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cv.plsRcox.R
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cv.plsRcox.R
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#' Cross-validating a plsRcox-Model
#'
#' This function cross-validates \link{plsRcox} models.\cr
#'
#' It only computes the recommended iAUCSH criterion. Set \code{allCVcrit=TRUE}
#' to retrieve the 13 other ones.
#'
#'
#' @param data A list of three items: \itemize{ \item\code{x} the explanatory
#' variables passed to \code{\link{plsRcox}}'s \code{Xplan} argument,
#' \item\code{time} passed to \code{\link{plsRcox}}'s \code{time} argument,
#' \item\code{status} \code{\link{plsRcox}}'s \code{status} argument. }
#' @param method A character string specifying the method for tie handling. If
#' there are no tied death times all the methods are equivalent. The Efron
#' approximation is used as the default here, it is more accurate when dealing
#' with tied death times, and is as efficient computationally.
#' @param nfold The number of folds to use to perform the cross-validation
#' process.
#' @param nt The number of components to include in the model. It this is not
#' supplied, 10 components are fitted.
#' @param plot.it Shall the results be displayed on a plot ?
#' @param se Should standard errors be plotted ?
#' @param givefold Explicit list of omited values in each fold can be provided
#' using this argument.
#' @param scaleX Shall the predictors be standardized ?
#' @param folddetails Should values and completion status for each folds be
#' returned ?
#' @param allCVcrit Should the other 13 CV criteria be evaled and returned ?
#' @param details Should all results of the functions that perform error
#' computations be returned ?
#' @param namedataset Name to use to craft temporary results names
#' @param save Should temporary results be saved ?
#' @param verbose Should some CV details be displayed ?
#' @param \dots Other arguments to pass to \code{\link{plsRcox}}.
#' @return \item{nt}{The number of components requested}
#' \item{cv.error1}{Vector with the mean values, across folds, of, per fold
#' unit, Cross-validated log-partial-likelihood for models with 0 to nt
#' components.} \item{cv.error2}{Vector with the mean values, across folds, of,
#' per fold unit, van Houwelingen Cross-validated log-partial-likelihood for
#' models with 0 to nt components.} \item{cv.error3}{Vector with the mean
#' values, across folds, of iAUC_CD for models with 0 to nt components.}
#' \item{cv.error4}{Vector with the mean values, across folds, of iAUC_hc for
#' models with 0 to nt components.} \item{cv.error5}{Vector with the mean
#' values, across folds, of iAUC_sh for models with 0 to nt components.}
#' \item{cv.error6}{Vector with the mean values, across folds, of iAUC_Uno for
#' models with 0 to nt components.} \item{cv.error7}{Vector with the mean
#' values, across folds, of iAUC_hz.train for models with 0 to nt components.}
#' \item{cv.error8}{Vector with the mean values, across folds, of iAUC_hz.test
#' for models with 0 to nt components.} \item{cv.error9}{Vector with the mean
#' values, across folds, of iAUC_survivalROC.train for models with 0 to nt
#' components.} \item{cv.error10}{Vector with the mean values, across folds, of
#' iAUC_survivalROC.test for models with 0 to nt components.}
#' \item{cv.error11}{Vector with the mean values, across folds, of iBrierScore
#' unw for models with 0 to nt components.} \item{cv.error12}{Vector with the
#' mean values, across folds, of iSchmidScore (robust BS) unw for models with 0
#' to nt components.} \item{cv.error13}{Vector with the mean values, across
#' folds, of iBrierScore w for models with 0 to nt components.}
#' \item{cv.error14}{Vector with the mean values, across folds, of iSchmidScore
#' (robust BS) w for models with 0 to nt components.} \item{cv.se1}{Vector with
#' the standard error values, across folds, of, per fold unit, Cross-validated
#' log-partial-likelihood for models with 0 to nt components.}
#' \item{cv.se2}{Vector with the standard error values, across folds, of, per
#' fold unit, van Houwelingen Cross-validated log-partial-likelihood for models
#' with 0 to nt components.} \item{cv.se3}{Vector with the standard error
#' values, across folds, of iAUC_CD for models with 0 to nt components.}
#' \item{cv.se4}{Vector with the standard error values, across folds, of
#' iAUC_hc for models with 0 to nt components.} \item{cv.se5}{Vector with the
#' standard error values, across folds, of iAUC_sh for models with 0 to nt
#' components.} \item{cv.se6}{Vector with the standard error values, across
#' folds, of iAUC_Uno for models with 0 to nt components.} \item{cv.se7}{Vector
#' with the standard error values, across folds, of iAUC_hz.train for models
#' with 0 to nt components.} \item{cv.se8}{Vector with the standard error
#' values, across folds, of iAUC_hz.test for models with 0 to nt components.}
#' \item{cv.se9}{Vector with the standard error values, across folds, of
#' iAUC_survivalROC.train for models with 0 to nt components.}
#' \item{cv.se10}{Vector with the standard error values, across folds, of
#' iAUC_survivalROC.test for models with 0 to nt components.}
#' \item{cv.se11}{Vector with the standard error values, across folds, of
#' iBrierScore unw for models with 0 to nt components.} \item{cv.se12}{Vector
#' with the standard error values, across folds, of iSchmidScore (robust BS)
#' unw for models with 0 to nt components.} \item{cv.se13}{Vector with the
#' standard error values, across folds, of iBrierScore w for models with 0 to
#' nt components.} \item{cv.se14}{Vector with the standard error values, across
#' folds, of iSchmidScore (robust BS) w for models with 0 to nt components.}
#' \item{folds}{Explicit list of the values that were omited values in each
#' fold.} \item{lambda.min1}{Vector with the standard error values, across
#' folds, of, per fold unit, Cross-validated log-partial-likelihood for models
#' with 0 to nt components.} \item{lambda.min2}{Vector with the standard error
#' values, across folds, of, per fold unit, van Houwelingen Cross-validated
#' log-partial-likelihood for models with 0 to nt components.}
#' \item{lambda.min1}{Optimal Nbr of components, min Cross-validated
#' log-partial-likelihood criterion.} \item{lambda.se1}{Optimal Nbr of
#' components, min+1se Cross-validated log-partial-likelihood criterion.}
#' \item{lambda.min2}{Optimal Nbr of components, min van Houwelingen
#' Cross-validated log-partial-likelihood.} \item{lambda.se2}{Optimal Nbr of
#' components, min+1se van Houwelingen Cross-validated log-partial-likelihood.}
#' \item{lambda.min3}{Optimal Nbr of components, max iAUC_CD criterion.}
#' \item{lambda.se3}{Optimal Nbr of components, max+1se iAUC_CD criterion.}
#' \item{lambda.min4}{Optimal Nbr of components, max iAUC_hc criterion.}
#' \item{lambda.se4}{Optimal Nbr of components, max+1se iAUC_hc criterion.}
#' \item{lambda.min5}{Optimal Nbr of components, max iAUC_sh criterion.}
#' \item{lambda.se5}{Optimal Nbr of components, max+1se iAUC_sh criterion.}
#' \item{lambda.min6}{Optimal Nbr of components, max iAUC_Uno criterion.}
#' \item{lambda.se6}{Optimal Nbr of components, max+1se iAUC_Uno criterion.}
#' \item{lambda.min7}{Optimal Nbr of components, max iAUC_hz.train criterion.}
#' \item{lambda.se7}{Optimal Nbr of components, max+1se iAUC_hz.train
#' criterion.} \item{lambda.min8}{Optimal Nbr of components, max iAUC_hz.test
#' criterion.} \item{lambda.se8}{Optimal Nbr of components, max+1se
#' iAUC_hz.test criterion.} \item{lambda.min9}{Optimal Nbr of components, max
#' iAUC_survivalROC.train criterion.} \item{lambda.se9}{Optimal Nbr of
#' components, max+1se iAUC_survivalROC.train criterion.}
#' \item{lambda.min10}{Optimal Nbr of components, max iAUC_survivalROC.test
#' criterion.} \item{lambda.se10}{Optimal Nbr of components, max+1se
#' iAUC_survivalROC.test criterion.} \item{lambda.min11}{Optimal Nbr of
#' components, min iBrierScore unw criterion.} \item{lambda.se11}{Optimal Nbr
#' of components, min+1se iBrierScore unw criterion.}
#' \item{lambda.min12}{Optimal Nbr of components, min iSchmidScore unw
#' criterion.} \item{lambda.se12}{Optimal Nbr of components, min+1se
#' iSchmidScore unw criterion.} \item{lambda.min13}{Optimal Nbr of components,
#' min iBrierScore w criterion.} \item{lambda.se13}{Optimal Nbr of components,
#' min+1se iBrierScore w criterion.} \item{lambda.min14}{Optimal Nbr of
#' components, min iSchmidScore w criterion.} \item{lambda.se14}{Optimal Nbr of
#' components, min+1se iSchmidScore w criterion.} \item{errormat1-14}{If
#' \code{details=TRUE}, matrices with the error values for every folds across
#' each of the components and each of the criteria} \item{completed.cv1-14}{If
#' \code{details=TRUE}, matrices with logical values for every folds across
#' each of the components and each of the criteria: \code{TRUE} if the
#' computation was completed and \code{FALSE} it is failed.}
#' \item{All_indics}{All results of the functions that perform error
#' computation, for each fold, each component and error criterion.}
#' @author Frédéric Bertrand\cr
#' \email{frederic.bertrand@@utt.fr}\cr
#' \url{http://www-irma.u-strasbg.fr/~fbertran/}
#' @seealso See Also \code{\link{plsRcox}}
#' @references plsRcox, Cox-Models in a high dimensional setting in R, Frederic
#' Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
#' Proceedings of User2014!, Los Angeles, page 152.\cr
#'
#' Deviance residuals-based sparse PLS and sparse kernel PLS regression for
#' censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam
#' Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404,
#' doi:10.1093/bioinformatics/btu660.
#'
#' Cross validating extensions of kernel, sparse or regular partial least
#' squares regression models to censored data, Bertrand, F., Bastien, Ph. and
#' Maumy-Bertrand, M. (2018), \url{https://arxiv.org/abs/1810.01005}.
#' @keywords models regression
#' @examples
#'
#' data(micro.censure)
#' data(Xmicro.censure_compl_imp)
#' set.seed(123456)
#' X_train_micro <- apply((as.matrix(Xmicro.censure_compl_imp)),FUN="as.numeric",MARGIN=2)[1:80,]
#' X_train_micro_df <- data.frame(X_train_micro)
#' Y_train_micro <- micro.censure$survyear[1:80]
#' C_train_micro <- micro.censure$DC[1:80]
#'
#' #Should be run with a higher value of nt (at least 10)
#' (cv.plsRcox.res=cv.plsRcox(list(x=X_train_micro,time=Y_train_micro,status=C_train_micro),nt=3))
#'
#' @export cv.plsRcox
cv.plsRcox =
function (data, method = c("efron", "breslow"), nfold = 5, nt = 10, plot.it = TRUE,
se = TRUE, givefold, scaleX = TRUE, folddetails = FALSE, allCVcrit=FALSE,
details=FALSE, namedataset="data", save=FALSE, verbose=TRUE,...)
{
try(attachNamespace("survival"),silent=TRUE)
#on.exit(try(unloadNamespace("survival"),silent=TRUE))
try(attachNamespace("rms"),silent=TRUE)
on.exit(try(unloadNamespace("rms"),silent=TRUE))
cv.error1<-NULL;cv.error2<-NULL;cv.error3<-NULL;cv.error4<-NULL;cv.error5<-NULL;cv.error6<-NULL;cv.error7<-NULL;cv.error8<-NULL;cv.error9<-NULL;cv.error10<-NULL;cv.error11<-NULL;cv.error12<-NULL;cv.error13<-NULL;cv.error14<-NULL
cv.se1<-NULL;cv.se2<-NULL;cv.se3<-NULL;cv.se4<-NULL;cv.se5<-NULL;cv.se6<-NULL;cv.se7<-NULL;cv.se8<-NULL;cv.se9<-NULL;cv.se10<-NULL;cv.se11<-NULL;cv.se12<-NULL;cv.se13<-NULL;cv.se14<-NULL
lamin1<-NULL;lamin2<-NULL;lamin3<-NULL;lamin4<-NULL;lamin5<-NULL;lamin6<-NULL;lamin7<-NULL;lamin8<-NULL;lamin9<-NULL;lamin10<-NULL;lamin11<-NULL;lamin12<-NULL;lamin13<-NULL;lamin14<-NULL
completed.cv1<-NULL;completed.cv2<-NULL;completed.cv3<-NULL;completed.cv4<-NULL;completed.cv5<-NULL;completed.cv6<-NULL;completed.cv7<-NULL;completed.cv8<-NULL;completed.cv9<-NULL;completed.cv10<-NULL;completed.cv11<-NULL;completed.cv12<-NULL;completed.cv13<-NULL;completed.cv14<-NULL
method <- match.arg(method)
x <- data$x
time <- data$time
status <- data$status
n <- length(time)
if(missing(givefold)){
folds <- split(sample(seq(n)), rep(1:nfold, length = n))} else
{
folds <- givefold
}
number_ind = 14
titlesCV = c("Cross-validated log-partial-likelihood","van Houwelingen Cross-validated log-partial-likelihood","iAUC_CD","iAUC_hc","iAUC_sh","iAUC_Uno","iAUC_hz.train","iAUC_hz.test","iAUC_survivalROC.train","iAUC_survivalROC.test","iBrierScore unw","iSchmidScore (robust BS) unw","iBrierScore w","iSchmidScore (robust BS) w")
ylabsCV = c(rep("Minus log-partial-likelihood",2),rep("iAUC",8),rep("Prediction Error",4))
xlabsCV = c(rep("nbr of components",14))
signCVerror = c(rep(1,2),rep(-1,8),rep(1,4))
show_nbr_var = FALSE
for(ind in 1:number_ind) {
assign(paste("errormat",ind,sep=""),matrix(NA, nt+1, nfold))
}
for (i in seq(nfold)) {
for(ind in 1:number_ind) {
assign(paste("pred",ind,sep=""),rep(NA, nt+1))
}
omit <- folds[[i]]
trdata <- list(x = x[-omit, ], time = time[-omit], status = status[-omit])
tsdata <- list(x = x[omit, ], time = time[omit], status = status[omit])
if(!file.exists(paste("cv.plsRcox_",namedataset,"_folds_",i,".RData",sep=""))){
assign(paste("cv.plsRcox_",namedataset,"_folds_",i,sep=""),plsRcox(Xplan=trdata$x, time=trdata$time, event=trdata$status, method = method, nt=nt, allres=TRUE, verbose=verbose, ...))
if(save){save(list=c(paste("cv.plsRcox_",namedataset,"_folds_",i,sep="")),file=paste("cv.plsRcox_",namedataset,"_folds_",i,".RData",sep=""))}
} else {
load(paste("cv.plsRcox_",namedataset,"_folds_",i,".RData",sep=""))
}
coeffit=as.matrix(get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep=""))$CoeffCFull)
newxdata=as.matrix(predict(get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep="")),newdata=tsdata$x, comps=min(nt,get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep=""))$computed_nt), type="scores"))
oldxdata=as.matrix(predict(get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep="")), comps=min(nt,get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep=""))$computed_nt), type="scores"))
allxdata=as.matrix(predict(get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep="")),newdata=x, comps=min(nt,get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep=""))$computed_nt), type="scores"))
pred1[1] <- logplik(x=newxdata[,1,drop=FALSE], time=tsdata$time, status=tsdata$status, b=matrix(0), method = method,return.all = FALSE) #"efron" match with loglik of coxph
plfull <- logplik(x=allxdata[,1,drop=FALSE], time=time, status=status, b=matrix(0), method = method,return.all = FALSE) #"efron"
plminusk <- logplik(x=oldxdata[,1,drop=FALSE], time=trdata$time, status=trdata$status, b=matrix(0), method = method,return.all = FALSE) #"efron"
pred2[1] = plfull - plminusk
Xlp <- rep(0,length(time))
assign(paste("dataset_",namedataset,"_",0,sep=""),as.data.frame(cbind(time=time,status=status,Xlp=Xlp)))
TR <- get(paste("dataset_",namedataset,"_",0,sep=""))[-omit,]
TE <- get(paste("dataset_",namedataset,"_",0,sep=""))[omit,]
survival.time <- get(paste("dataset_",namedataset,"_",0,sep=""))[,"time"]
survival.status <- get(paste("dataset_",namedataset,"_",0,sep=""))[,"status"]
tr.survival.time <- trdata$time
tr.survival.status <- trdata$status
te.survival.time <- tsdata$time
te.survival.status <- tsdata$status
#require(survival)
Surv.rsp <- Surv(tr.survival.time, tr.survival.status)
Surv.rsp.new <- Surv(te.survival.time, te.survival.status)
train.fit <- coxph(Surv(time,status) ~ Xlp, x=TRUE, y=TRUE, method=method, data=TR, iter.max=0, init=1)
#library(rms)
train.fit.cph <- cph(Surv(time,status) ~ Xlp, x=TRUE, y=TRUE, method=method, data=TR, iter.max=0, init=1)
lp <- predict(train.fit)
lpnew <- predict(train.fit, newdata=TE)
if(allCVcrit){
AUCs <- getIndicCV(lp,lpnew,Surv.rsp,Surv.rsp.new,times.auc=seq(0,max(time),length.out=1000),times.prederr=seq(0,max(time),length.out=1000)[-(990:1000)],train.fit,plot.it=FALSE)
pred3[1] = AUCs$AUC_CD$iauc
pred4[1] = AUCs$AUC_hc$iauc
pred5[1] = AUCs$AUC_sh$iauc
pred6[1] = AUCs$AUC_Uno$iauc
pred7[1] = AUCs$AUC_hz.train$iauc
pred8[1] = AUCs$AUC_hz.test$iauc
pred9[1] = AUCs$AUC_survivalROC.train$iauc
pred10[1] = AUCs$AUC_survivalROC.test$iauc
pred11[1] = AUCs$prederr$brier.unw$ierror
pred12[1] = AUCs$prederr$robust.unw$ierror
pred13[1] = AUCs$prederr$brier.w$ierror
pred14[1] = AUCs$prederr$robust.w$ierror
} else {
AUCs <- getIndicCViAUCSH(lp,lpnew,Surv.rsp,Surv.rsp.new,times.auc=seq(0,max(time),length.out=1000),times.prederr=seq(0,max(time),length.out=1000)[-(990:1000)],train.fit,plot.it=FALSE)
pred3[1] = NA
pred4[1] = NA
pred5[1] = AUCs$AUC_sh$iauc
pred6[1] = NA
pred7[1] = NA
pred8[1] = NA
pred9[1] = NA
pred10[1] = NA
pred11[1] = NA
pred12[1] = NA
pred13[1] = NA
pred14[1] = NA
}
for(jj in 1:min(nt,get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep=""))$computed_nt)){
pred1[jj+1] <- logplik(x=newxdata[,1:jj,drop=FALSE], time=tsdata$time, status=tsdata$status, b=coeffit[1:jj,jj,drop=FALSE], method = method,return.all = FALSE) #"efron" match with loglik of coxph
plfull <- logplik(x=allxdata[,1:jj,drop=FALSE], time=time, status=status, b=coeffit[1:jj,jj,drop=FALSE], method = method,return.all = FALSE) #"efron"
plminusk <- logplik(x=oldxdata[,1:jj,drop=FALSE], time=trdata$time, status=trdata$status, b=coeffit[1:jj,jj,drop=FALSE], method = method,return.all = FALSE) #"efron"
pred2[jj+1] = plfull - plminusk
predict.trainvectjj <- as.matrix(predict(get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep="")),newdata=trdata$x, comps=jj, type="lp"))
predictvectjj <- as.matrix(predict(get(paste("cv.plsRcox_",namedataset,"_folds_",i,sep="")),newdata=tsdata$x, comps=jj, type="lp"))
Xlp <- rep(NA,length(time))
Xlp[-omit] <- predict.trainvectjj
Xlp[omit] <- predictvectjj
assign(paste("dataset_",namedataset,"_",jj,sep=""),as.data.frame(cbind(time=time,status=status,Xlp=Xlp)))
TR <- get(paste("dataset_",namedataset,"_",jj,sep=""))[-omit,]
TE <- get(paste("dataset_",namedataset,"_",jj,sep=""))[omit,]
survival.time <- get(paste("dataset_",namedataset,"_",jj,sep=""))[,"time"]
survival.status <- get(paste("dataset_",namedataset,"_",jj,sep=""))[,"status"]
tr.survival.time <- trdata$time
tr.survival.status <- trdata$status
te.survival.time <- tsdata$time
te.survival.status <- tsdata$status
#require(survival)
Surv.rsp <- Surv(tr.survival.time, tr.survival.status)
Surv.rsp.new <- Surv(te.survival.time, te.survival.status)
train.fit <- coxph(Surv(time,status) ~ Xlp, x=TRUE, y=TRUE, method=method, data=TR, iter.max=0, init=1) #offset
#library(rms)
train.fit.cph <- cph(Surv(time,status) ~ Xlp, x=TRUE, y=TRUE, method=method, data=TR, iter.max=0, init=1) #offset
lp <- predict(train.fit)
lpnew <- predict(train.fit, newdata=TE)
if(allCVcrit){
AUCs <- getIndicCV(lp,lpnew,Surv.rsp,Surv.rsp.new,times.auc=seq(0,max(time),length.out=1000),times.prederr=seq(0,max(time),length.out=1000)[-(990:1000)],train.fit,plot.it=FALSE)
pred3[jj+1] = AUCs$AUC_CD$iauc
pred4[jj+1] = AUCs$AUC_hc$iauc
pred5[jj+1] = AUCs$AUC_sh$iauc
pred6[jj+1] = AUCs$AUC_Uno$iauc
pred7[jj+1] = AUCs$AUC_hz.train$iauc
pred8[jj+1] = AUCs$AUC_hz.test$iauc
pred9[jj+1] = AUCs$AUC_survivalROC.train$iauc
pred10[jj+1] = AUCs$AUC_survivalROC.test$iauc
pred11[jj+1] = AUCs$prederr$brier.unw$ierror
pred12[jj+1] = AUCs$prederr$robust.unw$ierror
pred13[jj+1] = AUCs$prederr$brier.w$ierror
pred14[jj+1] = AUCs$prederr$robust.w$ierror
} else {
AUCs <- getIndicCViAUCSH(lp,lpnew,Surv.rsp,Surv.rsp.new,times.auc=seq(0,max(time),length.out=1000),times.prederr=seq(0,max(time),length.out=1000)[-(990:1000)],train.fit,plot.it=FALSE)
pred3[jj+1] = NA
pred4[jj+1] = NA
pred5[jj+1] = AUCs$AUC_sh$iauc
pred6[jj+1] = NA
pred7[jj+1] = NA
pred8[jj+1] = NA
pred9[jj+1] = NA
pred10[jj+1] = NA
pred11[jj+1] = NA
pred12[jj+1] = NA
pred13[jj+1] = NA
pred14[jj+1] = NA
}
}
if(allCVcrit){
if(is.na(pred10[1])){pred10[1]<-.5}
}
if (length(omit) == 1){
for(ind in 1:number_ind) {
assign(paste("pred",ind,sep=""),matrix(get(paste("pred",ind,sep="")), nrow = 1))
}
}
#if(any(is.na(pred10))){save(list=c("pred10"),file=paste(Predspath,"/failed.fold.cv.",typemodel,"_",namedataset,"_folds_",i,".RData",sep=""))}
if(allCVcrit){
errormat1[, i] <- ifelse(is.finite(pred1),-pred1/length(omit),NA)
errormat2[, i] <- ifelse(is.finite(pred2),-pred2/length(omit),NA)
errormat3[, i] <- ifelse(is.finite(pred3),pred3,NA)
errormat4[, i] <- ifelse(is.finite(pred4),pred4,NA)
errormat5[, i] <- ifelse(is.finite(pred5),pred5,NA)
errormat6[, i] <- ifelse(is.finite(pred6),pred6,NA)
errormat7[, i] <- ifelse(is.finite(pred7),pred7,NA)
errormat8[, i] <- ifelse(is.finite(pred8),pred8,NA)
errormat9[, i] <- ifelse(is.finite(pred9),pred9,NA)
errormat10[, i] <- ifelse(is.finite(pred10),pred10,NA)
errormat11[, i] <- ifelse(is.finite(pred11),pred11,NA)
errormat12[, i] <- ifelse(is.finite(pred12),pred12,NA)
errormat13[, i] <- ifelse(is.finite(pred13),pred13,NA)
errormat14[, i] <- ifelse(is.finite(pred14),pred14,NA)
} else {
errormat5[, i] <- ifelse(is.finite(pred5),pred5,NA)
}
if(verbose){cat("CV Fold", i, "\n")}
rm(list=c(paste("cv.plsRcox_",namedataset,"_folds_",i,sep="")))
}
if(allCVcrit){
for(ind in 1:number_ind) {
assign(paste("cv.error",ind,sep=""),apply(get(paste("errormat",ind,sep="")), 1, mean, na.rm=TRUE))
assign(paste("completed.cv",ind,sep=""),is.finite(get(paste("errormat",ind,sep=""))))
assign(paste("cv.se",ind,sep=""),sqrt(apply(get(paste("errormat",ind,sep="")), 1, var, na.rm=TRUE))/nfold)
assign(paste("lamin",ind,sep=""),getmin2(0:nt,signCVerror[ind]*get(paste("cv.error",ind,sep="")),get(paste("cv.se",ind,sep=""))))
}} else {
ind=5
assign(paste("cv.error",ind,sep=""),apply(get(paste("errormat",ind,sep="")), 1, mean, na.rm=TRUE))
assign(paste("completed.cv",ind,sep=""),is.finite(get(paste("errormat",ind,sep=""))))
assign(paste("cv.se",ind,sep=""),sqrt(apply(get(paste("errormat",ind,sep="")), 1, var, na.rm=TRUE))/nfold)
assign(paste("lamin",ind,sep=""),getmin2(0:nt,signCVerror[ind]*get(paste("cv.error",ind,sep="")),get(paste("cv.se",ind,sep=""))))
}
sign.lambda=1
if(allCVcrit){
object <- list(nt=nt, cv.error1 = cv.error1, cv.error2 = cv.error2, cv.error3 = cv.error3, cv.error4 = cv.error4, cv.error5 = cv.error5, cv.error6 = cv.error6, cv.error7 = cv.error7, cv.error8 = cv.error8, cv.error9 = cv.error9, cv.error10 = cv.error10, cv.error11 = cv.error11, cv.error12 = cv.error12, cv.error13 = cv.error13, cv.error14 = cv.error14,
cv.se1 = cv.se1, cv.se2 = cv.se2, cv.se3 = cv.se3, cv.se4 = cv.se4, cv.se5 = cv.se5, cv.se6 = cv.se6, cv.se7 = cv.se7, cv.se8 = cv.se8, cv.se9 = cv.se9, cv.se10 = cv.se10, cv.se11 = cv.se11, cv.se12 = cv.se12, cv.se13 = cv.se13, cv.se14 = cv.se14,
folds = folds,
lambda.min1 = lamin1[[1]], lambda.1se1 = lamin1[[2]], lambda.min2 = lamin2[[1]], lambda.1se2 = lamin2[[2]], lambda.min3 = lamin3[[1]], lambda.1se3 = lamin3[[2]], lambda.min4 = lamin4[[1]], lambda.1se4 = lamin4[[2]],
lambda.min5 = lamin5[[1]], lambda.1se5 = lamin5[[2]], lambda.min6 = lamin6[[1]], lambda.1se6 = lamin6[[2]], lambda.min7 = lamin7[[1]], lambda.1se7 = lamin7[[2]], lambda.min8 = lamin8[[1]], lambda.1se8 = lamin8[[2]],
lambda.min9 = lamin9[[1]], lambda.1se9 = lamin9[[2]], lambda.min10 = lamin10[[1]], lambda.1se10 = lamin10[[2]], lambda.min11 = lamin11[[1]], lambda.1se11 = lamin11[[2]], lambda.min12 = lamin12[[1]], lambda.1se12 = lamin12[[2]],
lambda.min13 = lamin13[[1]], lambda.1se13 = lamin13[[2]], lambda.min14 = lamin14[[1]], lambda.1se14 = lamin14[[2]]
)#sign.lambda=sign.lambda
if(folddetails){object <- c(object,list(errormat1 = errormat1, errormat2 = errormat2, errormat3 = errormat3, errormat4 = errormat4, errormat5 = errormat5, errormat6 = errormat6, errormat7 = errormat7, errormat8 = errormat8, errormat9 = errormat9, errormat10 = errormat10, errormat11 = errormat11, errormat12 = errormat12, errormat13 = errormat13, errormat14 = errormat14,
completed.cv1 = completed.cv1, completed.cv2 = completed.cv2, completed.cv3 = completed.cv3, completed.cv4 = completed.cv4, completed.cv5 = completed.cv5, completed.cv6 = completed.cv6, completed.cv7 = completed.cv7,
completed.cv8 = completed.cv8, completed.cv9 = completed.cv9, completed.cv10 = completed.cv10, completed.cv11 = completed.cv11, completed.cv12 = completed.cv12, completed.cv13 = completed.cv13, completed.cv14 = completed.cv14))}
if(details){object <- c(object,list(All_indics=AUCs))}
} else {
object <- list(nt=nt,cv.error5=cv.error5,cv.se5=cv.se5,folds=folds,lambda.min5=lamin5[[1]],lambda.1se5=lamin5[[2]])
if(folddetails){object <- c(object,list(errormat5 = errormat5, completed.cv5 = completed.cv5))}
}
if (plot.it) {
if(allCVcrit){
for(ind in 1:number_ind) {
if((ind%% 4)==1){dev.new();layout(matrix(1:4,nrow=2))}
plot((sign.lambda*(0:nt))[!is.nan(get(paste("cv.error",ind,sep="")))], get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))], type = "l", xlim=c(0,nt), ylim = range(c(get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] -
get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))], get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] + get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))])), xlab = xlabsCV[ind], ylab = ylabsCV[ind],
main = titlesCV[ind]
)
abline(v = sign.lambda*getElement(object,paste("lambda.min",ind,sep="")), lty = 3)
abline(v = sign.lambda*getElement(object,paste("lambda.1se",ind,sep="")), lty = 3, col="red")
if(show_nbr_var){axis(side = 3, at = sign.lambda*(0:nt), labels = paste(object$nzb), tick = FALSE, line = -1)}
if (se)
segments(sign.lambda*((0:nt)[!is.nan(get(paste("cv.error",ind,sep="")))]), get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] - get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))], sign.lambda*((0:nt)[!is.nan(get(paste("cv.error",ind,sep="")))]), get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] +
get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))])
}
layout(1)
} else {
ind=5
plot((sign.lambda*(0:nt))[!is.nan(get(paste("cv.error",ind,sep="")))], get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))], type = "l", xlim=c(0,nt), ylim = range(c(get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] -
get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))], get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] + get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))])), xlab = xlabsCV[ind], ylab = ylabsCV[ind],
main = titlesCV[ind]
)
abline(v = sign.lambda*getElement(object,paste("lambda.min",ind,sep="")), lty = 3)
abline(v = sign.lambda*getElement(object,paste("lambda.1se",ind,sep="")), lty = 3, col="red")
if(show_nbr_var){axis(side = 3, at = sign.lambda*(0:nt), labels = paste(object$nzb), tick = FALSE, line = -1)}
if (se)
segments(sign.lambda*((0:nt)[!is.nan(get(paste("cv.error",ind,sep="")))]), get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] - get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))], sign.lambda*((0:nt)[!is.nan(get(paste("cv.error",ind,sep="")))]), get(paste("cv.error",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))] +
get(paste("cv.se",ind,sep=""))[!is.nan(get(paste("cv.error",ind,sep="")))])
}
}
invisible(object)
}