FALSE, title=txt_method_choice)$res + if(any(autres.options==.dico[["txt_selection_methods"]]) || (select.m!="none" && !method%in%c("AIC", "p", "F", .dico[["txt_f_value"]],.dico[["txt_probability_value"]], .dico[["txt_aic_criterion"]])) ){ + if(info) writeLines(.dico[["ask_selection_method"]]) + method<- dlgList(c(.dico[["txt_f_value"]],.dico[["txt_probability_value"]], .dico[["txt_aic_criterion"]]), + preselect=c(.dico[["txt_f_value"]]), multiple = FALSE, title=.dico[["txt_method_choice"]])$res if(length(method)==0) return(.regressions.options(data=data, modele=modele)) } - - if(select.m!="none" & (method==txt_f_value | method=="F")){ - if(!is.null(criteria) && (!is.numeric(criteria) || criteria<1)) {msgBox(desc_specify_f_value) + + if(select.m!="none" & (method==.dico[["txt_f_value"]] | method=="F")){ + if(!is.null(criteria) && (!is.numeric(criteria) || criteria<1)) {msgBox(.dico[["desc_specify_f_value"]]) criteria<-NULL} - + if(is.null(criteria)) { while(is.null(criteria)){ - criteria <- dlgInput(ask_f_value, 4)$res + criteria <- dlgInput(.dico[["ask_f_value"]], 4)$res if(length(criteria)==0) return(.regressions.options(data=data, modele=modele)) strsplit(criteria, ":")->criteria tail(criteria[[1]],n=1)->criteria as.numeric(criteria)->criteria if(is.na(criteria) || criteria<1) {criteria<-NULL - msgBox(desc_specify_f_value) + msgBox(.dico[["desc_specify_f_value"]]) } criteria<-df(criteria, df1=1, df2=(length(data[,1])-1-length(step1)), log = FALSE) } } } - - if(select.m!="none" & (method==txt_probability_value | method=="p")){ - if(dial | !is.null(criteria) && (!is.numeric(criteria) || criteria<0 || criteria>1)) {msgBox(desc_specify_probability_value) + + if(select.m!="none" & (method==.dico[["txt_probability_value"]] | method=="p")){ + if(dial | !is.null(criteria) && (!is.numeric(criteria) || criteria<0 || criteria>1)) {msgBox(.dico[["desc_specify_probability_value"]]) criteria<-NULL} if(is.null(criteria)) { while(is.null(criteria)){ - criteria <- dlgInput(ask_probability_value, 0.15)$res + criteria <- dlgInput(.dico[["ask_probability_value"]], 0.15)$res if(length(criteria)==0) return(.regressions.options(data=data, modele=modele)) strsplit(criteria, ":")->criteria tail(criteria[[1]],n=1)->criteria as.numeric(criteria)->criteria if(is.na(criteria) || criteria>1 || criteria<0 ) {criteria<-NULL - msgBox(desc_specify_probability_value)} + msgBox(.dico[["desc_specify_probability_value"]])} } } - + } } - if(any(autres.options==txt_hierarchical_models)| !is.null(step)) { - + if(any(autres.options==.dico[["txt_hierarchical_models"]])| !is.null(step)) { + if(!is.null(step) ){ st1<-unlist(step) - if(any(table(st1>1))) st1<-txt_error - if(any(!st1%in%step1 ))st1<-txt_error - if(st1==txt_error){ - msgBox(desc_issue_in_hierarchical_regression) + if(any(table(st1>1))) st1<-.dico[["txt_error"]] + if(any(!st1%in%step1 ))st1<-.dico[["txt_error"]] + if(st1==.dico[["txt_error"]]){ + msgBox(.dico[["desc_issue_in_hierarchical_regression"]]) step<-NULL } } if(is.null(step)){ - if(info) writeLines(ask_chose_variables) + if(info) writeLines(.dico[["ask_chose_variables"]]) step<-list() - step[[1]]<- dlgList(step1, preselect=NULL, multiple = TRUE, title=txt_variables_from_step)$res + step[[1]]<- dlgList(step1, preselect=NULL, multiple = TRUE, title=.dico[["txt_variables_from_step"]])$res if(length(step[[1]])==0) return(.regressions.options(data=data, modele=modele)) setdiff(step1,step[[1]])->step1 - + while(length(step1!=0)){ - step[[length(step)+1]]<-dlgList(step1, multiple = TRUE,title=txt_variables_from_step)$res + step[[length(step)+1]]<-dlgList(step1, multiple = TRUE,title=.dico[["txt_variables_from_step"]])$res if(length(step[[length(step)]])==0) return(.regressions.options(data=data, modele=modele)) setdiff(step1,step[[length(step) ]])->step1 } } } - + Resultats$step<-step Resultats$select.m<-select.m Resultats$method<-method Resultats$criteria<-criteria Resultats$group<-group - rm( "dtrgeasieR", envir = .GlobalEnv) + rm( "dtrgeasieR", envir = .GlobalEnv) return(Resultats) } diff --git a/R/regressions.log.R b/R/regressions.log.R index e04cd85..2268c61 100644 --- a/R/regressions.log.R +++ b/R/regressions.log.R @@ -24,16 +24,16 @@ regressions.log <- if(dial && is.null(modele)){ - if(info) writeLines(ask_chose_relation_between_vars_regressions_log) - dlgList(c(txt_additive_effects, txt_interaction_effects, txt_specify_model), preselect=txt_regressions, multiple = TRUE, title=ask_which_regression_type)$res->link + if(info) writeLines(.dico[["ask_chose_relation_between_vars_regressions_log"]]) + dlgList(c(.dico[["txt_additive_effects"]], .dico[["txt_interaction_effects"]], .dico[["txt_specify_model"]]), preselect=.dico[["txt_regressions"]], multiple = TRUE, title=.dico[["ask_which_regression_type"]])$res->link if(length(link)==0) return(NULL)} else link<-"none" if(length(Y)>1){ - msgBox(desc_only_one_dependant_variable_alllowed) + msgBox(.dico[["desc_only_one_dependant_variable_alllowed"]]) Y<-NULL } - if(any(link %in% c(txt_additive_effects, txt_interaction_effects))){ - msg3<-ask_chose_dependant_variable - Y<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=FALSE, title=txt_dependant_variable, out=NULL) + if(any(link %in% c(.dico[["txt_additive_effects"]], .dico[["txt_interaction_effects"]]))){ + msg3<-.dico[["ask_chose_dependant_variable"]] + Y<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=FALSE, title=.dico[["txt_dependant_variable"]], out=NULL) if(is.null(Y)) { reg.log.in()->Resultats return(Resultats)} @@ -41,27 +41,27 @@ regressions.log <- Y<-Y$X if(length(unique(data[,Y]))!=2) { - msg1<-paste(desc_your_dependant_variable_has, length(unique(data[,Y])), desc_must_be_dichotomic ) + msg1<-paste(.dico[["desc_your_dependant_variable_has"]], length(unique(data[,Y])), .dico[["desc_must_be_dichotomic"]] ) msgBox(msg1) if(class(data[,Y]) %in%c("numeric","integer")){ - dlgMessage(ask_convert_dependant_variable_to_dichotomic,"yesno")$res->conv + dlgMessage(.dico[["ask_convert_dependant_variable_to_dichotomic"]],"yesno")$res->conv if(conv=="no") return(reg.log.in()) else{ - if(info) writeLines(ask_criterion_for_dichotomy) - dlgList(c(txt_median, txt_threshold), preselect=txt_median, multiple = FALSE, title=ask_coding_criterion)$res->codage + if(info) writeLines(.dico[["ask_criterion_for_dichotomy"]]) + dlgList(c(.dico[["txt_median"]], .dico[["txt_threshold"]]), preselect=.dico[["txt_median"]], multiple = FALSE, title=.dico[["ask_coding_criterion"]])$res->codage if(length(codage)==0) return(reg.log.in()) - if(codage==txt_median) data[,Y]<-ifelse(data[,Y]>median(data[,Y]),1, 0) + if(codage==.dico[["txt_median"]]) data[,Y]<-ifelse(data[,Y]>median(data[,Y]),1, 0) View(data) readline() - if(codage==txt_threshold) { + if(codage==.dico[["txt_threshold"]]) { seuil<-NA while(is.na(seuil)){ - seuil<-dlgInput(ask_separation_value, median(data[,Y]))$res + seuil<-dlgInput(.dico[["ask_separation_value"]], median(data[,Y]))$res if(length(seuil)==0) return(reg.log.in()) strsplit(seuil, ":")->seuil tail(seuil[[1]],n=1)->seuil as.numeric(seuil)->seuil - if(is.na(seuil) || seuil>max(data[,Y]) || seuilmax(data[,Y]) || seuilseuil,1, 0) @@ -70,10 +70,10 @@ regressions.log <- } } if(class(data[,Y]) %in%c("factor","character")){ - dlgMessage(ask_regroup_modalities,"yesno")$res->reg + dlgMessage(.dico[["ask_regroup_modalities"]],"yesno")$res->reg if(reg=="no") return(reg.log.in()) else { - if(info) writeLines(ask_linebase_modalities) - reg<- dlgList(levels(data[,Y]), preselect=NULL, multiple = TRUE, title=txt_modalities_to_regroup)$res + if(info) writeLines(.dico[["ask_linebase_modalities"]]) + reg<- dlgList(levels(data[,Y]), preselect=NULL, multiple = TRUE, title=.dico[["txt_modalities_to_regroup"]])$res setdiff(levels(data[,Y]),reg)->reste data[,Y]<-ifelse(data[,Y]%in%reg, 0,1) data[,Y]<-factor(data[,Y]) @@ -82,9 +82,9 @@ regressions.log <- } - if(any(link==txt_additive_effects) || !is.null(X_a)| any(X_a %in% names(data)==F)) { - msg3<-ask_chose_dependant_variable - X_a<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=txt_additive_model_variables, out=Y) + if(any(link==.dico[["txt_additive_effects"]]) || !is.null(X_a)| any(X_a %in% names(data)==F)) { + msg3<-.dico[["ask_chose_dependant_variable"]] + X_a<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=.dico[["txt_additive_model_variables"]], out=Y) if(is.null(X_a)) { reg.log.in()->Resultats return(Resultats)} @@ -93,11 +93,11 @@ regressions.log <- }else X_a<-NULL - if(any(link==txt_interaction_effects) || !is.null(X_i) & (length(X_i)<2 | any(X_i %in% names(data)==F))) { - msg3<-ask_chose_interaction_model_predictors + if(any(link==.dico[["txt_interaction_effects"]]) || !is.null(X_i) & (length(X_i)<2 | any(X_i %in% names(data)==F))) { + msg3<-.dico[["ask_chose_interaction_model_predictors"]] X_i<-c() while(length(X_i)<2){ - X_i<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=txt_interactive_model_variables, out=c(X_a,Y)) + X_i<-.var.type(X=Y, info=info, data=data, type=NULL, check.prod=F, message=msg3, multiple=TRUE, title=.dico[["txt_interactive_model_variables"]], out=c(X_a,Y)) if(is.null(X_i)) { reg.log.in()->Resultats return(Resultats)} @@ -127,7 +127,7 @@ regressions.log <- - if(any(link==txt_specify_model)) modele<-fix(modele) + if(any(link==.dico[["txt_specify_model"]])) modele<-fix(modele) variables<-terms(as.formula(modele)) variables<-as.character( attributes(variables)$variables)[-1] pred<-attributes(terms(as.formula(modele)))$term.labels @@ -136,8 +136,8 @@ regressions.log <- if(length(pred>1)){ pred.ord<-c() while(length(pred)!=0){ - if(info) writeLines(ask_variables_order_for_max_likelihood) - V1<-dlgList(pred, multiple = FALSE,title=ask_variable_at_this_point)$res + if(info) writeLines(.dico[["ask_variables_order_for_max_likelihood"]]) + V1<-dlgList(pred, multiple = FALSE,title=.dico[["ask_variable_at_this_point"]])$res c(pred.ord,V1)->pred.ord setdiff(pred,V1)->pred} }else pred.ord<-pred @@ -148,7 +148,7 @@ regressions.log <- model.test<-try(model.matrix(modele, data), silent=T) if(any(class(model.test)=='try-error')) { - msgBox(desc_incorrect_model) + msgBox(.dico[["desc_incorrect_model"]]) return(reg.log.in()) } @@ -162,8 +162,8 @@ regressions.log <- if(is.null(reg.options)) return(reg.log.in()) if(dial){ - if(info) writeLines(ask_integrate_probabilities_to_dataset) - dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=ask_probabilities)$res->proba + if(info) writeLines(.dico[["ask_integrate_probabilities_to_dataset"]]) + dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=.dico[["ask_probabilities"]])$res->proba } @@ -183,20 +183,20 @@ regressions.log <- variables<-terms(as.formula(modele)) variables<-as.character( attributes(variables)$variables)[-1] pred<-attributes(terms(as.formula(modele)))$term.labels - Resultats[[txt_descriptive_statistics]]<-.stat.desc.out(X=variables, groupes=NULL, data=data, tr=.1, type=3, plot=T) - -if(scale==T || scale==txt_center) { - Resultats$info<-desc_centered_data_schielzeth_recommandations - variables[-1]->pred2 - if(length(pred2)>1) { which(!sapply(data[,pred2[which(pred2 %in% variables)]],class)%in%c("factor", "character"))->centre - centre<-pred2[centre]}else{centre<-NULL} - if(!is.null(centre)){ - if(length(centre)==1) data[,centre]-mean(data[,centre],na.rm=T)->data[,centre] else{ - sapply(X=data[,centre], FUN = function(X){X-mean(X, na.rm=T)})->data[,centre] - } - -} -} + Resultats[[.dico[["txt_descriptive_statistics"]]]]<-.stat.desc.out(X=variables, groupes=NULL, data=data, tr=.1, type=3, plot=T) + + if(scale==T || scale==.dico[["txt_center"]]) { + Resultats$info<-.dico[["desc_centered_data_schielzeth_recommandations"]] + variables[-1]->pred2 + if(length(pred2)>1) { which(!sapply(data[,pred2[which(pred2 %in% variables)]],class)%in%c("factor", "character"))->centre + centre<-pred2[centre]}else{centre<-NULL} + if(!is.null(centre)){ + if(length(centre)==1) data[,centre]-mean(data[,centre],na.rm=T)->data[,centre] else{ + sapply(X=data[,centre], FUN = function(X){X-mean(X, na.rm=T)})->data[,centre] + } + + } + } if(class(data[,variables[1]])=="character") factor(data[,variables[1]])->data[,variables[1]] if(!is.null(step)){ @@ -220,10 +220,10 @@ if(scale==T || scale==txt_center) { hier<-paste0(hier,")") hier<-eval(parse(text=hier)) - attributes(hier)$heading[1]<-txt_hierarchical_models_deviance_table + attributes(hier)$heading[1]<-.dico[["txt_hierarchical_models_deviance_table"]] round(1-pchisq(hier$Deviance,hier$Df,lower.tail=F),4)->hier$valeur.p - names(hier)<-c(txt_df_residual, "Deviance.resid",txt_df_effect, txt_deviation, txt_p_dot_val) - Resultats[[txt_hierarchical_model_analysis]]<-hier + names(hier)<-c(.dico[["txt_df_residual"]], "Deviance.resid",.dico[["txt_df_effect"]], .dico[["txt_deviation"]], .dico[["txt_p_dot_val"]]) + Resultats[[.dico[["txt_hierarchical_model_analysis"]]]]<-hier } @@ -244,45 +244,45 @@ if(scale==T || scale==txt_center) { summary(mod[[length(mod)]])->resultats as(resultats$call,"character")->texte - paste(desc_tested_model_is , texte[2])->Resultats[[txt_test_model]] + paste(.dico[["desc_tested_model_is"]] , texte[2])->Resultats[[.dico[["txt_test_model"]]]] cbind(rms::vif(mod[[length(mod)]]), 1/rms::vif(mod[[length(mod)]]))->MC - dimnames(MC)[[2]]<-c(txt_inflation_variance_factor, txt_tolerance) - round(MC,4)->Resultats[[txt_multicolinearity_test]] + dimnames(MC)[[2]]<-c(.dico[["txt_inflation_variance_factor"]], .dico[["txt_tolerance"]]) + round(MC,4)->Resultats[[.dico[["txt_multicolinearity_test"]]]] sum(Amelioration_du_MV$Df[2:length(Amelioration_du_MV$Df)])->ddl Amelioration_du_MV$`Resid. Dev`[1]-Amelioration_du_MV$`Resid. Dev`[length(Amelioration_du_MV$`Resid. Dev`)]->chi.carre.modele round(1-pchisq(chi.carre.modele,ddl),4)->valeur.p logisticPseudoR2s(mod[[length(mod)]])->Pseudo.R.carre data.frame(chi.carre.modele, ddl, valeur.p,Pseudo.R.carre[1],Pseudo.R.carre[2],Pseudo.R.carre[3])->mod.glob - names(mod.glob)<-c(txt_chi_dot_squared_model, txt_df, txt_p_dot_val,txt_hosmer_lemeshow_r_2,txt_cox_snell_r_2,"Nagelkerke R^2") - mod.glob->Resultats[[txt_model_significance]] + names(mod.glob)<-c(.dico[["txt_chi_dot_squared_model"]], .dico[["txt_df"]], .dico[["txt_p_dot_val"]],.dico[["txt_hosmer_lemeshow_r_2"]],.dico[["txt_cox_snell_r_2"]],"Nagelkerke R^2") + mod.glob->Resultats[[.dico[["txt_model_significance"]]]] Amelioration_du_MV$chi.deux.prob<-1-pchisq(Amelioration_du_MV$Deviance, Amelioration_du_MV$Df) round(Amelioration_du_MV,4)->Amelioration_du_MV - names(Amelioration_du_MV)<-c(txt_df_predictor, "MV",txt_df_residuals,"MV residuel",txt_p_dot_val) - Resultats[[desc_improve_likelihood_for_each_variable]]<-data.frame(Amelioration_du_MV) + names(Amelioration_du_MV)<-c(.dico[["txt_df_predictor"]], "MV",.dico[["txt_df_residuals"]],"MV residuel",.dico[["txt_p_dot_val"]]) + Resultats[[.dico[["desc_improve_likelihood_for_each_variable"]]]]<-data.frame(Amelioration_du_MV) data.frame(resultats$coefficients)->table (table$z.value)^2->table$Wald.statistic exp(table$Estimate)->table$Odd.Ratio round(table,4)->table - names(table)<-c("b",txt_error_dot_standard,txt_z_dot_val,"p.Wald", "Wald",txt_odd_ratio_dot) + names(table)<-c("b",.dico[["txt_error_dot_standard"]],.dico[["txt_z_dot_val"]],"p.Wald", "Wald",.dico[["txt_odd_ratio_dot"]]) cbind(table, round(exp(confint(mod[[length(mod)]])),4))->table - table$interpretation<-ifelse(table$Odd.ratio>=1,paste(table$Odd.ratio, desc_times_more), paste(round(1/table$Odd.ratio,4), desc_times_less)) - table->Resultats[[txt_coeff_table]] + table$interpretation<-ifelse(table$Odd.ratio>=1,paste(table$Odd.ratio, .dico[["desc_times_more"]]), paste(round(1/table$Odd.ratio,4), .dico[["desc_times_less"]])) + table->Resultats[[.dico[["txt_coeff_table"]]]] R_sq<-NULL for(i in 1:length(mod)){logisticPseudoR2s(mod[[i]])->R_squared rbind(R_sq, R_squared)->R_sq} diff(R_sq,lag=1)->R_sq[2.] dimnames(R_sq)[[1]]<-pred - dimnames(R_sq)[[2]]<-c(txt_hosmer_lemeshow_r_2,txt_cox_snell_r_2,"Nagelkerke R^2") - R_sq->Resultats[[txt_pseudo_r_square_delta]] + dimnames(R_sq)[[2]]<-c(.dico[["txt_hosmer_lemeshow_r_2"]],.dico[["txt_cox_snell_r_2"]],"Nagelkerke R^2") + R_sq->Resultats[[.dico[["txt_pseudo_r_square_delta"]]]] if(proba=="TRUE") { - round(fitted(mod[[length(mod)]]),4)->data[[txt_predicted_probabilities]] + round(fitted(mod[[length(mod)]]),4)->data[[.dico[["txt_predicted_probabilities"]]]] head(data) print(nom) assign(x=nom, value=data, envir=.GlobalEnv)} @@ -290,9 +290,9 @@ if(scale==T || scale==txt_center) { if(select.m!="none"){ #select.m<-switch(select.m,txt_forward_step_ascending="forward", txt_backward_step_descending="backward", txt_bidirectionnal="both", # "forward"="forward", "bidirectional"="both","backward"="backward" ) - if (select.m==txt_forward_step_ascending) "forward" -> select.m - else if (select.m==txt_backward_step_descending) "backward" -> select.m - else if (select.m==txt_bidirectionnal) "both" -> select.m + if (select.m==.dico[["txt_forward_step_ascending"]]) "forward" -> select.m + else if (select.m==.dico[["txt_backward_step_descending"]]) "backward" -> select.m + else if (select.m==.dico[["txt_bidirectionnal"]]) "both" -> select.m else if (select.m=="forward") "forward" -> select.m else if (select.m=="bidirectional") "both" -> select.m else if (select.m=="backward") "backward" -> select.m @@ -306,7 +306,7 @@ if(scale==T || scale==txt_center) { lower = glm.0)) }else{ steps<-stepAIC(glm.r1, direction=select.m) } - Resultats[[txt_selection_method_akaike]]<-steps$anova + Resultats[[.dico[["txt_selection_method_akaike"]]]]<-steps$anova # modele<-as.formula(attributes(steps$anova)$heading[5]) } @@ -339,10 +339,10 @@ if(scale==T || scale==txt_center) { group<-reg.in.output$reg.options$group proba<-reg.in.output$proba - if(!is.null(reg.in.output$reg.options$CV) && reg.in.output$reg.options$CV==TRUE) print(desc_cross_validation_is_not_yet_supported) + if(!is.null(reg.in.output$reg.options$CV) && reg.in.output$reg.options$CV==TRUE) print(.dico[["desc_cross_validation_is_not_yet_supported"]]) - if(any(outlier== txt_complete_dataset)){ - Resultats[[txt_complete_dataset]]<- reg.log.out(data=data, modele=modele, select.m=select.m, step=step, scale=scale, proba=proba, nom=nom) + if(any(outlier== .dico[["txt_complete_dataset"]])){ + Resultats[[.dico[["txt_complete_dataset"]]]]<- reg.log.out(data=data, modele=modele, select.m=select.m, step=step, scale=scale, proba=proba, nom=nom) if(!is.null(group)) { R1<-list() G<-data[,group] @@ -350,14 +350,14 @@ if(scale==T || scale==txt_center) { G<-split(data, G) for(i in 1:length(G)){ resg<- try(reg.log.out(data=G[[i]], modele=modele, select.m=select.m, step=step, scale=scale,proba=proba), silent=T) - if(class(resg)=='try-error') R1[[length(R1)+1]]<-desc_insufficient_obs else R1[[length(R1)+1]]<-resg + if(class(resg)=='try-error') R1[[length(R1)+1]]<-.dico[["desc_insufficient_obs"]] else R1[[length(R1)+1]]<-resg names(R1)[length(R1)]<-names(G)[i] } - Resultats[[txt_complete_dataset]][[txt_group_analysis]]<-R1 + Resultats[[.dico[["txt_complete_dataset"]]]][[.dico[["txt_group_analysis"]]]]<-R1 } } - if(any(outlier==txt_identifying_outliers)|any(outlier==txt_without_outliers)|inf==T){ + if(any(outlier==.dico[["txt_identifying_outliers"]])|any(outlier==.dico[["txt_without_outliers"]])|inf==T){ lm.r1<-glm(modele, data, na.action=na.exclude ,family="binomial") as.character(attributes(terms(modele))$variables)->variables @@ -374,23 +374,23 @@ if(scale==T || scale==txt_center) { data[which(apply(mesure_influence$is.inf, 1, any)),"est.inf"]<-"*" data[order(data$res.student.p.Bonf), ]->data - writeLines(desc_obs_with_asterisk_are_outliers) + writeLines(.dico[["desc_obs_with_asterisk_are_outliers"]]) View(data) suppression<-"yes" outliers<-data.frame() nettoyees<-data while(suppression=="yes"){ - cat (ask_press_enter_to_continue) + cat (.dico[["ask_press_enter_to_continue"]]) line <- readline() sup<-NA while(is.na(sup)){ - sup <- dlgInput(ask_obs_to_remove, 0)$res + sup <- dlgInput(.dico[["ask_obs_to_remove"]], 0)$res if(length(sup)==0) return(regressions()) strsplit(sup, ":")->sup tail(sup[[1]],n=1)->sup as.numeric(sup)->sup - if(is.na(sup)) msgBox(desc_you_must_give_obs_number) + if(is.na(sup)) msgBox(.dico[["desc_you_must_give_obs_number"]]) } if(sup==0) suppression<-"no" else { rbind(outliers, nettoyees[sup,])->outliers @@ -406,22 +406,22 @@ if(scale==T || scale==txt_center) { data[which(data$cook.d<= seuil_cook), ]->nettoyees data[which(data$cook.d>= seuil_cook), ]->outliers cbind(outliers[,variables],outliers$cook.d)->outliers - Resultats$"information"[[desc_outliers_identified_on_4_div_n]] + Resultats$"information"[[.dico[["desc_outliers_identified_on_4_div_n"]]]] } nettoyees->>nettoyees - if(any(outlier== txt_identifying_outliers)){ + if(any(outlier== .dico[["txt_identifying_outliers"]])){ length(data[,1])-length(nettoyees[,1])->N_retire # identifier le nombre d observations retirees sur la base de la distance de cook paste(N_retire/length(data[,1])*100,"%")->Pourcentage_retire # fournit le pourcentage retire - data.frame("N.retirees"=N_retire, txt_percentage_removed_obs=Pourcentage_retire)->Resultats[[txt_identified_outliers_synthesis]] - if(length(outliers)!=0) Resultats[[txt_identifying_outliers]][[desc_identified_outliers]]<-outliers + data.frame("N.retirees"=N_retire, txt_percentage_removed_obs=Pourcentage_retire)->Resultats[[.dico[["txt_identified_outliers_synthesis"]]]] + if(length(outliers)!=0) Resultats[[.dico[["txt_identifying_outliers"]]]][[.dico[["desc_identified_outliers"]]]]<-outliers } - if(any(outlier== txt_without_outliers)) { - if(N_retire!=0 | all(outlier!=txt_complete_dataset)){ - so<- try(reg.log.out(data=nettoyees,modele=modele, select.m=select.m, step=step, scale=scale,proba=proba, nom=paste0(nom,txt_dot_cleaned)),silent=T) - if(class(so)=='try-error') Resultats[[txt_without_outliers]]<-desc_removing_outliers_weakens_sample_size else{ - Resultats[[txt_without_outliers]]<-so + if(any(outlier== .dico[["txt_without_outliers"]])) { + if(N_retire!=0 | all(outlier!=.dico[["txt_complete_dataset"]])){ + so<- try(reg.log.out(data=nettoyees,modele=modele, select.m=select.m, step=step, scale=scale,proba=proba, nom=paste0(nom,.dico[["txt_dot_cleaned"]])),silent=T) + if(class(so)=='try-error') Resultats[[.dico[["txt_without_outliers"]]]]<-.dico[["desc_removing_outliers_weakens_sample_size"]] else{ + Resultats[[.dico[["txt_without_outliers"]]]]<-so if(!is.null(group)) { R1<-list() @@ -431,10 +431,10 @@ if(scale==T || scale==txt_center) { for(i in 1:length(G)){ resg<- try( reg.log.out(data=G[[i]], modele=modele, VC=VC, select.m=select.m, method=method, step=step, group=group, scale=scale,proba=proba), silent=T) - if(class(resg)=='try-error') R1[[length(R1)+1]]<-desc_insufficient_obs else R1[[length(R1)+1]]<-resg + if(class(resg)=='try-error') R1[[length(R1)+1]]<-.dico[["desc_insufficient_obs"]] else R1[[length(R1)+1]]<-resg names(R1)[length(R1)]<-names(G)[i] } - Resultats[[txt_without_outliers]][[txt_group_analysis]]<-R1 + Resultats[[.dico[["txt_without_outliers"]]]][[.dico[["txt_group_analysis"]]]]<-R1 } } @@ -461,9 +461,9 @@ if(scale==T || scale==txt_center) { .add.history(data=data, command=Resultats$Call, nom=nom) - .add.result(Resultats=Resultats, name =paste(txt_log_regression_dot, Sys.time() )) - if(sauvegarde) if(sauvegarde) save(Resultats=Resultats, choix=txt_log_regression_dot, env=.e) - Resultats[[txt_references]]<-ref1(packages) + .add.result(Resultats=Resultats, name =paste(.dico[["txt_log_regression_dot"]], Sys.time() )) + if(sauvegarde) if(sauvegarde) save(Resultats=Resultats, choix=.dico[["txt_log_regression_dot"]], env=.e) + Resultats[[.dico[["txt_references"]]]]<-ref1(packages) if(html) try(ez.html(Resultats), silent=T) return(Resultats) diff --git a/R/save.R b/R/save.R index 58d4edf..9f15b93 100644 --- a/R/save.R +++ b/R/save.R @@ -8,16 +8,16 @@ save <- if(class(test2)== 'try-error') return(ez.install()) Resultats <- list() - if(is.null(Resultats) & exists("ez.results")) Resultats<-ez.results else return(desc_no_result_saved) + if(is.null(Resultats) & exists("ez.results")) Resultats<-ez.results else return(.dico[["desc_no_result_saved"]]) ez.html <-function(ez.results=Resultats) fileHTML<-file.path("file:/", tempdir(), "easieR/Rapport.easieR.html") # TODO not working on Windows I guess? - fileNAME<-dlgInput(ask_filename)$res + fileNAME<-dlgInput(.dico[["ask_filename"]])$res fileNAME<-strsplit(fileNAME, ":") fileNAME<-tail(fileNAME[[1]],n=1) save_html(html=fileHTML, file=fileNAME, background = "white") Resultats<-NULL - Resultats$SAUVEGARDE<-paste(desc_data_saved_in, getwd()) + Resultats$SAUVEGARDE<-paste(.dico[["desc_data_saved_in"]], getwd()) } diff --git a/R/selectionO.R b/R/selectionO.R index 27ed2bb..d74b2e4 100644 --- a/R/selectionO.R +++ b/R/selectionO.R @@ -6,68 +6,68 @@ selectionO <- list()->Resultats choix.data()->data if(length(data)==0) {return(preprocess())} - if(info==TRUE) writeLines(desc_possible_apply_multiple_selection_criterion) - X<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), txt_other_data), multiple = TRUE, - title=txt_variable)$res + if(info==TRUE) writeLines(.dico[["desc_possible_apply_multiple_selection_criterion"]]) + X<-dlgList(c(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), .dico[["txt_other_data"]]), multiple = TRUE, + title=.dico[["txt_variable"]])$res if(length(X)==0 ) return(preprocess()) listes<-data.frame(paste(names(data), "(format :", sapply(data, class), ")", sep=" "), 1:length(data)) subset(listes, listes[,1] %in% X)[,2]->X for(i in 1:length(X)) { if(class(data[,X[i]])=="factor"){ - if(info==TRUE) {writeLines(ask_modalities_to_keep) - writeLines(paste(ask_modalities_for_variable, names(data[,X])[i],"?" ))} + if(info==TRUE) {writeLines(.dico[["ask_modalities_to_keep"]]) + writeLines(paste(.dico[["ask_modalities_for_variable"]], names(data[,X])[i],"?" ))} Y<-dlgList(levels(data[,X[i]]), multiple = TRUE, - title=paste(ask_modalities_for_variable, names(data[,X])[i],"?" ))$res + title=paste(.dico[["ask_modalities_for_variable"]], names(data[,X])[i],"?" ))$res if(length(Y)==0) return(selectionO()) data[data[,X[i]]%in% Y,]->data factor(data[,X[i]])->data[,X [i]]}else{ - if(info==TRUE) {print(ask_criterion_for_obs_to_keep) - writeLines(paste(ask_criterion_for_variable, names(data[,X])[i], "?"))} - dlgList(c(txt_superior_to,txt_superior_or_equal_to, txt_inferior_to, txt_inferior_or_equal_to, txt_equals_to, txt_is_different_from, txt_between, - desc_beyond_with_lower_and_upper), - preselect=NULL, multiple = FALSE, title=paste(ask_criterion_for_variable, names(data[,X])[i], "?"))$res->choix + if(info==TRUE) {print(.dico[["ask_criterion_for_obs_to_keep"]]) + writeLines(paste(.dico[["ask_criterion_for_variable"]], names(data[,X])[i], "?"))} + dlgList(c(.dico[["txt_superior_to"]],.dico[["txt_superior_or_equal_to"]], .dico[["txt_inferior_to"]], .dico[["txt_inferior_or_equal_to"]], .dico[["txt_equals_to"]], .dico[["txt_is_different_from"]], .dico[["txt_between"]], + .dico[["desc_beyond_with_lower_and_upper"]]), + preselect=NULL, multiple = FALSE, title=paste(.dico[["ask_criterion_for_variable"]], names(data[,X])[i], "?"))$res->choix if(length(choix)==0) return(selectionO()) - if(choix==txt_superior_to|choix==txt_inferior_to|choix==txt_equals_to|choix==txt_superior_or_equal_to| - choix==txt_inferior_or_equal_to|choix==txt_is_different_from){ - if(info==TRUE) writeLines(ask_value_for_selected_obs) - seuil<- dlgInput(ask_value, 0)$res + if(choix==.dico[["txt_superior_to"]]|choix==.dico[["txt_inferior_to"]]|choix==.dico[["txt_equals_to"]]|choix==.dico[["txt_superior_or_equal_to"]]| + choix==.dico[["txt_inferior_or_equal_to"]]|choix==.dico[["txt_is_different_from"]]){ + if(info==TRUE) writeLines(.dico[["ask_value_for_selected_obs"]]) + seuil<- dlgInput(.dico[["ask_value"]], 0)$res if(length(seuil)==0) return(selectionO()) else { strsplit(seuil, ":")->seuil tail(seuil[[1]],n=1)->seuil - as.numeric(seuil)->seuil}} else{seuil.inf<- dlgInput(ask_lower_bound, 0)$res - while(length(seuil.inf)==0) {writeLines(desc_specify_lower_bound) - dlgMessage(ask_exit_no_lower_bound_specified, "yesno")$res->quitte + as.numeric(seuil)->seuil}} else{seuil.inf<- dlgInput(.dico[["ask_lower_bound"]], 0)$res + while(length(seuil.inf)==0) {writeLines(.dico[["desc_specify_lower_bound"]]) + dlgMessage(.dico[["ask_exit_no_lower_bound_specified"]], "yesno")$res->quitte if(quitte=="yes") return(selectionO()) - seuil.inf<- dlgInput(ask_lower_bound, 0)$res} + seuil.inf<- dlgInput(.dico[["ask_lower_bound"]], 0)$res} strsplit(seuil.inf, ":")->seuil.inf tail(seuil.inf[[1]],n=1)->seuil.inf as.numeric(seuil.inf)->seuil.inf - seuil.sup<- dlgInput(ask_upper_bound, 0)$res - while(length(seuil.sup)==0) {writeLines(desc_specify_upper_bound) - dlgMessage(ask_exit_no_upper_bound_specified, "yesno")$res->quitte + seuil.sup<- dlgInput(.dico[["ask_upper_bound"]], 0)$res + while(length(seuil.sup)==0) {writeLines(.dico[["desc_specify_upper_bound"]]) + dlgMessage(.dico[["ask_exit_no_upper_bound_specified"]], "yesno")$res->quitte if(quitte=="yes") return(selectionO()) - seuil.sup<- dlgInput(ask_upper_bound, 0)$res} + seuil.sup<- dlgInput(.dico[["ask_upper_bound"]], 0)$res} strsplit(seuil.sup, ":")->seuil.sup tail(seuil.sup[[1]],n=1)->seuil.sup as.numeric(seuil.sup)->seuil.sup} - if(choix==txt_superior_to){data[data[,X[i]]>seuil,]->data} - if(choix==txt_inferior_to){data[data[,X[i]]data} - if(choix==txt_equals_to){data[data[,X[i]]==seuil,]->data} - if(choix==txt_is_different_from){data[data[,X[i]]!=seuil,]->data} - if(choix==txt_superior_or_equal_to){data[data[,X[i]]>=seuil,]->data} - if(choix==txt_inferior_or_equal_to){data[data[,X[i]]<=seuil,]->data} - if(choix==txt_between){data[data[,X[i]]>=seuil.inf & data[,X[i]]<=seuil.sup,]->data} - if(choix==desc_beyond_with_lower_and_upper){data[data[,X[i]]seuil.sup,]->data} + if(choix==.dico[["txt_superior_to"]]){data[data[,X[i]]>seuil,]->data} + if(choix==.dico[["txt_inferior_to"]]){data[data[,X[i]]data} + if(choix==.dico[["txt_equals_to"]]){data[data[,X[i]]==seuil,]->data} + if(choix==.dico[["txt_is_different_from"]]){data[data[,X[i]]!=seuil,]->data} + if(choix==.dico[["txt_superior_or_equal_to"]]){data[data[,X[i]]>=seuil,]->data} + if(choix==.dico[["txt_inferior_or_equal_to"]]){data[data[,X[i]]<=seuil,]->data} + if(choix==.dico[["txt_between"]]){data[data[,X[i]]>=seuil.inf & data[,X[i]]<=seuil.sup,]->data} + if(choix==.dico[["desc_beyond_with_lower_and_upper"]]){data[data[,X[i]]seuil.sup,]->data} } } - fichier<- dlgInput(ask_filename, txt_selection)$res + fichier<- dlgInput(.dico[["ask_filename"]], .dico[["txt_selection"]])$res if(length(fichier)==0) return(selectionO()) strsplit(fichier, ":")->fichier tail(fichier[[1]],n=1)->fichier assign(x=fichier, value=data, envir=.GlobalEnv) - View(data, txt_selected_data) - Resultats<-paste(desc_selected_obs_are_in, fichier) + View(data, .dico[["txt_selected_data"]]) + Resultats<-paste(.dico[["desc_selected_obs_are_in"]], fichier) return(Resultats) } diff --git a/R/stat.desc.R b/R/stat.desc.R index b275dce..0d4548c 100644 --- a/R/stat.desc.R +++ b/R/stat.desc.R @@ -17,27 +17,27 @@ stat.desc <- # choix X if(!is.null(x)) dial<-F else dial<-T - msg1<-ask_variables_for_description_statistics - .var.type(X=X, info=T, data=data, type=NULL, message=msg1,multiple=T, title=ask_variable)->X1 + msg1<-.dico[["ask_variables_for_description_statistics"]] + .var.type(X=X, info=T, data=data, type=NULL, message=msg1,multiple=T, title=.dico[["ask_variable"]])->X1 if(is.null(X1)) return(NULL) X1$X->x setdiff(names(data), x)->diff if(length(diff)==0 & !is.null(groupes)) { - msgBox(desc_cannot_group_variables_because_not_described) + msgBox(.dico[["desc_cannot_group_variables_because_not_described"]]) groupes<-NULL } if(length(diff)>0){ if(dial){ - writeLines(ask_subgroups) - groupes<-dlgList(c(txt_yes, txt_no), multiple = F, preselect=txt_no, title=ask_specify_groups)$res + writeLines(.dico[["ask_subgroups"]]) + groupes<-dlgList(c(.dico[["txt_yes"]], .dico[["txt_no"]]), multiple = F, preselect=.dico[["txt_no"]], title=.dico[["ask_specify_groups"]])$res if(length(groupes)==0) {stat.desc.in(x=X, groupes=NULL, data=NULL, tr=tr, type=type,save=save)->Resultats return(Resultats)} - if(groupes==txt_no) groupes<-NULL + if(groupes==.dico[["txt_no"]]) groupes<-NULL } if(!is.null(groupes)){ - msg2<-ask_variables_used_for_groups + msg2<-.dico[["ask_variables_used_for_groups"]] .var.type(X=groupes, info=T, data=data, type="factor", message=msg2,multiple=T, title="Variable(s) groupes ?", out=x)->groupes if(is.null(groupes)){ stat.desc.in(x=X, groupes=NULL, data=NULL, tr=tr, type=type,save=save)->Resultats @@ -49,19 +49,19 @@ stat.desc <- } if(dial==T | tr>1 | tr<0 | (type %in% 1:3==F) ) { - writeLines(desc_flattening_and_asymetry_configurable) - options<-dlgList(c(txt_yes, txt_no), multiple = F, preselect=txt_no, title=ask_specify_other_options)$res + writeLines(.dico[["desc_flattening_and_asymetry_configurable"]]) + options<-dlgList(c(.dico[["txt_yes"]], .dico[["txt_no"]]), multiple = F, preselect=.dico[["txt_no"]], title=.dico[["ask_specify_other_options"]])$res if(length(options)==0) { stat.desc.in(x=X, groupes=NULL, data=NULL, tr=tr, type=type,save=save)->Resultats return(Resultats) } - if(options==txt_yes) {opts2<-NA + if(options==.dico[["txt_yes"]]) {opts2<-NA while(any(is.na(opts2))){ - #dlgForm(list(txt_troncature_num=0.1, "Type de skew et kurtosis, doit se situer entre 1 et 3:NUM"=3), ask_troncature_threshold)$res->opts2 - name <- c(txt_troncature_num,desc_skew_and_kurtosis_between_1_and_3) + #dlgForm(list(txt_troncature_num=0.1, "Type de skew et kurtosis, doit se situer entre 1 et 3:NUM"=3), .dico[["ask_troncature_threshold"]])$res->opts2 + name <- c(.dico[["txt_troncature_num"]],.dico[["desc_skew_and_kurtosis_between_1_and_3"]]) vals <- c(0.1, 3) Form <- setNames(as.list(vals), name) - dlgForm(Form, ask_troncature_threshold)$res->opts2 + dlgForm(Form, .dico[["ask_troncature_threshold"]])$res->opts2 if(opts2[[1]]>0.5 | opts2[[1]]<0 ) NA->opts2[[1]] else tr<-opts2[[1]] if(opts2[[2]]%in% 1:3) type<-opts2[[2]] else opts2[[2]]<-NA @@ -98,10 +98,10 @@ stat.desc <- paste0("'), groupes =c('",groupes ,"'), data=")->groupes} paste0("stat.desc(X=c('", X, groupes, data.in$nom1, ",tr=" , tr, ",type=", type, ", plot=", plot, ", ref=", ref,", html=",html, ")")->Resultats$Call .add.history(data=data.in$data, command=Resultats$Call, nom=data.in$nom1) - .add.result(Resultats=Resultats, name =paste(txt_descriptive_statistics, Sys.time() )) + .add.result(Resultats=Resultats, name =paste(.dico[["txt_descriptive_statistics"]], Sys.time() )) - if(data.in$sauvegarde==TRUE) save(Resultats=Resultats ,choix =paste(desc_descriptive_statistics_on,data.in$nom1 ), env=.e) - if(ref) ref1(packages)->Resultats[[desc_references]] + if(data.in$sauvegarde==TRUE) save(Resultats=Resultats ,choix =paste(.dico[["desc_descriptive_statistics_on"]],data.in$nom1 ), env=.e) + if(ref) ref1(packages)->Resultats[[.dico[["desc_references"]]]] if(html) try(ez.html(Resultats), silent=T) return(Resultats) } diff --git a/R/teaching.R b/R/teaching.R index 3f8ed90..df5f067 100644 --- a/R/teaching.R +++ b/R/teaching.R @@ -2,7 +2,7 @@ teaching <- function(){ tcl<-function(){ clt.examp(1) - msgBox(ask_are_you_ready) + msgBox(.dico[["ask_are_you_ready"]]) for(i in 1:50){ clt.examp(i*2) Sys.sleep(1) @@ -13,35 +13,35 @@ teaching <- try(lapply(packages, library, character.only=T), silent=T)->test2 if(class(test2)== 'try-error') return(ez.install()) - choix <- dlgList(c(txt_understanding_confidance_interval, txt_understanding_alpha_and_power, - txt_understanding_corr, - txt_understanding_central_limit_theorem,txt_understanding_corr_2, - txt_understanding_prev_sens_specificity, - txt_understanding_prev_sens_specificity_2, - txt_understanding_negative_positive_predic_power, - txt_understanding_bayesian_inference, - txt_understanding_likelihood, - txt_understanding_heterogenous_variance_effects), preselect=NULL, multiple = FALSE, title=ask_what_do_you_want)$res + choix <- dlgList(c(.dico[["txt_understanding_confidance_interval"]], .dico[["txt_understanding_alpha_and_power"]], + .dico[["txt_understanding_corr"]], + .dico[["txt_understanding_central_limit_theorem"]],.dico[["txt_understanding_corr_2"]], + .dico[["txt_understanding_prev_sens_specificity"]], + .dico[["txt_understanding_prev_sens_specificity_2"]], + .dico[["txt_understanding_negative_positive_predic_power"]], + .dico[["txt_understanding_bayesian_inference"]], + .dico[["txt_understanding_likelihood"]], + .dico[["txt_understanding_heterogenous_variance_effects"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_what_do_you_want"]])$res if(length(choix)==0) return(easieR()) - if (choix==txt_understanding_confidance_interval) ci.examp() # peut etre completer par des arguments - if (choix==txt_understanding_central_limit_theorem) tcl() - if (choix==txt_understanding_prev_sens_specificity) plotFagan2() - if (choix==txt_understanding_bayesian_inference) plotFagan() - if (choix==txt_understanding_likelihood) mle.demo() #des arguments peuvent etre utilises - if (choix==txt_understanding_alpha_and_power) run.power.examp(hscale=1.5, vscale=1.5, wait=FALSE) - if (choix==txt_understanding_corr) put.points.demo() - if (choix==txt_understanding_heterogenous_variance_effects) { - writeLines(desc_with_two_equal_means_ratio_must_be_5_percent) + if (choix==.dico[["txt_understanding_confidance_interval"]]) ci.examp() # peut etre completer par des arguments + if (choix==.dico[["txt_understanding_central_limit_theorem"]]) tcl() + if (choix==.dico[["txt_understanding_prev_sens_specificity"]]) plotFagan2() + if (choix==.dico[["txt_understanding_bayesian_inference"]]) plotFagan() + if (choix==.dico[["txt_understanding_likelihood"]]) mle.demo() #des arguments peuvent etre utilises + if (choix==.dico[["txt_understanding_alpha_and_power"]]) run.power.examp(hscale=1.5, vscale=1.5, wait=FALSE) + if (choix==.dico[["txt_understanding_corr"]]) put.points.demo() + if (choix==.dico[["txt_understanding_heterogenous_variance_effects"]]) { + writeLines(.dico[["desc_with_two_equal_means_ratio_must_be_5_percent"]]) run.Pvalue.norm.sim() } - if (choix==txt_understanding_prev_sens_specificity_2) roc.demo() - if (choix==txt_understanding_corr_2) run.cor2.examp() - if (choix==txt_understanding_negative_positive_predic_power) { + if (choix==.dico[["txt_understanding_prev_sens_specificity_2"]]) roc.demo() + if (choix==.dico[["txt_understanding_corr_2"]]) run.cor2.examp() + if (choix==.dico[["txt_understanding_negative_positive_predic_power"]]) { for(i in seq(1,11,2)) { SensSpec.demo(sens=0.95, spec=0.99, prev=0.01, step=i) # on peut modifier sensibilite et specificite if( interactive() ) { - readline(ask_press_enter_to_continue) + readline(.dico[["ask_press_enter_to_continue"]]) } } } diff --git a/R/test.t.R b/R/test.t.R index bab3806..145630a 100644 --- a/R/test.t.R +++ b/R/test.t.R @@ -1,21 +1,21 @@ test.t <- function(X=NULL, Y=NULL, group=NULL, choix=NULL, - sauvegarde=F, outlier=c(txt_complete_dataset, txt_identifying_outliers,txt_without_outliers), z=NULL, data=NULL, + sauvegarde=F, outlier=c(.dico[["txt_complete_dataset"]], .dico[["txt_identifying_outliers"]],.dico[["txt_without_outliers"]]), z=NULL, data=NULL, alternative="two.sided", mu=NULL, formula=NULL, n.boot=NULL, - param=c(txt_param_test, txt_non_param_test,txt_robusts_tests_with_bootstraps, - txt_bayesian_factors), info=TRUE, rscale=0.707, html=T){ + param=c(.dico[["txt_param_test"]], .dico[["txt_non_param_test"]],.dico[["txt_robusts_tests_with_bootstraps"]], + .dico[["txt_bayesian_factors"]]), info=TRUE, rscale=0.707, html=T){ # X : Character specifying the dependant variable in dataframe. # Y : character specifying either a two levels factor in dataframe or a numeric variable if paired is TRUE # group : Factor vector allowing to decompose analysis by group in one sample t test - # choix : Character. One among c(txt_comparison_to_norm, txt_two_paired_samples,txt_two_independant_samples) + # choix : Character. One among c(.dico[["txt_comparison_to_norm"]], .dico[["txt_two_paired_samples"]],.dico[["txt_two_independant_samples"]]) # sauvegarde : logical. Should the results be saved ? - # outlier : character. One or several possibilities among c(txt_complete_dataset, txt_identifying_outliers, txt_without_outliers) + # outlier : character. One or several possibilities among c(.dico[["txt_complete_dataset"]], .dico[["txt_identifying_outliers"]], .dico[["txt_without_outliers"]]) # z : if NULL and the identification/exclusion of outlier is desired, outlier are identified on Grubbs' test. If z is numeric, outliers are identified on abs(z) # data : data on which analysis has to be performed. # alternative : one among c("greater", "lower", "two.sided"). Two sided is default. # formula : a formula of the form dependant.variable~independant.variable # n.boot : number of bootstrap. Must be a positive value - # param : character vector with one or several choices among c(txt_param_test, txt_non_param_test,txt_robusts_tests_with_bootstraps, txt_bayesian_factors) + # param : character vector with one or several choices among c(.dico[["txt_param_test"]], .dico[["txt_non_param_test"]],.dico[["txt_robusts_tests_with_bootstraps"]], .dico[["txt_bayesian_factors"]]) # info : logical. If dialog box are used, Should information be printed in the console # rscale : if desc_bayesian_factors_chosen_inparam", rscale is the prior scale. See t.testBF for more information @@ -25,54 +25,54 @@ test.t <- Resultats<-list() if(!is.null(choix)) dial<-F else dial<-T - if(is.null(choix) || (choix %in%c(txt_comparison_to_norm, txt_two_paired_samples,txt_two_independant_samples)==FALSE)){ - if(info) writeLines(ask_t_test_type) - choix<-dlgList(c(txt_comparison_to_norm, txt_two_paired_samples, - txt_two_independant_samples), preselect=NULL, multiple = FALSE, title=txt_t_test_choice)$res + if(is.null(choix) || (choix %in%c(.dico[["txt_comparison_to_norm"]], .dico[["txt_two_paired_samples"]],.dico[["txt_two_independant_samples"]])==FALSE)){ + if(info) writeLines(.dico[["ask_t_test_type"]]) + choix<-dlgList(c(.dico[["txt_comparison_to_norm"]], .dico[["txt_two_paired_samples"]], + .dico[["txt_two_independant_samples"]]), preselect=NULL, multiple = FALSE, title=.dico[["txt_t_test_choice"]])$res if(length(choix)==0) return(NULL) } data<-choix.data(data=data, info=info, nom=T) if(length(data)==0) return(NULL) nom<-data[[1]] data<-data[[2]] - if(is.null(Y) || class(data[,Y]) == "factor") format<-"long" else format<-txt_large + if(is.null(Y) || class(data[,Y]) == "factor") format<-"long" else format<-.dico[["txt_large"]] if(is.null(formula)){ - if(choix==txt_two_paired_samples){ + if(choix==.dico[["txt_two_paired_samples"]]){ if(dial){ if(info==TRUE){ temps1<-1:3 temps2<-4:6 data.frame(txt_time1=temps1,txt_time2=temps2)->large - data.frame(c(rep(txt_time1,3),rep(txt_time2, 3)), 1:6)->long + data.frame(c(rep(.dico[["txt_time1"]],3),rep(.dico[["txt_time2"]], 3)), 1:6)->long names(long)<-c("moment","mesure") - writeLines(desc_this_is_large_format) + writeLines(.dico[["desc_this_is_large_format"]]) print(large) - writeLines(desc_this_is_long_format) + writeLines(.dico[["desc_this_is_long_format"]]) print(long)} - format<-dlgList(c(txt_large, "long"), preselect=txt_large, multiple = FALSE, title=ask_data_format)$res + format<-dlgList(c(.dico[["txt_large"]], "long"), preselect=.dico[["txt_large"]], multiple = FALSE, title=.dico[["ask_data_format"]])$res if(length(format)==0) { Resultats<-test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL) return(Resultats) } }} - if(format==txt_large) { - msg3<-ask_time1 - msg4<-ask_time2 - title1<-txt_time_1 - title2<-txt_time_2 + if(format==.dico[["txt_large"]]) { + msg3<-.dico[["ask_time1"]] + msg4<-.dico[["ask_time2"]] + title1<-.dico[["txt_time_1"]] + title2<-.dico[["txt_time_2"]] } else{ - msg3<-ask_chose_dependant_variable - msg4<-ask_independant_variable - title1<-txt_dependant_variables - title2<-txt_independant_variable + msg3<-.dico[["ask_chose_dependant_variable"]] + msg4<-.dico[["ask_independant_variable"]] + title1<-.dico[["txt_dependant_variables"]] + title2<-.dico[["txt_independant_variable"]] } - if(choix==txt_two_paired_samples) {multiple<-F + if(choix==.dico[["txt_two_paired_samples"]]) {multiple<-F if(length(X)>1){ - msgBox(desc_single_dependant_variable_allowed_in_paired_t) + msgBox(.dico[["desc_single_dependant_variable_allowed_in_paired_t"]]) X<-NULL }}else multiple<-T X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=multiple, title=title1, out=NULL) if(is.null(X)) { @@ -82,8 +82,8 @@ test.t <- data<-X$data X1<-X$X - if(choix!=txt_comparison_to_norm){ - if(choix==txt_two_paired_samples && format==txt_large) type<-"numeric" else type<-"factor" + if(choix!=.dico[["txt_comparison_to_norm"]]){ + if(choix==.dico[["txt_two_paired_samples"]] && format==.dico[["txt_large"]]) type<-"numeric" else type<-"factor" Y<-.var.type(X=Y, info=info, data=data, type=type, check.prod=F, message=msg4, multiple=FALSE, title=title2, out=X1) if(is.null(Y)) { test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", @@ -92,7 +92,7 @@ test.t <- data<-Y$data Y<-Y$X if(class(data[,Y])=="factor" && nlevels(data[,Y])!=2) { - msgBox(desc_two_modalities_for_independante_categorial_variable) + msgBox(.dico[["desc_two_modalities_for_independante_categorial_variable"]]) test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL)->Resultats return(Resultats) @@ -107,11 +107,11 @@ test.t <- - if(choix==txt_two_paired_samples){ - if(format==txt_large){ + if(choix==.dico[["txt_two_paired_samples"]]){ + if(format==.dico[["txt_large"]]){ if(dial){ - if(info==TRUE)writeLines(ask_independant_variable_name) - nomVI <- dlgInput(ask_independant_variable_name, "Moment")$res + if(info==TRUE)writeLines(.dico[["ask_independant_variable_name"]]) + nomVI <- dlgInput(.dico[["ask_independant_variable_name"]], "Moment")$res if(length(nomVI)==0) { Resultats<-test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL) @@ -119,15 +119,15 @@ test.t <- } strsplit(nomVI, ":")->nomVI tail(nomVI[[1]],n=1)->nomVI - if(info==TRUE) writeLines(ask_dependant_variable_name) - nomVD <- dlgInput(ask_dependant_variable_name, txt_result)$res + if(info==TRUE) writeLines(.dico[["ask_dependant_variable_name"]]) + nomVD <- dlgInput(.dico[["ask_dependant_variable_name"]], .dico[["txt_result"]])$res if(length(nomVD)==0) { Resultats<-test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL) return(Resultats) } } else { - nomVD<-txt_result + nomVD<-.dico[["txt_result"]] nomVI<-"Moment" } strsplit(nomVD, ":")->nomVD @@ -141,9 +141,9 @@ test.t <- } if(format=="long") { if( length(unique(table(data[,Y])))!=1) { - msgBox(desc_non_equal_independant_variable_modalities_occurrence) - msg4<-ask_id_variable - ID<-.var.type(X=NULL, info=info, data=data, type=type, check.prod=F, message=msg4, multiple=multiple, title=txt_id_variable, out=c(X1,Y)) + msgBox(.dico[["desc_non_equal_independant_variable_modalities_occurrence"]]) + msg4<-.dico[["ask_id_variable"]] + ID<-.var.type(X=NULL, info=info, data=data, type=type, check.prod=F, message=msg4, multiple=multiple, title=.dico[["txt_id_variable"]], out=c(X1,Y)) if(is.null(ID)) { test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL)->Resultats @@ -160,11 +160,11 @@ test.t <- } - if(choix==txt_comparison_to_norm){ - writeLines(ask_specify_norm_value) + if(choix==.dico[["txt_comparison_to_norm"]]){ + writeLines(.dico[["ask_specify_norm_value"]]) if(class(mu) !="numeric") mu<-NA while(is.na(mu)){ - mu <- dlgInput(ask_norm_value, 0)$res + mu <- dlgInput(.dico[["ask_norm_value"]], 0)$res if(length(mu)==0) { test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL)->Resultats @@ -173,34 +173,34 @@ test.t <- strsplit(mu, ":")->mu tail(mu[[1]],n=1)->mu as.numeric(mu)->mu - if(is.na(mu)) msgBox(desc_norm_must_be_numeric) + if(is.na(mu)) msgBox(.dico[["desc_norm_must_be_numeric"]]) } if(dial){ - if(info==TRUE) writeLines(desc_bilateral_superior_inferior_test_t) - dlgList(c(txt_bilateral, txt_superior, txt_inferior), preselect=NULL, multiple = FALSE, title=txt_means_comparison)$res->alternative + if(info==TRUE) writeLines(.dico[["desc_bilateral_superior_inferior_test_t"]]) + dlgList(c(.dico[["txt_bilateral"]], .dico[["txt_superior"]], .dico[["txt_inferior"]]), preselect=NULL, multiple = FALSE, title=.dico[["txt_means_comparison"]])$res->alternative if(length(alternative)==0) { test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL)->Resultats return(Resultats) #} else car::recode(alternative, "'Bilateral'= 'two.sided';'Superieur'='greater'; 'Inferieur'='less'")->alternative # TODO check here for troubles translation } else { - if (alternative == txt_bilateral) { 'two.sided' -> alternative } - else if (alternative == txt_superior) { 'greater' -> alternative } - else if (alternative == txt_inferior) { 'less' -> alternative } + if (alternative == .dico[["txt_bilateral"]]) { 'two.sided' -> alternative } + else if (alternative == .dico[["txt_superior"]]) { 'greater' -> alternative } + else if (alternative == .dico[["txt_inferior"]]) { 'less' -> alternative } else { print('[ERROR] Unknown alternative (./test.t.R)') } } - if(info==TRUE) writeLines(desc_corr_group_analysis_spec) - dlgList(c(txt_yes, txt_no), preselect=txt_no, multiple = FALSE, title=ask_analysis_by_group)$res->par.groupe + if(info==TRUE) writeLines(.dico[["desc_corr_group_analysis_spec"]]) + dlgList(c(.dico[["txt_yes"]], .dico[["txt_no"]]), preselect=.dico[["txt_no"]], multiple = FALSE, title=.dico[["ask_analysis_by_group"]])$res->par.groupe if(length(par.groupe)==0) { test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative="two.sided", formula=NULL,n.boot=NULL, rscale=NULL)->Resultats return(Resultats) } - msg5<-ask_chose_categorial_ranking_factor - if(par.groupe==txt_yes){group<-.var.type(X=group, info=info, data=data, type="factor", check.prod=F, message=msg5, multiple=FALSE, title=txt_variables, out=X1) + msg5<-.dico[["ask_chose_categorial_ranking_factor"]] + if(par.groupe==.dico[["txt_yes"]]){group<-.var.type(X=group, info=info, data=data, type="factor", check.prod=F, message=msg5, multiple=FALSE, title=.dico[["txt_variables"]], out=X1) if(length(group)==0) { test.t.in(X=NULL, Y=NULL, data=NULL, choix=NULL, param=NULL, outlier=NULL, sauvegarde=NULL, info=T, group=NULL,alternative='two.sided', formula=NULL,n.boot=NULL, rscale=NULL)->Resultats return(Resultats)} @@ -209,8 +209,8 @@ test.t <- } } } - msg.options1<- desc_param_is_t_test - msg.options2<- desc_non_param_is_wilcoxon_or_mann_withney + msg.options1<- .dico[["desc_param_is_t_test"]] + msg.options2<- .dico[["desc_non_param_is_wilcoxon_or_mann_withney"]] options<-.ez.options(options=c('choix',"outlier"), n.boot=n.boot,param=T, non.param=T, robust=T, Bayes=T, msg.options1=msg.options1, msg.options2=msg.options2, info=info, dial=dial, choix=param,sauvegarde=sauvegarde, outlier=outlier, rscale=rscale) @@ -220,23 +220,23 @@ test.t <- return(Resultats) } Resultats$choix<-choix - Resultats$nom<-ifelse(format==txt_large, paste0(nom,".format.long"), nom) + Resultats$nom<-ifelse(format==.dico[["txt_large"]], paste0(nom,".format.long"), nom) Resultats$data<-data Resultats$X<-X1 if(exists("Y")) Resultats$Y<-Y if(exists("mu")) Resultats$mu<-mu - if(exists(txt_alternative)) Resultats$alternative<-alternative + if(exists(.dico[["txt_alternative"]])) Resultats$alternative<-alternative if(exists("group")) Resultats$group<-group Resultats$options<-options return(Resultats) } - norme<-function(X, mu, data, param=c("param", "non param", txt_robusts), group=NULL, alternative='two.sided', n.boot=NULL, rscale=0.707){ + norme<-function(X, mu, data, param=c("param", "non param", .dico[["txt_robusts"]]), group=NULL, alternative='two.sided', n.boot=NULL, rscale=0.707){ if(class(data)!="data.frame") {data<-data.frame(data) names(data)[1]<-X} Resultats<-list() .e <- environment() - Resultats[[txt_descriptive_statistics]]<-.stat.desc.out(X=X, groupes=NULL, data=data, tr=.1, type=3, plot=F) + Resultats[[.dico[["txt_descriptive_statistics"]]]]<-.stat.desc.out(X=X, groupes=NULL, data=data, tr=.1, type=3, plot=F) cutoff <- data.frame(x = c(-Inf, Inf), y = mu, cutoff = factor(mu) ) p2<- ggplot(data) p2<-p2+ eval(parse(text=paste0("aes(x=factor(0), y=", X,")"))) + geom_violin() @@ -245,21 +245,21 @@ test.t <- p2<-p2 + stat_summary(fun.data=data_summary,geom="pointrange", color="red", size=0.50,position=position_dodge(0.9)) p2<-p2 + geom_dotplot(binaxis='y', stackdir='center', dotsize=1/4) p2<-p2 + theme(legend.position="none") - p2<-p2+theme(plot.title = element_text(size = 12))+ggtitle(txt_mean_sd) + p2<-p2+theme(plot.title = element_text(size = 12))+ggtitle(.dico[["txt_mean_sd"]]) # print(p2) - Resultats[[txt_descriptive_statistics]]$Graphique<-p2 + Resultats[[.dico[["txt_descriptive_statistics"]]]]$Graphique<-p2 - if(!is.null(group)) {Resultats[[txt_descriptive_statistics_by_group]]<-.stat.desc.out(X=X, groupes=group, data=data, tr=.1, type=3, plot=T) } - if(any(param=="param") | any(param==txt_param_tests)){ - Resultats[[txt_normality_tests]]<-.normalite(data=data, X=X, Y=NULL) + if(!is.null(group)) {Resultats[[.dico[["txt_descriptive_statistics_by_group"]]]]<-.stat.desc.out(X=X, groupes=group, data=data, tr=.1, type=3, plot=T) } + if(any(param=="param") | any(param==.dico[["txt_param_tests"]])){ + Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=data, X=X, Y=NULL) t.test(data[,X], mu = mu, paired = FALSE, conf.level = 0.95, alternative=alternative)->ttest ttest$statistic^2/( ttest$statistic^2+ ttest$parameter)->R_carre cohensD(data[,X], mu=mu)->dc data.frame("t test"=round(ttest$statistic,3), txt_df=ttest$parameter, txt_p_dot_val=round(ttest$p.value,4), txt_ci_inferior_limit_dot=ttest$conf.int[[1]], txt_ci_superior_limit_dot=ttest$conf.int[[2]], txt_r_dot_square=round(R_carre,4), txt_cohen_d=round(dc,3))->ttest - c("t test", txt_df, txt_p_dot_val, txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot, txt_r_dot_square, txt_cohen_d)->names(ttest) + c("t test", .dico[["txt_df"]], .dico[["txt_p_dot_val"]], .dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]], .dico[["txt_r_dot_square"]], .dico[["txt_cohen_d"]])->names(ttest) dimnames(ttest)[1]<-" " - ttest->Resultats[[txt_student_t_test_norm]] + ttest->Resultats[[.dico[["txt_student_t_test_norm"]]]] if(!is.null(group)){ data<-data[complete.cases(data[,group]),] func <- function(data, moy=mu){ @@ -273,23 +273,23 @@ test.t <- IC.sup=ttest$conf.int[[2]], txt_r_dot_square=round(R_carre,4), D.Cohen=round(dc,3)) - names(current_df) <- c("test.t",txt_df,txt_p_dot_val,txt_ci_inferior_limit_dot,txt_ci_inferior_limit_dot,txt_r_dot_square,txt_cohen_d) + names(current_df) <- c("test.t",.dico[["txt_df"]],.dico[["txt_p_dot_val"]],.dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_r_dot_square"]],.dico[["txt_cohen_d"]]) return(current_df) } data.frame(data[,X])->Y ddply(.data=Y, .(data[,group]), func)->t.groupes - t.groupes->Resultats[[txt_student_t_by_group]]}} + t.groupes->Resultats[[.dico[["txt_student_t_by_group"]]]]}} - if(any(param=="Bayes") | any(param==txt_bayesian_factors) ){ - if(all(param!="param") & all(param!=txt_param_tests)) Resultats[[txt_normality_tests]]<-.normalite(data=data, X=X, Y=NULL) + if(any(param=="Bayes") | any(param==.dico[["txt_bayesian_factors"]]) ){ + if(all(param!="param") & all(param!=.dico[["txt_param_tests"]])) Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=data, X=X, Y=NULL) BF<-ttestBF(x = data[,X], mu=mu , paired=FALSE, rscale=rscale) BF<-extractBF(BF, onlybf=F) BF<-data.frame(txt_bayesian_factor=c(round(BF$bf,5), round((1/BF$bf),5)), txt_error=round(c( BF$error, BF$error),5)) - names(BF)<-c(txt_bayesian_factor, txt_error) - dimnames(BF)[[1]]<-c(txt_supports_alternative, txt_supports_null) - Resultats[[txt_bayesian_factors]]<-BF + names(BF)<-c(.dico[["txt_bayesian_factor"]], .dico[["txt_error"]]) + dimnames(BF)[[1]]<-c(.dico[["txt_supports_alternative"]], .dico[["txt_supports_null"]]) + Resultats[[.dico[["txt_bayesian_factors"]]]]<-BF if(!is.null(group)){ func <- function(data, moy=mu, scale=rscale){ ttestBF(data, mu = moy, rscale=scale)->BF @@ -298,8 +298,8 @@ test.t <- } BFgroup<-tapply(X=data[,X], data[,group], func,scale=rscale, moy=mu) BFgroup<-matrix(unlist(BFgroup), ncol=2, byrow=T) - dimnames(BFgroup)<-list(levels(data[,group]), c("FB", txt_error)) - BFgroup->Resultats[[txt_bayesian_factor_by_group]] + dimnames(BFgroup)<-list(levels(data[,group]), c("FB", .dico[["txt_error"]])) + BFgroup->Resultats[[.dico[["txt_bayesian_factor_by_group"]]]] } samples<-ttestBF(x = data[,X], mu=mu , paired=FALSE, rscale=rscale, posterior=T, iterations = ifelse(is.null(n.boot), 1000, n.boot)) plot(samples[,"mu"]) @@ -314,10 +314,10 @@ test.t <- } SBF<-data.frame("n"=rep(5:length(data[,X]), each=3 ),"BF"= bfs, - "rscale"=factor(rep(c("moyen", txt_large, txt_ultrawide), length.out= 3*(length(data[,X])-4) ))) + "rscale"=factor(rep(c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]), length.out= 3*(length(data[,X])-4) ))) names(SBF)<-c("n", "BF", "rscale") - reorder( c("moyen", txt_large, txt_ultrawide),levels(SBF$rscale))->levels(SBF$rscale) - Resultats[[txt_bayesian_factors_sequential]]<-.plotSBF(SBF) + reorder( c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]),levels(SBF$rscale))->levels(SBF$rscale) + Resultats[[.dico[["txt_bayesian_factors_sequential"]]]]<-.plotSBF(SBF) ##### Debut du graphique Bayes Factor Robustness Check @@ -341,7 +341,7 @@ test.t <- # do the Bayes factor plot plot(cauchyRates, bayesFactors, type = "l", lwd = 2, col = "gray48", ylim = c(0, max(bayesFactors)), xaxt = "n", - xlab = txt_cauchy_prior_width, ylab = txt_bayes_factor_10) + xlab = .dico[["txt_cauchy_prior_width"]], ylab = .dico[["txt_bayes_factor_10"]]) abline(h = 0, lwd = 1) abline(h = 6, col = "black", lty = 2, lwd = 2) axis(1, at = seq(0, 1.5, 0.25)) @@ -356,7 +356,7 @@ test.t <- } - if(any(param=="non param")| any(param==txt_non_parametric_test)){ + if(any(param=="non param")| any(param==.dico[["txt_non_parametric_test"]])){ wilcox.test(x= data[,X], y = NULL, alternative = alternative, mu = mu, paired = FALSE, exact = T, conf.int = TRUE, conf.level = 0.95) @@ -365,7 +365,7 @@ test.t <- r<-z/(length(data[,X]))^0.5 Resultats$Wilcoxon<- data.frame("Wilcoxon W"=WT$statistic, txt_p_dot_val=round(WT$p.value,4), "z"=round(z,4), "r"=round(r,4), txt_ci_inferior_limit_dot=WT$conf.int[1],txt_ci_superior_limit_dot=WT$conf.int[2]) - names(Resultats$Wilcoxon) <- c("Wilcoxon W", txt_p_dot_val, "z", "r", txt_ci_inferior_limit_dot,txt_ci_superior_limit_dot) + names(Resultats$Wilcoxon) <- c("Wilcoxon W", .dico[["txt_p_dot_val"]], "z", "r", .dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_ci_superior_limit_dot"]]) if(!is.null(group)){ func <- function(data,Y=X, moy=mu, alt=alternative){ @@ -376,87 +376,87 @@ test.t <- } ddply(.data=data, .(data[, group]), func)->Wilcox.groupes - Wilcox.groupes->Resultats[[txt_wilcoxon_by_group]] + Wilcox.groupes->Resultats[[.dico[["txt_wilcoxon_by_group"]]]] } } - if(any(param==txt_robusts| any(param==txt_robusts_tests_with_bootstraps))){ + if(any(param==.dico[["txt_robusts"]]| any(param==.dico[["txt_robusts_tests_with_bootstraps"]]))){ try( round(unlist(WRS::trimci(data[,X],tr=.2,alpha=.05, null.value=mu)),4), silent=T)->m.tr if(class(m.tr)!='try-error'){ - names(m.tr)<-c(txt_ci_inferior_limit_dot,txt_ci_superior_limit_dot, txt_truncated_m,"test.t", "se",txt_p_dot_val,"n") + names(m.tr)<-c(.dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_ci_superior_limit_dot"]], .dico[["txt_truncated_m"]],"test.t", "se",.dico[["txt_p_dot_val"]],"n") #m.tr->Resultats$'Test sur la moyenne tronquee a 0.2' - m.tr->Resultats[[txt_truncated_mean_0_2]] + m.tr->Resultats[[.dico[["txt_truncated_mean_0_2"]]]] data[,X]->x try(WRS::trimcibt(x, tr=.2,alpha=.05,nboot=n.boot,plotit=T,op=3)$ci, silent=T)->trimci try(WRS::mestci(x,alpha=.05,nboot=n.boot,bend=1.28,os=F),silent=T)->M.estimator try(WRS:: momci(x,alpha=.05,nboot=n.boot),silent=T)->MoM IC.robustes<-data.frame() if(class(trimci)!='try-error') {IC.robustes<-rbind(IC.robustes,trimci) - dimnames(IC.robustes)[[1]][1]<-txt_bootstrap_t_method} + dimnames(IC.robustes)[[1]][1]<-.dico[["txt_bootstrap_t_method"]]} if(class(M.estimator)!='try-error') {IC.robustes<-rbind(IC.robustes,M.estimator$ci) dimnames(IC.robustes)[[1]][length(IC.robustes[,1])]<-"M-estimator"} if(class(MoM)!='try-error') {IC.robustes<-rbind(IC.robustes,MoM$ci) dimnames(IC.robustes)[[1]][length(IC.robustes[,1])]<-"M-estimator modifie"} - if(all(dim(IC.robustes)!=0)) names(IC.robustes )<-c(txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot) - Resultats[[txt_robusts_statistics]]<-IC.robustes - c(desc_bootstrap_t_adapt_to_truncated_mean, - desc_this_index_is_prefered_for_most_cases, - desc_truncature_on_m_estimator_adapts_to_sample)->Resultats$infos - } else Resultats[[txt_robusts_statistics]]<-desc_robusts_statistics_could_not_be_computed_verify_WRS + if(all(dim(IC.robustes)!=0)) names(IC.robustes )<-c(.dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]]) + Resultats[[.dico[["txt_robusts_statistics"]]]]<-IC.robustes + c(.dico[["desc_bootstrap_t_adapt_to_truncated_mean"]], + .dico[["desc_this_index_is_prefered_for_most_cases"]], + .dico[["desc_truncature_on_m_estimator_adapts_to_sample"]])->Resultats$infos + } else Resultats[[.dico[["txt_robusts_statistics"]]]]<-.dico[["desc_robusts_statistics_could_not_be_computed_verify_WRS"]] } return(Resultats) } - apparies<-function(X, Y, data=NULL, param=c("param", "non param", txt_robusts),alternative="two.sided", n.boot=NULL, rscale=0.707){ + apparies<-function(X, Y, data=NULL, param=c("param", "non param", .dico[["txt_robusts"]]),alternative="two.sided", n.boot=NULL, rscale=0.707){ Resultats<-list() .e <- environment() - Resultats[[txt_descriptive_statistics]]<-.stat.desc.out(X=X, groupes=Y, data=data, tr=.1, type=3, plot=T) + Resultats[[.dico[["txt_descriptive_statistics"]]]]<-.stat.desc.out(X=X, groupes=Y, data=data, tr=.1, type=3, plot=T) large<-data.frame("t1"=data[which(data[,Y]==levels(data[,Y])[1]), X], "t2"=data[which(data[,Y]==levels(data[,Y])[2]), X]) - if(any(param=="param") | any(param==txt_param_tests)){ + if(any(param=="param") | any(param==.dico[["txt_param_tests"]])){ large$diff<--large$t2-large$t1 - Resultats[[txt_normality_tests]]<-.normalite(data=large, X="diff", Y=NULL) + Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=large, X="diff", Y=NULL) t.test(data[,X]~data[,Y], paired = TRUE, conf.level = 0.95, alternative=alternative)->ttest ttest$statistic^2/( ttest$statistic^2+ ttest$parameter)->R_carre cohensD(x= large[,1], y=large[,2], method="paired")->dc data.frame("t test"= round(ttest$statistic,3), txt_df= ttest$parameter, txt_p_dot_val= round(ttest$p.value,4), txt_ci_inferior_limit_dot= ttest$conf.int[[1]], txt_ci_superior_limit_dot=ttest$conf.int[[2]], txt_r_dot_square=round(R_carre,4), txt_cohen_d=round(dc,3))->ttest - c("t test", txt_df, txt_p_dot_val, txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot, txt_r_dot_square, txt_cohen_d)->names(ttest) + c("t test", .dico[["txt_df"]], .dico[["txt_p_dot_val"]], .dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]], .dico[["txt_r_dot_square"]], .dico[["txt_cohen_d"]])->names(ttest) dimnames(ttest)[1]<-" " - ttest->Resultats[[txt_student_t_test_paired]]} - if(any(param=="param") | any(param==txt_param_tests, any(param=="Bayes") | any(param==txt_bayesian_factors))) { + ttest->Resultats[[.dico[["txt_student_t_test_paired"]]]]} + if(any(param=="param") | any(param==.dico[["txt_param_tests"]], any(param=="Bayes") | any(param==.dico[["txt_bayesian_factors"]]))) { # realisation du graphique X1<-which(names(data)==X) nonaj<-ggplot(data) nonaj<- nonaj+eval(parse(text=paste0("aes(x=", Y, ", y=", X,")"))) # aes(x=data[,Y], y=data[,X1]))+labs(x=Y, y=X)+ nonaj<- nonaj+ stat_summary(fun.y=mean, geom="bar",fill="grey", colour="White")+stat_summary(fun.data="mean_sdl", geom="errorbar", position=position_dodge(width=0.90), width=0.2) - nonaj<-nonaj+theme(plot.title = element_text(size = 12))+ggtitle(txt_non_adjusted_data) + nonaj<-nonaj+theme(plot.title = element_text(size = 12))+ggtitle(.dico[["txt_non_adjusted_data"]]) # realisation du graphique ajuste propose par Loftus et Masson 1994 (pour plus d informations voir l article) - Resultats[[txt_mean_sd_for_non_adjusted_data]]<-nonaj + Resultats[[.dico[["txt_mean_sd_for_non_adjusted_data"]]]]<-nonaj large$meanD2<-(large[ ,1]+large[ ,2])/2 mean(large$meanD2)->GMean GMean-large$meanD2->large$adj large$adjM1<-large[ ,1]+large$adj large$adjM2<-large[ ,2]+large$adj - data[,paste0(X, txt_dot_adjusted)]<-c(large$adjM1,large$adjM2) + data[,paste0(X, .dico[["txt_dot_adjusted"]])]<-c(large$adjM1,large$adjM2) aj<-ggplot(data) aj<-aj+eval(parse(text=paste0("aes(x=", Y, ", y=", names(data)[length(data)],")"))) aj<-aj+labs(x=Y, y=X)+stat_summary(fun.y=mean, geom="bar", fill="grey", colour="White")+stat_summary(fun.data="mean_sdl", geom="errorbar", position=position_dodge(width=0.90), width=0.2) - aj<-aj+theme(plot.title = element_text(size = 12))+ggtitle(txt_adjusted_data_loftus_masson) - Resultats[[txt_mean_sd_for_adjusted_data]]<-aj + aj<-aj+theme(plot.title = element_text(size = 12))+ggtitle(.dico[["txt_adjusted_data_loftus_masson"]]) + Resultats[[.dico[["txt_mean_sd_for_adjusted_data"]]]]<-aj .multiplot(nonaj,aj, cols=2 ) } - if(any(param=="Bayes") | any(param==txt_bayesian_factors) ){ - if(all(param!="param") & all(param!=txt_param_tests)) Resultats[[txt_normality_tests]]<-.normalite(data=data, X=X, Y=Y) + if(any(param=="Bayes") | any(param==.dico[["txt_bayesian_factors"]]) ){ + if(all(param!="param") & all(param!=.dico[["txt_param_tests"]])) Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=data, X=X, Y=Y) BF<-ttestBF(x=data[ which(data[ ,Y]==levels(data[ ,Y])[1]) ,X], y=data[ which(data[ ,Y]==levels(data[ ,Y])[2]) ,X] , paired=TRUE, rscale=rscale) BF<-extractBF(BF, onlybf=F) BF<-data.frame(txt_bayesian_factor=c(round(BF$bf,5), round((1/BF$bf),5)), txt_error=round(c( BF$error, BF$error),5)) - names(BF)<-c(txt_bayesian_factor, txt_error) - dimnames(BF)[[1]]<-c(txt_supports_alternative, txt_supports_null) - Resultats[[txt_bayesian_factors]]<-BF + names(BF)<-c(.dico[["txt_bayesian_factor"]], .dico[["txt_error"]]) + dimnames(BF)[[1]]<-c(.dico[["txt_supports_alternative"]], .dico[["txt_supports_null"]]) + Resultats[[.dico[["txt_bayesian_factors"]]]]<-BF samples<-ttestBF(x=data[ which(data[ ,Y]==levels(data[ ,Y])[1]) ,X], y=data[ which(data[ ,Y]==levels(data[ ,Y])[2]) ,X] , paired=TRUE, rscale=rscale, posterior=T, iterations = ifelse(is.null(n.boot), 1000, n.boot)) plot(samples[,1:4]) @@ -471,10 +471,10 @@ test.t <- } SBF<-data.frame("n"=rep(5:(length(data[,X])/2), each=3 ),"BF"= bfs, - "rscale"=factor(rep(c("moyen", txt_large, txt_ultrawide), length.out= 3*((length(data[,X])/2)-4) ))) + "rscale"=factor(rep(c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]), length.out= 3*((length(data[,X])/2)-4) ))) names(SBF)<-c("n", "BF", "rscale") - reorder( c("moyen", txt_large, txt_ultrawide),levels(SBF$rscale))->levels(SBF$rscale) - Resultats[[txt_bayesian_factors_sequential]]<-.plotSBF(SBF) + reorder( c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]),levels(SBF$rscale))->levels(SBF$rscale) + Resultats[[.dico[["txt_bayesian_factors_sequential"]]]]<-.plotSBF(SBF) ##### Debut du graphique Bayes Factor Robustness Check @@ -498,7 +498,7 @@ test.t <- # do the Bayes factor plot plot(cauchyRates, bayesFactors, type = "l", lwd = 2, col = "gray48", ylim = c(0, max(bayesFactors)), xaxt = "n", - xlab = txt_cauchy_prior_width, ylab = txt_bayes_factor_10) + xlab = .dico[["txt_cauchy_prior_width"]], ylab = .dico[["txt_bayes_factor_10"]]) abline(h = 0, lwd = 1) abline(h = 6, col = "black", lty = 2, lwd = 2) axis(1, at = seq(0, 1.5, 0.25)) @@ -512,31 +512,31 @@ test.t <- col = c("black", "black"), pt.bg = c("black", "gray", "white"), bty = "n") } - if(any(param=="non param")| any(param==txt_non_parametric_test)) { + if(any(param=="non param")| any(param==.dico[["txt_non_parametric_test"]])) { WT<-wilcox.test(as.formula(paste0(X, "~",Y)), paired=T,data=data, alternative=alternative, conf.int=T, conf.level=0.95) if(alternative!="two.sided") abs(qnorm(WT$p.value))->z else abs(qnorm(WT$p.value/2))->z r<-z/(length(data[,X]))^0.5 Resultats$Wilcoxon<- data.frame("Wilcoxon W"=WT$statistic, txt_p_dot_val=round(WT$p.value,4), "z"=round(z,4), "r"=round(r,4), txt_ci_inferior_limit_dot=WT$conf.int[1],txt_ci_superior_limit_dot=WT$conf.int[2]) - names(Resultats$Wilcoxon)<- c("Wilcoxon W", txt_p_dot_val, "z", "r", txt_ci_inferior_limit_dot,txt_ci_superior_limit_dot) + names(Resultats$Wilcoxon)<- c("Wilcoxon W", .dico[["txt_p_dot_val"]], "z", "r", .dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_ci_superior_limit_dot"]]) } - if(any(param==txt_robusts| any(param==txt_robusts_tests_with_bootstraps)) ){ + if(any(param==.dico[["txt_robusts"]]| any(param==.dico[["txt_robusts_tests_with_bootstraps"]])) ){ try(WRS::yuend(data[ which(data[ ,Y]==levels(data[ ,Y])[1]) ,X], data[ which(data[ ,Y]==levels(data[ ,Y])[2]) ,X], tr=.2),silent=T)->moy.tr if(class(moy.tr)!='try-error'){ round(unlist(moy.tr),3)->moy.tr - names(moy.tr)<-c(txt_ci_inferior,txt_ci_superior, txt_p_dot_val, txt_mean1, txt_mean2, txt_difference,"se", "Stat", "n", txt_df) + names(moy.tr)<-c(.dico[["txt_ci_inferior"]],.dico[["txt_ci_superior"]], .dico[["txt_p_dot_val"]], .dico[["txt_mean1"]], .dico[["txt_mean2"]], .dico[["txt_difference"]],"se", "Stat", "n", .dico[["txt_df"]]) if(n.boot>99){ WRS::ydbt(data[ which(data[ ,Y]==levels(data[ ,Y])[1]) ,X], data[ which(data[ ,Y]==levels(data[ ,Y])[2]) ,X], tr=0.2, nboot=n.boot)->moy.tr.bt - moy.tr->Resultats[[txt_robusts_statistics]][[txt_comparison_on_truncated_means]] - round(unlist(moy.tr.bt),4)->Resultats[[txt_robusts_statistics]][[txt_student_bootstrap_on_truncated_means]] + moy.tr->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_comparison_on_truncated_means"]]]] + round(unlist(moy.tr.bt),4)->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_student_bootstrap_on_truncated_means"]]]] if(length(data[,1])>20) { try({WRS::bootdpci(data[ which(data[ ,Y]==levels(data[ ,Y])[1]) ,X], data[ which(data[ ,Y]==levels(data[ ,Y])[2]) ,X], nboot=n.boot, BA=T)$output[,2:6]->Mest - names(Mest)<-c(txt_statistic, txt_p_dot_val, "p.crit", "CI inf", "CI sup") - Mest->Resultats[[txt_robusts_statistics]][[txt_bca_bootstrap_on_m_estimator]]} + names(Mest)<-c(.dico[["txt_statistic"]], .dico[["txt_p_dot_val"]], "p.crit", "CI inf", "CI sup") + Mest->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_bca_bootstrap_on_m_estimator"]]]]} , silent=T) - }}} else Resultats[[txt_robusts_statistics]]<-desc_robusts_statistics_could_not_be_computed + }}} else Resultats[[.dico[["txt_robusts_statistics"]]]]<-.dico[["desc_robusts_statistics_could_not_be_computed"]] } @@ -545,17 +545,17 @@ test.t <- return(Resultats) } - indpdts<-function(X, Y, data, param=c("param", "non param",txt_robusts),alternative="two.sided", n.boot=NULL, rscale=0.707){ + indpdts<-function(X, Y, data, param=c("param", "non param",.dico[["txt_robusts"]]),alternative="two.sided", n.boot=NULL, rscale=0.707){ Resultats<-list() .e <- environment() - Resultats[[txt_descriptive_statistics]]<-.stat.desc.out(X=X, groupes=Y, data=data, tr=.1, type=3, plot=T) + Resultats[[.dico[["txt_descriptive_statistics"]]]]<-.stat.desc.out(X=X, groupes=Y, data=data, tr=.1, type=3, plot=T) as.formula(paste0(X," ~ ",Y))->modele - if(any(param=="param") | any(param==txt_param_tests)){ - Resultats[[txt_normality_tests]]<-.normalite(data=data, X=X, Y=Y) + if(any(param=="param") | any(param==.dico[["txt_param_tests"]])){ + Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=data, X=X, Y=Y) car::leveneTest(data[ ,X], data[ ,Y])->Levene # test de Levene pour homogeneite des variances round(unlist(Levene)[c(1,2,3,5)],3)->Levene - names(Levene)<-c(txt_df1,txt_df2,"F",txt_p_dot_val) - Levene->Resultats[[txt_levene_test_verifying_homogeneity_variances]] + names(Levene)<-c(.dico[["txt_df1"]],.dico[["txt_df2"]],"F",.dico[["txt_p_dot_val"]]) + Levene->Resultats[[.dico[["txt_levene_test_verifying_homogeneity_variances"]]]] t.test(modele, data=data, alternative=alternative, var.equal=TRUE, conf.level=0.95)->student round(student$statistic^2/(student$statistic^2+student$parameter),3)->R.deux d_cohen<-round(cohensD(modele , data=data, method = "pooled"),3) @@ -566,25 +566,25 @@ test.t <- d_cohen.corr<-cohensD(modele , data=data, method = "unequal") data.frame(corrige[9], round(corrige$statistic,3), round(corrige$parameter,3), round(corrige$p.value,3), round(corrige$conf.int[1],4), round(corrige$conf.int[2],4), R.deux, d_cohen)->corrige - names(student)<-c("modele", "test t", txt_df, txt_p_dot_val, txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot,txt_r_dot_square,txt_cohen_d) - names(corrige)<- c("modele", "test t", txt_df, txt_p_dot_val, txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot,txt_r_dot_square,txt_cohen_d) + names(student)<-c("modele", "test t", .dico[["txt_df"]], .dico[["txt_p_dot_val"]], .dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]],.dico[["txt_r_dot_square"]],.dico[["txt_cohen_d"]]) + names(corrige)<- c("modele", "test t", .dico[["txt_df"]], .dico[["txt_p_dot_val"]], .dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]],.dico[["txt_r_dot_square"]],.dico[["txt_cohen_d"]]) student<-rbind(student, corrige) - dimnames(student)[[1]]<-c(txt_without_welch_correction,txt_with_welch_correction) - student->Resultats[[txt_student_t_independant]] + dimnames(student)[[1]]<-c(.dico[["txt_without_welch_correction"]],.dico[["txt_with_welch_correction"]]) + student->Resultats[[.dico[["txt_student_t_independant"]]]] p<-ggplot(data) p<-p+eval(parse(text=paste0("aes(x=", Y, ", y=", X,")"))) p<-p+ stat_summary(fun.y=mean, geom="bar",fill="grey", colour="White")+stat_summary(fun.data="mean_sdl", geom="errorbar", position=position_dodge(width=0.90), width=0.2) - Resultats[[txt_graphic_mean_sd]]<-p + Resultats[[.dico[["txt_graphic_mean_sd"]]]]<-p } - if(any(param=="Bayes") | any(param==txt_bayesian_factors) ){ - if(all(param!="param") & all(param!=txt_param_tests)) Resultats[[txt_normality_tests]]<-.normalite(data=data, X=X, Y=Y) + if(any(param=="Bayes") | any(param==.dico[["txt_bayesian_factors"]]) ){ + if(all(param!="param") & all(param!=.dico[["txt_param_tests"]])) Resultats[[.dico[["txt_normality_tests"]]]]<-.normalite(data=data, X=X, Y=Y) BF<-ttestBF(formula=modele,data=data, paired=FALSE, rscale=rscale) BF<-extractBF(BF, onlybf=F) BF<-data.frame(txt_bayesian_factor=c(round(BF$bf,5), round((1/BF$bf),5)), txt_error=round(c( BF$error, BF$error),5)) - names(BF)<-c(txt_bayesian_factor, txt_error) - dimnames(BF)[[1]]<-c(txt_supports_alternative, txt_supports_null) - Resultats[[txt_bayesian_factors]]<-BF + names(BF)<-c(.dico[["txt_bayesian_factor"]], .dico[["txt_error"]]) + dimnames(BF)[[1]]<-c(.dico[["txt_supports_alternative"]], .dico[["txt_supports_null"]]) + Resultats[[.dico[["txt_bayesian_factors"]]]]<-BF samples<-ttestBF(formula=modele,data=data, paired=FALSE, rscale=rscale, posterior=T, iterations = ifelse(is.null(n.boot), 1000, n.boot)) plot(samples[,1:4]) @@ -603,10 +603,10 @@ test.t <- } SBF<-data.frame("n"=rep(5:(length(data[,X])), each=3 ),"BF"= bfs, - "rscale"=factor(rep(c("moyen", txt_large, txt_ultrawide), length.out= 3*(length(data[,X])-4) ))) + "rscale"=factor(rep(c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]), length.out= 3*(length(data[,X])-4) ))) names(SBF)<-c("n", "BF", "rscale") - reorder( c("moyen", txt_large, txt_ultrawide),levels(SBF$rscale))->levels(SBF$rscale) - Resultats[[txt_bayesian_factors_sequential]]<-.plotSBF(SBF) + reorder( c("moyen", .dico[["txt_large"]], .dico[["txt_ultrawide"]]),levels(SBF$rscale))->levels(SBF$rscale) + Resultats[[.dico[["txt_bayesian_factors_sequential"]]]]<-.plotSBF(SBF) ##### Debut du graphique Bayes Factor Robustness Check @@ -631,7 +631,7 @@ test.t <- # do the Bayes factor plot plot(cauchyRates, bayesFactors, type = "l", lwd = 2, col = "gray48", ylim = c(0, max(bayesFactors)), xaxt = "n", - xlab = txt_cauchy_prior_width, ylab = txt_bayes_factor_10) + xlab = .dico[["txt_cauchy_prior_width"]], ylab = .dico[["txt_bayes_factor_10"]]) abline(h = 0, lwd = 1) abline(h = 6, col = "black", lty = 2, lwd = 2) axis(1, at = seq(0, 1.5, 0.25)) @@ -645,16 +645,16 @@ test.t <- col = c("black", "black"), pt.bg = c("black", "gray", "white"), bty = "n") } - if(any(param=="non param")| any(param==txt_non_parametric_test)) { + if(any(param=="non param")| any(param==.dico[["txt_non_parametric_test"]])) { WT<-wilcox.test(modele, paired=F,data=data, alternative=alternative, conf.int=T, conf.level=0.95) if(alternative!="two.sided") abs(qnorm(WT$p.value))->z else abs(qnorm(WT$p.value/2))->z r<-z/(length(data[,X]))^0.5 - Resultats[[txt_mann_whitney_test]]<- data.frame("Wilcoxon W"=WT$statistic, txt_p_dot_val=round(WT$p.value,4), "z"=round(z,4), "r"=round(r,4), + Resultats[[.dico[["txt_mann_whitney_test"]]]]<- data.frame("Wilcoxon W"=WT$statistic, txt_p_dot_val=round(WT$p.value,4), "z"=round(z,4), "r"=round(r,4), txt_ci_inferior_limit_dot=WT$conf.int[1],txt_ci_superior_limit_dot=WT$conf.int[2]) - names(Resultats[[txt_mann_whitney_test]])<- c("Wilcoxon W", txt_p_dot_val, "z", "r", txt_ci_inferior_limit_dot,txt_ci_superior_limit_dot) + names(Resultats[[.dico[["txt_mann_whitney_test"]]]])<- c("Wilcoxon W", .dico[["txt_p_dot_val"]], "z", "r", .dico[["txt_ci_inferior_limit_dot"]],.dico[["txt_ci_superior_limit_dot"]]) } - if(any(param==txt_robusts| any(param==txt_robusts_tests_with_bootstraps)) ){ + if(any(param==.dico[["txt_robusts"]]| any(param==.dico[["txt_robusts_tests_with_bootstraps"]])) ){ data[which(data[,Y]==levels(data[,Y])[1]),]->g1 # on cree une base de Donnees avec le groupe 1 uniquement (sans valeur aberrantes) data[which(data[,Y]==levels(data[,Y])[2]),]->g2 # on cree une base de Donnees avec le groupe 2 uniquement (sans valeur aberrantes) try(WRS::yuen(g1[,X],g2[,X]), silent=T)->yuen.modele### fournit la probabilite associee a des moyennes tronquees.Par defaut, la troncature est de 0.20 @@ -662,13 +662,13 @@ test.t <- round(unlist(yuen.modele),4)->yuen.modele cbind(yuen.modele[1:2], yuen.modele[3:4])->yuen.desc dimnames(yuen.desc)[[1]]<-levels(data[,Y]) - dimnames(yuen.desc)[[2]]<-c("n", txt_truncated_means) - yuen.desc->Resultats[[txt_robusts_statistics]][[txt_descriptive_statistics]] + dimnames(yuen.desc)[[2]]<-c("n", .dico[["txt_truncated_means"]]) + yuen.desc->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_descriptive_statistics"]]]] yuen.modele[c(5,6,8,9,10,11,12,7)]->yuen.modele - names(yuen.modele)<-c(txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot, - txt_difference,"Err-type","Stat", txt_threshold, txt_df,txt_p_dot_val) - yuen.modele->Resultats[[txt_robusts_statistics]][[txt_analysis_on_truncated_means]] + names(yuen.modele)<-c(.dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]], + .dico[["txt_difference"]],"Err-type","Stat", .dico[["txt_threshold"]], .dico[["txt_df"]],.dico[["txt_p_dot_val"]]) + yuen.modele->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_analysis_on_truncated_means"]]]] if(n.boot>99){ WRS2::yuenbt(modele, data= data, nboot=n.boot, side=T)->yuen.bt.modele ### fournit la probabilite associee a des moyennes tronquees apres un bootstrap. yuen.bt.modele<-round(data.frame(test = yuen.bt.modele$test, @@ -676,20 +676,20 @@ test.t <- valeur.p = yuen.bt.modele$p.value, lim.inf.IC = yuen.bt.modele$conf.int[1], lim.sup.IC = yuen.bt.modele$conf.int[2]),3) - yuen.bt.modele->Resultats[[txt_robusts_statistics]][[txt_bootstrap_t_method_on_truncated_means]] + yuen.bt.modele->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_bootstrap_t_method_on_truncated_means"]]]] WRS::pb2gen(g1[,X],g2[,X], nboot=n.boot)->pb2gen.modele### calcule le bootstrap sur le M-estimateur et fournit l intervalle de confiance. round(unlist(pb2gen.modele)[1:6],4)->pb2gen.modele - names(pb2gen.modele)<-c("M.estimator.G1", "M.estimator.G2", "diff", txt_ci_inferior_limit_dot, txt_ci_superior_limit_dot, txt_p_dot_val) - pb2gen.modele->Resultats[[txt_robusts_statistics]][[txt_percentile_bootstrap_on_m_estimators]] - Resultats[[txt_robusts_statistics]]$Informations<-c(desc_percentile_bootstrap_prefered_for_small_samples, - desc_for_bigger_samples_bootstrap_t_prefered) + names(pb2gen.modele)<-c("M.estimator.G1", "M.estimator.G2", "diff", .dico[["txt_ci_inferior_limit_dot"]], .dico[["txt_ci_superior_limit_dot"]], .dico[["txt_p_dot_val"]]) + pb2gen.modele->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_percentile_bootstrap_on_m_estimators"]]]] + Resultats[[.dico[["txt_robusts_statistics"]]]]$Informations<-c(.dico[["desc_percentile_bootstrap_prefered_for_small_samples"]], + .dico[["desc_for_bigger_samples_bootstrap_t_prefered"]]) } easieR::ks(g1[,X],g2[,X],w=F,sig=T)->KS round(unlist(KS),4)->KS - names(KS)<-c("KS", txt_critical_dot_threshold,txt_p_dot_val) - KS->Resultats[[txt_robusts_statistics]][[txt_kolmogorov_smirnov_comparing_two_distrib]] - }else Resultats[[txt_robusts_statistics]]<-desc_robusts_statistics_could_not_be_computed_verify_WRS + names(KS)<-c("KS", .dico[["txt_critical_dot_threshold"]],.dico[["txt_p_dot_val"]]) + KS->Resultats[[.dico[["txt_robusts_statistics"]]]][[.dico[["txt_kolmogorov_smirnov_comparing_two_distrib"]]]] + }else Resultats[[.dico[["txt_robusts_statistics"]]]]<-.dico[["desc_robusts_statistics_could_not_be_computed_verify_WRS"]] } @@ -735,7 +735,7 @@ test.t <- for(i in 1 : length(X)) { - if(choix==txt_two_paired_samples){ + if(choix==.dico[["txt_two_paired_samples"]]){ diffs<-data[which(is.na(data[,X])), "IDeasy"] if(length(diffs)==0) data->data1 else data[which(data$IDeasy!=diffs), ]->data1 } else { @@ -747,20 +747,20 @@ test.t <- X1<-X[i] R1<-list() - if(any(outlier== txt_complete_dataset)){ - if (choix==txt_comparison_to_norm) { - R1[[txt_complete_dataset]]<-norme(X=X1, mu=mu, data=data1, param=param, group=group, alternative=alternative, n.boot=n.boot, rscale=rscale) + if(any(outlier== .dico[["txt_complete_dataset"]])){ + if (choix==.dico[["txt_comparison_to_norm"]]) { + R1[[.dico[["txt_complete_dataset"]]]]<-norme(X=X1, mu=mu, data=data1, param=param, group=group, alternative=alternative, n.boot=n.boot, rscale=rscale) } - if (choix==txt_two_paired_samples) { - R1[[txt_complete_dataset]]<-apparies(X=X1, Y=Y, data=data1, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) + if (choix==.dico[["txt_two_paired_samples"]]) { + R1[[.dico[["txt_complete_dataset"]]]]<-apparies(X=X1, Y=Y, data=data1, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) } - if (choix==txt_two_independant_samples) { - R1[[txt_complete_dataset]]<-indpdts(X=X1, Y=Y, data=data1, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) + if (choix==.dico[["txt_two_independant_samples"]]) { + R1[[.dico[["txt_complete_dataset"]]]]<-indpdts(X=X1, Y=Y, data=data1, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) } } - if(any(outlier==txt_identifying_outliers)|any(outlier==txt_without_outliers)){ - if(choix==txt_comparison_to_norm) { + if(any(outlier==.dico[["txt_identifying_outliers"]])|any(outlier==.dico[["txt_without_outliers"]])){ + if(choix==.dico[["txt_comparison_to_norm"]]) { if(class(data1)!="data.frame"){ data1<-data.frame(data1) names(data1)[1]<-X1 @@ -772,30 +772,30 @@ test.t <- critere<-ifelse(is.null(z), "Grubbs", "z") valeurs.influentes(X='residu', critere=critere,z=z, data=data1)->influentes } - if(any(outlier== txt_identifying_outliers)){influentes->R1[[txt_outliers_values]]} - if(any(outlier== txt_without_outliers)) { - if(influentes[[txt_outliers_synthesis]][[txt_synthesis]][1]!=0 | all(outlier!=txt_complete_dataset)){ - if(choix==txt_two_paired_samples){ - setdiff(data$IDeasy,influentes[[txt_outliers]]$IDeasy)->diffs + if(any(outlier== .dico[["txt_identifying_outliers"]])){influentes->R1[[.dico[["txt_outliers_values"]]]]} + if(any(outlier== .dico[["txt_without_outliers"]])) { + if(influentes[[.dico[["txt_outliers_synthesis"]]]][[.dico[["txt_synthesis"]]]][1]!=0 | all(outlier!=.dico[["txt_complete_dataset"]])){ + if(choix==.dico[["txt_two_paired_samples"]]){ + setdiff(data$IDeasy,influentes[[.dico[["txt_outliers"]]]]$IDeasy)->diffs data[which(data$IDeasy%in%diffs), ]->nettoyees } else get('nettoyees', envir=.GlobalEnv)->nettoyees ### Regler le souci pour les echantillons apparies - if (choix==txt_comparison_to_norm) { - R1[[txt_without_outliers]]<-norme(X=X1, mu=mu, data=nettoyees, param=param, group=group, alternative=alternative, n.boot=n.boot, rscale=rscale) + if (choix==.dico[["txt_comparison_to_norm"]]) { + R1[[.dico[["txt_without_outliers"]]]]<-norme(X=X1, mu=mu, data=nettoyees, param=param, group=group, alternative=alternative, n.boot=n.boot, rscale=rscale) } - if (choix==txt_two_paired_samples) { - R1[[txt_without_outliers]]<-apparies(X=X1, Y=Y, data=nettoyees, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) + if (choix==.dico[["txt_two_paired_samples"]]) { + R1[[.dico[["txt_without_outliers"]]]]<-apparies(X=X1, Y=Y, data=nettoyees, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) } - if (choix==txt_two_independant_samples) { - R1[[txt_without_outliers]]<-indpdts(X=X1, Y=Y, data=nettoyees, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) + if (choix==.dico[["txt_two_independant_samples"]]) { + R1[[.dico[["txt_without_outliers"]]]]<-indpdts(X=X1, Y=Y, data=nettoyees, param=param,alternative=alternative, n.boot=n.boot, rscale=rscale) } } } Resultats[[i]]<-R1 } - names(Resultats)<-paste(txt_analysis_on_variable, X) + names(Resultats)<-paste(.dico[["txt_analysis_on_variable"]], X) paste(unique(X), collapse="','", sep="")->X paste(outlier, collapse="','", sep="")->outlier @@ -813,7 +813,7 @@ test.t <- if(sauvegarde){save(Resultats=Resultats ,choix =choix, env=.e)} - ref1(packages)->Resultats[[txt_references]] + ref1(packages)->Resultats[[.dico[["txt_references"]]]] if(html) ez.html(Resultats) ### Obtenir les Resultats return(Resultats) diff --git a/R/tetrapoly.R b/R/tetrapoly.R index d190051..7db428f 100644 --- a/R/tetrapoly.R +++ b/R/tetrapoly.R @@ -18,8 +18,8 @@ tetrapoly <- Resultats<-list() if(is.null(data) | is.null(X)) {dial<-TRUE - if(info) writeLines(ask_correlation_type) - dlgList(c(txt_polyc_correlations, txt_mixt_correlations), preselect=NULL, multiple = FALSE, title=ask_correlations_type)$res->method + if(info) writeLines(.dico[["ask_correlation_type"]]) + dlgList(c(.dico[["txt_polyc_correlations"]], .dico[["txt_mixt_correlations"]]), preselect=NULL, multiple = FALSE, title=.dico[["ask_correlations_type"]])$res->method if(length(method)==0) return(choix.corr()) } else dial<-F @@ -34,26 +34,26 @@ tetrapoly <- } - msg3<-ask_variabels_for_polyc_tetra_mixt_corr - X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=ask_variables, out=NULL) + msg3<-.dico[["ask_variabels_for_polyc_tetra_mixt_corr"]] + X<-.var.type(X=X, info=info, data=data, type="numeric", check.prod=F, message=msg3, multiple=T, title=.dico[["ask_variables"]], out=NULL) if(is.null(X)) { Resultats<-tetrapoly(data=NULL,X=NULL, sauvegarde=F, ord=NULL ,info=T, group=NULL, estimator=estimator, output=output) return(Resultats)} data<-X$data X<-X$X - if(!is.null(ord) & any(ord %in%X==F)||(dial && method==txt_mixt_correlations ) ){ - if(info) writeLines(ask_ordinal_variables) - ord<-dlgList(X, preselect=X, multiple = TRUE, title=ask_ordinal_variables)$res + if(!is.null(ord) & any(ord %in%X==F)||(dial && method==.dico[["txt_mixt_correlations"]] ) ){ + if(info) writeLines(.dico[["ask_ordinal_variables"]]) + ord<-dlgList(X, preselect=X, multiple = TRUE, title=.dico[["ask_ordinal_variables"]])$res if(length(ord)==0){ Resultats<-tetrapoly(data=NULL,X=NULL, sauvegarde=F, ord=NULL ,info=T, group=NULL, estimator=estimator, output=output) return(Resultats) } } else ord<-X if(any(is.na(data[,X]))) { - if(is.null(imp)) {msgBox(ask_how_to_treat_missing_values) - imp<- dlgList(c(txt_do_nothing_keep_all_obs, txt_delete_observations_with_missing_values,txt_replace_by_median,txt_multiple_imputation_amelia), - preselect=FALSE, multiple = TRUE, title=ask_missing_values_treatment)$res} + if(is.null(imp)) {msgBox(.dico[["ask_how_to_treat_missing_values"]]) + imp<- dlgList(c(.dico[["txt_do_nothing_keep_all_obs"]], .dico[["txt_delete_observations_with_missing_values"]],.dico[["txt_replace_by_median"]],.dico[["txt_multiple_imputation_amelia"]]), + preselect=FALSE, multiple = TRUE, title=.dico[["ask_missing_values_treatment"]])$res} if(length(imp)==0){ Resultats<-tetrapoly(data=NULL,X=NULL, sauvegarde=F, ord=NULL ,info=T, group=NULL, estimator=estimator, output=output) return(Resultats) @@ -62,13 +62,13 @@ tetrapoly <- data<-data.frame(data1, data[which(dimnames(data)[[1]] %in% dimnames(data1)[[1]]),group]) } if(dial || !is.logical(sauvegarde)){ - sauvegarde<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=ask_save_results)$res + sauvegarde<- dlgList(c(TRUE, FALSE), preselect=FALSE, multiple = FALSE, title=.dico[["ask_save_results"]])$res if(length(sauvegarde)==0) { Resultats<-tetrapoly(data=NULL,X=NULL, sauvegarde=F, ord=NULL ,info=T, group=NULL, estimator=estimator, output=output) return(Resultats) } } - Resultats[[txt_tetra_polyc_corr_matrix_or_mixt]]<-lavCor(data[,c(X,group)], ordered=ord,estimator=estimator, group=group, missing="default", output=output) + Resultats[[.dico[["txt_tetra_polyc_corr_matrix_or_mixt"]]]]<-lavCor(data[,c(X,group)], ordered=ord,estimator=estimator, group=group, missing="default", output=output) paste(X, collapse="','", sep="")->X if(!is.null(ord)) paste(ord, collapse="','", sep="")->ord Resultats$Call<-paste0("tetrapoly(data=", nom,",X=c('", X,"'),sauvegarde=", sauvegarde, ",ord=", ifelse(!is.null(ord),paste0("c('",ord,"')"), "NULL"), @@ -80,6 +80,6 @@ tetrapoly <- if(sauvegarde) save(Resultats=Resultats, choix="cor.polychorique", env=.e) - ref1(packages)->Resultats[[txt_references]] + ref1(packages)->Resultats[[.dico[["txt_references"]]]] if(html) ez.html(Resultats) return(Resultats) } diff --git a/R/trier.R b/R/trier.R index 8dead67..1255e29 100644 --- a/R/trier.R +++ b/R/trier.R @@ -9,22 +9,22 @@ trier <- if(length(data)==0) return(preprocess()) data[[1]]->nom1 data[[2]]->data - if(info==TRUE) writeLines(ask_variables_to_order) - X<-dlgList(c(names(data), txt_other_data), multiple = TRUE, title=txt_variables)$res - if(any(X==txt_other_data)) return(trier()) + if(info==TRUE) writeLines(.dico[["ask_variables_to_order"]]) + X<-dlgList(c(names(data), .dico[["txt_other_data"]]), multiple = TRUE, title=.dico[["txt_variables"]])$res + if(any(X==.dico[["txt_other_data"]])) return(trier()) if(length(X)==0) return(preprocess()) X->diff Y2<-c() d<-c() for(i in 1:length(diff)) { - writeLines(paste(ask_level, i, desc_order)) - Y<-dlgList(diff, multiple = FALSE, title=txt_variables)$res + writeLines(paste(.dico[["ask_level"]], i, .dico[["desc_order"]])) + Y<-dlgList(diff, multiple = FALSE, title=.dico[["txt_variables"]])$res if(length(Y)==0) return(trier()) setdiff(diff, Y)->diff c(Y2,Y)->Y2 } data[do.call("order", data[Y2]), ]->data View(data) - Resultats<-desc_data_succesfully_ordered + Resultats<-.dico[["desc_data_succesfully_ordered"]] assign(x=nom1, value=data, envir=.GlobalEnv) return(Resultats)} diff --git a/R/valeurs.influentes.R b/R/valeurs.influentes.R index 99b130e..54ce5f4 100644 --- a/R/valeurs.influentes.R +++ b/R/valeurs.influentes.R @@ -6,9 +6,9 @@ valeurs.influentes <- if(any(lapply(packages, require, character.only=T))==FALSE) {install.packages(packages) require(packages)} if(class(data[,X])=="integer") as.numeric(data[,X])->data[,X] - if(class(data[,X])!="numeric") return(desc_non_numeric_variable) - if(critere=="z" && class(z)!="numeric") return(desc_z_must_be_a_number) - if(any(match(c("Grubbs","z"), critere))==FALSE) return(desc_accepted_values_are_z_and_grubbs) + if(class(data[,X])!="numeric") return(.dico[["desc_non_numeric_variable"]]) + if(critere=="z" && class(z)!="numeric") return(.dico[["desc_z_must_be_a_number"]]) + if(any(match(c("Grubbs","z"), critere))==FALSE) return(.dico[["desc_accepted_values_are_z_and_grubbs"]]) length(data[,1])->i if(critere=="Grubbs"){ grubbs.test(data[,X], type = 10, opposite = FALSE, two.sided = FALSE)->outliers # test de Grubbs permettant de savoir s il y a des valeurs aberrantes @@ -20,9 +20,9 @@ valeurs.influentes <- rbind(valeur.influentes,data[max, ])->valeur.influentes data<-data[ -max, ] # supprime la valeur maximmal de data } - data.frame(G=outliers$statistic[1], U=outliers$statistic[2], valeur.p=round(outliers$p.value,4))->Resultats.valeurs.influentes[[txt_grubbs_test]] - c("G", "U", txt_p_dot_val)-> names(Resultats.valeurs.influentes[[txt_grubbs_test]]) - Resultats.valeurs.influentes[[desc_highest_value]]<-outliers$alternative + data.frame(G=outliers$statistic[1], U=outliers$statistic[2], valeur.p=round(outliers$p.value,4))->Resultats.valeurs.influentes[[.dico[["txt_grubbs_test"]]]] + c("G", "U", .dico[["txt_p_dot_val"]])-> names(Resultats.valeurs.influentes[[.dico[["txt_grubbs_test"]]]]) + Resultats.valeurs.influentes[[.dico[["desc_highest_value"]]]]<-outliers$alternative } @@ -37,10 +37,10 @@ valeurs.influentes <- i-iso->n # nombre d observations supprimees round((n/i)*100,2)-> pourcentage_N # proportions d observations supprimees (nombre / taille de l echantillon) rbind(n, paste(pourcentage_N, "%"))->synthese_aberrant # on combine le nombre et le pourcentage. - data.frame(information=c(desc_number_outliers_removed, desc_percentage_outliers), Synthese=synthese_aberrant)->synthese_aberrant # on cree un data.frame - c(txt_information, txt_synthesis)->names(synthese_aberrant) - if(all(dim( valeur.influentes)!=0)) Resultats.valeurs.influentes[[txt_outliers]]<-valeur.influentes - Resultats.valeurs.influentes[[txt_outliers_synthesis]] <-synthese_aberrant + data.frame(information=c(.dico[["desc_number_outliers_removed"]], .dico[["desc_percentage_outliers"]]), Synthese=synthese_aberrant)->synthese_aberrant # on cree un data.frame + c(.dico[["txt_information"]], .dico[["txt_synthesis"]])->names(synthese_aberrant) + if(all(dim( valeur.influentes)!=0)) Resultats.valeurs.influentes[[.dico[["txt_outliers"]]]]<-valeur.influentes + Resultats.valeurs.influentes[[.dico[["txt_outliers_synthesis"]]]] <-synthese_aberrant data->>nettoyees return(Resultats.valeurs.influentes) } diff --git a/R/vef.pack.R b/R/vef.pack.R index fd97a45..79a966e 100644 --- a/R/vef.pack.R +++ b/R/vef.pack.R @@ -64,7 +64,7 @@ function(){ ) list()->Resultats - Resultats[[desc_install_correct_packages]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==TRUE) ] - Resultats[[desc_install_bad_packages]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==FALSE) ] + Resultats[[.dico[["desc_install_correct_packages"]]]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==TRUE) ] + Resultats[[.dico[["desc_install_bad_packages"]]]]<-pack.to.inst[ which(lapply(pack.to.inst, require, character.only=T)==FALSE) ] return(Resultats) } diff --git a/R/view.results.R b/R/view.results.R index 4cc648e..b54d612 100644 --- a/R/view.results.R +++ b/R/view.results.R @@ -5,8 +5,8 @@ function(){ if(class(test2)== 'try-error') return(ez.install()) list()->Resultats Resultats$Call<-"view.results()" - ref1(packages)->Resultats[[desc_packages_used_for_this_function]] - if(!exists("ez.results")) return(desc_no_saved_analysis_found) else get("ez.results") + ref1(packages)->Resultats[[.dico[["desc_packages_used_for_this_function"]]]] + if(!exists("ez.results")) return(.dico[["desc_no_saved_analysis_found"]]) else get("ez.results") TkListView(ez.results) return(Resultats) } diff --git a/R/voir.R b/R/voir.R index c199cef..edb328c 100644 --- a/R/voir.R +++ b/R/voir.R @@ -12,6 +12,6 @@ function(){ } voir.msg<-function(){ -msg<-txt_dataframe_choice +msg<-.dico[["txt_dataframe_choice"]] return(msg)} diff --git a/i18n/README.md b/i18n/README.md index 2ea7b03..6f14b8e 100644 --- a/i18n/README.md +++ b/i18n/README.md @@ -70,6 +70,10 @@ Not tested : 2) The "dictionnaries" method loads a lot of objects in memory. This is potentially annoying. Placeholders variables are stored in accessible variables. Moreove those variables are made global (might not be good practise). +# Doing + +- Make placeholders variables less visible to the user environment: create an environment `.dico` into which placeholders are stored. + # Done - Language can be dynamically selected from GUI (`Interface` > `Choose language`) or command-line (e.g. `load_language("English")`) @@ -78,5 +82,9 @@ Not tested : # TODO - Chose better variables names for placeholders -- Make placeholders variables less visible to the user environment - Data interpolation system ("placeholders for data" inside placeholders) + +# References + +- [https://stackoverflow.com/questions/34254716/how-to-define-hidden-global-variables-inside-r-packages](https://stackoverflow.com/questions/34254716/how-to-define-hidden-global-variables-inside-r-packages) - Define hidden global variables inside R packages +- [http://adv-r.had.co.nz/Environments.html](http://adv-r.had.co.nz/Environments.html)