version 0.2.0

cran · May 31, 2019 · 58f8762 · 58f8762
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: dfConn
 Type: Package
 Title: Dynamic Functional Connectivity Analysis
-Version: 0.1.1
+Version: 0.2.0
 Authors@R: c(person("Zikai", "Lin", email = "ziklin@iu.edu", role = c("aut", "cre")),
             person("Maria", "Kudela", email = "maria.kudela@gmail.com", role = c("aut")),
             person("Jaroslaw","Harezlak",email = "harezlak@iu.edu", role = c("aut")),
@@ -20,10 +20,10 @@ Imports: doParallel, nlme, parallel, foreach, ggplot2, fields, gplots,
 LinkingTo: Rcpp, RcppArmadillo
 RoxygenNote: 6.1.1
 NeedsCompilation: yes
-Packaged: 2019-03-25 19:38:03 UTC; ziklin
+Packaged: 2019-05-31 17:04:05 UTC; ziklin
 Author: Zikai Lin [aut, cre],
   Maria Kudela [aut],
   Jaroslaw Harezlak [aut],
   Mario Dzemidzic [aut]
 Repository: CRAN
-Date/Publication: 2019-03-25 22:33:20 UTC
+Date/Publication: 2019-05-31 18:00:04 UTC
diff --git a/MD5 b/MD5
@@ -1,38 +1,41 @@
-7c5d452414e32716ab103c43a94f9ad0 *DESCRIPTION
+4e06c0c90cf80ef78ba914e1bec878ca *DESCRIPTION
 8dc4dd367716da3d741838c6a8e830ed *LICENSE
-93b7a7047065da580135d8dce6890273 *NAMESPACE
+21da86b6ae198539350ff255573f58f5 *NAMESPACE
 1e70ac2c81d1c915a6bf6206f4b5638c *R/MLPB3.R
 09467175ccefa10462de4608b2b83769 *R/RcppExports.R
 c892bfcedfa1dc4dc17d51670001314d *R/data_summary_coverage.r
 771904ec595c41c37a5eaaa284955008 *R/dfConn.R
-66199f7cbe55168ee969ec3faeb4c8ac *R/lme_model0.r
-3d89d6ca2e1f15a38b1756cea97b1c18 *R/lme_model6.r
+d5671cf83533706468f3c8b415b08ee7 *R/lme_model0.r
+24545a08e008c35ae321f7dc5af9d147 *R/lme_model6.r
 1ad2e4f3571ca0d04394a2ca5ee582f6 *R/lmm_ci_vis.R
-40bb28bb965160ee43c9b43403ef4fc9 *R/mlpb.R
+10de9afd3f87506ed921495122df2533 *R/mlpb.R
 48345987ba7c60695901db2d4eb808ff *R/nzc_mat_construct.r
-a2ca620d8352fc8a38d8c2ebbf8e4d48 *R/nzc_mat_vis.r
+1fb730251ba5da8c448d5b9023af4961 *R/nzc_mat_vis.r
+908ccbd25d8d7d557de29661f04c0d97 *R/option_control.R
 a2f439f6db7c754a7f0182f6143947f4 *R/plots.r
 61f720d5cb54596384caeae466f95993 *R/setters.R
-8d39b9c394ee48e22b7e392e9db4d594 *R/summary_table.r
+2c37a0831138fdb226f49ea2d7864a03 *R/summary_table.r
 852948da1bed1d808d2a3739de5ae977 *R/sysdata.rda
-1137d1ad613ebc8fba702abfb16ca33d *R/utils.r
+ca61c4002748c53a11a6f8ae5bc110de *R/utils.r
 929fb0a54af8e643467cf9a4a5a63a25 *README.md
 1dc116c571fd89cef1cbde6dadb83f66 *data/DynModel_results.rda
 c48f1474670b3f46d96982ecdade3d4d *data/MLPB_output_median.rda
 24944c332629f20dd1c739319178572f *data/coverage.tbl.list.rda
-d4fbd1314ed2648a8021c1336de9e6c1 *data/fMRI_dataList.rda
+1744924f0cbdb447bc3ff7675bdf0b6e *data/fMRI_dataList.rda
 c9bb9b3e1e798019ffa7f7483640cd0e *data/fMRI_dataList_shrinked.rda
 5073b53fa8f3088d8a6e4803a1f5e692 *man/DynModel_results.Rd
 709765a2b7c36d468f0676dfe24116f6 *man/MLPB3.Rd
-52d620f6e47149e5ab257c5ab674e824 *man/MLPB_boot.Rd
+1acaf55e0c903f25f2a50591b11e7eed *man/MLPB_boot.Rd
 ab420afbcb3b38f9209756efe02161a7 *man/MLPB_output_median.Rd
 66fb1817a2bcbca31c12fcb78a571dda *man/coverage.tbl.list.Rd
 625cd173de4cf8b896e448d235bb5225 *man/data_summary_coverage.Rd
 0a987d54dde8932b4799ed5867749d36 *man/fMRI_dataList.Rd
 97f3abf819c842e4d85af868b90cbd20 *man/fMRI_dataList_shrinked.Rd
-c1d5ee13d0e7343a822a3a1cdd63cb9d *man/lmmConn.Rd
-f8041479fe05ef1ed078a5af8654a54a *man/lmmDyn.Rd
-294039488892ec38f4342b58d921a1ca *man/nzc_vis.Rd
+fd810e889c79691c69ebfa7949d03bd4 *man/lmmConn.Rd
+dedb9ba01a029aa4047721ee905c6698 *man/lmmDyn.Rd
+eb173443628e7863b450e8e5a412ac91 *man/nzc_vis.Rd
+d1be64aa81c5dcd92371d580ee64fc88 *man/options_boot.Rd
+be4c02b527ab9454336b4bb4f084eab0 *man/options_lme.Rd
 f103a402addfdebb68cca4c21b339339 *man/plot_generate_timeDependence.Rd
 e483460c28b1b4c7c1bf56ade4d68c3b *src/MLPB3.cpp
 61059660eb073d93e00e8ee054237071 *src/Makevars

diff --git a/NAMESPACE b/NAMESPACE
@@ -6,6 +6,8 @@ export(data_summary_coverage)
 export(lmmConn)
 export(lmmDyn)
 export(nzc_vis)
+export(options_boot)
+export(options_lme)
 export(plot_generate_timeDependence)
 import(Rcpp)
 importFrom(Rcpp,evalCpp)
@@ -17,5 +19,6 @@ importFrom(stats,mad)
 importFrom(stats,median)
 importFrom(stats,qnorm)
 importFrom(stats,quantile)
+importFrom(utils,combn)
 importFrom(utils,write.csv)
 useDynLib(dfConn)
diff --git a/R/lme_model0.r b/R/lme_model0.r
@@ -2,16 +2,7 @@
 #' 
 #' @title Semiparametric modelling - Static
 #' @param dataList list, list of data matrices .
-#' @param subjects character vector, names of subjects.
-#' @param eff_time_points integer vector, effective sacn timepoints.
-#' @param num.scan integer, number of scans.
-#' @param ntps.per.scan integer, number of timepoints per scan.
-#' @param save_res logical, whether to save the result or not, if true, an output directory should be provided.
-#' @param output_dir character, directory for output files.
-#' @param cores integer, number of cores to register for parallel jobs.
-#' @param seed numeric, random seed.
-#' @param ngrid integer, number of grids.
-#' @param ci_level numeric, level of confidence interval, default is 0.975.
+#' @param op_lme list, options constructed by function \code{options_lme}, see \code{options_lme}.
 #' @importFrom utils write.csv
 #' @importFrom parallel makePSOCKcluster
 #' @details 
@@ -55,7 +46,12 @@
 #' num.scan <- 6 # Each subject has 6 scans
 #' ntps.per.scan <- 105 # Each scan has 105 time points
 #' 
-#' resConn <- lmmConn(MLPB_output_median, subjects, time.points, num.scan, ntps.per.scan)
+#' op <- options_lme(effective_tp = time.points, 
+#'                  ntps.per.scan = ntps.per.scan,
+#'                  subjects = subjects, 
+#'                  num.scan = num.scan)
+#'                  
+#' resConn <- lmmConn(MLPB_output_median, op)
 #' \dontshow{
 #' rm(list = c('subjects', 'MLPB_output_median', 'time.points', 'num.scan', 'ntps.per.scan','resConn'))
 #' gc()}
@@ -65,8 +61,22 @@
 
 
 
-lmmConn <- function(dataList, subjects, eff_time_points, num.scan, ntps.per.scan, save_res = FALSE, output_dir = NULL, ci_level = 0.975, ngrid = 201, cores = 1, seed = NULL) {
+lmmConn <- function(dataList, op_lme) {
 
+
+  # Load input arguments from options list
+  subjects <- op_lme$subjects
+  eff_time_points <- op_lme$effective_tp
+  num.scan <- op_lme$num.scan
+  ntps.per.scan <- op_lme$ntps.per.scan
+  output_dir <- op_lme$output_dir
+  ci_level <- op_lme$ci_level
+  cores <- op_lme$cores
+  ngrid <- op_lme$ngrid
+  seed <- op_lme$seed
+  parallel_run <- op_lme$parallel_run
+  save_output <- op_lme$save_output
+
     requireNamespace("foreach")
     num.subjects <- length(subjects)
 
@@ -76,7 +86,7 @@ lmmConn <- function(dataList, subjects, eff_time_points, num.scan, ntps.per.scan
 
     files.to.run.analysis <- dataList
 
-    if (isTRUE(save_res)) {
+    if (isTRUE(save_output)) {
         if (!is.null(output_dir)) {
           if (!dir.exists(output_dir)) {
             dir.create(output_dir)
@@ -159,17 +169,16 @@ lmmConn <- function(dataList, subjects, eff_time_points, num.scan, ntps.per.scan
 
         # dummy data
         data.data2 <- data.data1
-        GPTS <- subjects
 
         if (num.of.sub != num.subjects) {
-            GPTS <- GPTS[-c(num_NA_series)]
+          subjects <- subjects[-c(num_NA_series)]
         } else {
-            GPTS <- GPTS
+          subjects <- subjects
         }
 
         ###### data for 6 scans
         full.ntps <- ntps.per.scan * num.scan
-        subject <- as.vector(t(matrix(rep(GPTS, full.ntps), num.of.sub, full.ntps)))
+        subject <- as.vector(t(matrix(rep(subjects, full.ntps), num.of.sub, full.ntps)))
         long.scans.est <- as.vector(t(data.data1))
         scan <- c()
         for (i in 1:num.scan) {
@@ -251,7 +260,7 @@ lmmConn <- function(dataList, subjects, eff_time_points, num.scan, ntps.per.scan
             output.0.row[size.fixed + 7] <- name.rdata.save1
             output.0.row[size.fixed + 8] <- minus.subj
 
-            if (isTRUE(save_res)) {
+            if (isTRUE(save_output)) {
                 utils::write.csv(output.0.row, paste(path4, "outputConn_REML_", name.rdata.save1, ".csv", sep = ""), row.names = FALSE)
             }
 
@@ -320,7 +329,7 @@ lmmConn <- function(dataList, subjects, eff_time_points, num.scan, ntps.per.scan
 
             output.0.row[(length(output.0.row) + 1)] <- sum(diag(diag.S))
 
-            if (isTRUE(save_res)) {
+            if (isTRUE(save_output)) {
                 utils::write.csv(output.0.row, file.path(path5, paste("outputConn_REML_", name.rdata.save1, ".csv", sep = "")), row.names = FALSE)
             }
 
@@ -374,7 +383,7 @@ lmmConn <- function(dataList, subjects, eff_time_points, num.scan, ntps.per.scan
             est.CI[8, ] <- lowerg.Gat
             est.CI[9, ] <- upperg.Gat
 
-            if (isTRUE(save_res)) {
+            if (isTRUE(save_output)) {
                 utils::write.csv(est.CI, file.path(path3, paste("modelConn_REML_lme", "_", name.rdata.save1, ".csv", sep = "")), row.names = FALSE)
                 save(output.0, file = file.path(path1, paste("output_fit0_REML", "_", name.rdata.save1, ".RData", sep = "")))
             }
@@ -400,7 +409,7 @@ lmmConn <- function(dataList, subjects, eff_time_points, num.scan, ntps.per.scan
         "Xintercept_difference_DF", "Xintercept_difference_t-value", "Xintercept_difference_p-value", "AIC", "BIC", "logLik", "sigma_eps", "sigma_b0_unstruct", "sigma_a_unstruct", 
         "comparison", "missing_series", "edf")
 
-    if (isTRUE(save_res)) {
+    if (isTRUE(save_output)) {
         write.csv(output.m0, file.path(path2, paste("modelConn_REML_lme_all.csv", sep = "")), row.names = FALSE)
     }
     #class(output_obj) <- "dFClmm"