add NBS_lme and clean up unused functions

Author: CogBrainHealthLab

DESCRIPTION

@@ -24,7 +24,6 @@ Imports:
     cowplot,
     doParallel,
     doSNOW,
-    fMRItools,
     foreach,
     ggplot2,
     ggplotify,

NAMESPACE

@@ -3,6 +3,7 @@
 export(CIFTI_subj_avg)
 export(CIFTItoFC)
 export(NBS)
+export(NBS_lme)
 export(check_cifti)
 export(cpm.lesion)
 export(cpm.predict)
@@ -11,7 +12,6 @@ export(cpm.train.cv)
 export(edgelist)
 export(editJSON_IntendedFor)
 export(extract.edges)
-export(extractFC)
 export(extractFS.ROI)
 export(extract_headmotion)
 export(extract_links)
@@ -21,11 +21,11 @@ export(headmotion.XCP)
 export(headmotion.fmriprep)
 export(intersubject_similarity)
 export(reorder_subcortical)
-export(vectorizeFC)
 export(vizChord)
 export(vizConnectogram)
 export(vizGlassbrain)
 export(vizHeatmap)
+importFrom(Rfast,rint.reg)
 importFrom(caret,createFolds)
 importFrom(ciftiTools,ciftiTools.setOption)
 importFrom(ciftiTools,move_from_mwall)
@@ -38,8 +38,6 @@ importFrom(cowplot,get_plot_component)
 importFrom(cowplot,plot_grid)
 importFrom(doParallel,registerDoParallel)
 importFrom(doSNOW,registerDoSNOW)
-importFrom(fMRItools,dct_bases)
-importFrom(fMRItools,nuisance_regression)
 importFrom(foreach,"%dopar%")
 importFrom(foreach,foreach)
 importFrom(ggplot2,aes)

R/HCP_dat_func.R

@@ -46,108 +46,3 @@ extract_headmotion=function(filename="motiondat.csv",start=1)
   write.table(motiondat, file = filename,sep = ",",quote = F,row.names = F)
   cat(paste0("headmotion data saved to ",filename))
 }
-########################################################################################################
-########################################################################################################
-
-#' @title vectorizeFC
-#'
-#' @description vectorizing and concatenating and M x M matrices
-#'
-#' @details This function reads the M x M matrices (saved as .csv files) in a directory, vectorizes and concatenates them into a single N x ((M x M)-M)/2 matrix. N= number of subjects; M=number of nodes in the connectome
-#'
-#' @param FC_dir the directory containing the M x M matrices (saved as .csv files)
-#' @param filename the output format of the concatenated group level matrix. Only filenames with the *.csv or *.rds extensions are accepted. The .rds format uses less disk space. Set to 'FCmat.rds' by default
-#' @param task A string to select the .csv files with a specific taskname.
-#' @param fisherz `TRUE` or `FALSE` option specifying if the edge values should undergo a fisher-Z transformation. This would be applicable in cases and functional connectivity is calculated as a pearson's correlation coefficient. Set to `TRUE` by default.
-#' @param timeseries `TRUE` or `FALSE` option specifying if the parcellated timeseries are used as inputs. Set to `FALSE` by default.
-#' @returns Depending on whether a filename with a .csv or .rds file extension is specified, outputs a .csv file containing the  N x ((M x M)-M)/2 matrix or a .rds file containing a list object: 1)N x ((M x M)-M)/2 matrix, 2) a vector of subject IDs.
-#'
-#' @examples
-#' \dontrun{
-#' vectorizeFC(FC_dir="FCmat", filename="FCmat.rds", fisherz=TRUE)
-#' }
-#' @importFrom stringr str_detect
-#' @importFrom psych fisherz
-#' @export
-#'
-#'
-########################################################################################################
-########################################################################################################
-vectorizeFC=function(FC_dir, filename="FCmat.rds", task, fisherz=TRUE, timeseries=FALSE)
-{
-  ##check filename extension
-  if(stringr::str_detect(string=filename, pattern=".csv")==F & stringr::str_detect(string=filename, pattern=".rds")==F)
-  {
-    stop("invalid filename extension")
-  }
-  if(missing("task"))  {subjlist=list.files(path=FC_dir)}
-  else {subjlist=list.files(path=FC_dir,pattern=task)}
-
-  if(timeseries==F)
-  {
-    FCformat=read.csv(paste(FC_dir,"/",subjlist[1], sep=""), header=F)
-
-    #if subjects' FCmat is already vectorized
-    if(dim(FCformat)[1]==1)
-    {
-      FCmat=matrix(NA,nrow=NROW(subjlist), ncol=dim(FCformat)[2])
-      for(sub in 1:NROW(subjlist))
-      {
-        FCmat[sub,]=unlist(read.table(paste(FC_dir,"/",subjlist[sub], sep=""),header = F,sep=",",col.names = F))
-        cat(paste(subjlist[sub],"\n"))
-      }
-      if(fisherz==T)
-      {
-        FCmat=psych::fisherz(FCmat)
-      }
-    }
-
-    #if subjects' FCmat has not been vectorized
-    if(dim(FCformat)[1]>1)
-    {
-      x=dim(FCformat)[1]
-      FCmat=matrix(NA,nrow=NROW(subjlist), ncol=((x*x-x)/2))
-      for(sub in 1:NROW(subjlist))
-      {
-        FCmat.temp=read.table(paste(FC_dir,"/",subjlist[sub], sep=""),header = F,sep=",")
-        FCmat[sub,]=FCmat.temp[upper.tri(FCmat.temp, diag = FALSE)]
-        cat(paste(subjlist[sub],"\n"))
-        remove(FCmat.temp)
-      }
-      if(fisherz==T)
-      {
-        FCmat=psych::fisherz(FCmat)
-      }
-    }
-  } else if(timeseries==T)
-  {
-    FCformat=read.csv(paste(FC_dir,"/",subjlist[1], sep=""), header=F)
-    x=NCOL(FCformat)
-    FCmat=matrix(NA,nrow=NROW(subjlist), ncol=((x*x-x)/2))
-    for(sub in 1:NROW(subjlist))
-    {
-      FCmat.temp=read.table(paste(FC_dir,"/",subjlist[sub], sep=""),header = F,sep=",")
-      FCmat[sub,]=FCmat.temp[upper.tri(cor(FCmat.temp), diag = FALSE)]
-      cat(paste(subjlist[sub],"\n"))
-      remove(FCmat.temp)
-    }
-    if(fisherz==T)
-    {
-      FCmat=psych::fisherz(FCmat)
-    }
-  }
-
-  ##output file
-  if(stringr::str_detect(string=filename, pattern=".csv"))
-  {
-    write.table(FCmat,file = filename,row.names = F,col.names = F, sep=",")
-    cat(paste("Concatenated FC matrices saved to",filename,"\n",sep=" "))
-  } else if(stringr::str_detect(string=filename, pattern=".rds"))
-  {
-    saveRDS(list(FCmat,gsub(".csv","",subjlist)),file = filename)
-    cat(paste("Concatenated FC matrices saved to",filename,"\n",sep=" "))
-  }
-}
-
-########################################################################################################
-########################################################################################################