R/rrwPlotSGSoutputByGroup.r

In ccpluncw/ccpl_R_RRW: What the Package Does (One Line, Title Case)

#' This function plots the rrw data fits from the output of getMeanRRWfit() and the rrwModelList.
#'
#' Function that plots the rrw data fits from the output of getMeanRRWfit() and the rrwModelList.
#' @param df.RRWout This is a dataframe containing the output from getMeanRRWfit() or rrwRunWithFixedParameters () or rrwRunSmartGridSearch (). It contains the rrwData and the simulation fits.
#' @param grpCols A vector of strings containing the grouping columns. If NULL this argument will be ignored.  DEFAULT = NULL
#' @param dataRtCol A string that identifies the name of the column in data that contains the RTs for the specific overlap/correct/condition combination. Default is "rt"
#' @param dataPhitCol A string that identifies the name of the column in data that contains the proportion of trials that are either correct or incorrect for the specific overlap/correct/condition combination. Default is "pHit"
#' @param rtFitCol A string that identifies the name of the column in data that contains the RRW fit of the RTs for the specific overlap/correct/condition combination. Default is "rt"
#' @param pHitFitCol A string that identifies the name of the column in data that contains the RRW fit of the proportion of trials that are either correct or incorrect for the specific overlap/correct/condition combination. Default is "pCross"
#' @param correctCol a boolean that specifies the name of the column that specifies if response was correct or incorrect.  It must be coded as a boolean, whereby correct == TRUE, and incorrect == FALSE. The default is "correct"
#' @param overlapCol A string that identifies the name of the column in data that contains the distributional overlaps for each row. The default is "overlap"
#' @param fileTag A string that is appended to the name of files to identify the analysis and experiment. The default is NULL, whereby the filetag will just be based on a timestamp.
#' @param numSimsToPlot A number specifying how many simulation runs are in the dataset and should be plotted. Default is 10.
#''
#' @return Several files containing plots are saved.  The plots are duplicated: one version with each simulation plus the average and one with just the average.
#' @keywords RRW plots
#' @export
#' @examples rrwPlotSGSoutput(df.fitted, "rt", "pHit", "rtFit", "pCross", "correct", "overlap", fileTag = NULL, numSimsToPlot = 40)

rrwPlotSGSoutputByGroup <- function (df.RRWout, grpCols = NULL, dataRtCol = 'rt', dataPhitCol = 'pHit', rtFitCol = 'rtFit', pHitFitCol = "pCross", correctCol = "correct", overlapCol = "overlap",fileTag = NULL, numSimsToPlot = 10) {

  #if fileTag does not exist, create one based on the timestamp
  if(is.null(fileTag)) {
    fileTag <- paste(format(Sys.time(), "%b_%d_%Y_%H-%M"), "RRW", sep="_")
  }

  #create the plotname
  plotFilename <- paste(fileTag,"FitResults.pdf")
  #get the xy axis bounds
  yMinMixRT <- chutils::ch.getPlotAxisMinMax(df.RRWout[[dataRtCol]])

  #create a condition variable that codes all the conditions in a single variable
  #if there are grouping variables
  if(!is.null(grpCols)) {
    #how many grouping variables
    nGrpsCols <- length(grpCols)
    #for each group
    for (i in grpCols) {
      #if it is the first group
      if(i == grpCols[1]) {
        #initiate the variable
        df.RRWout$cond <- df.RRWout[[i]]
      } else {
        #otherise, just add values to the variable
        df.RRWout$cond <- paste(df.RRWout$cond, df.RRWout[[i]], sep="-")
      }
    }
  }

  #for each grouping variable, plot the data with all the simulations and without all the simulations (only the mean simulation).
  if(!is.null(grpCols)) {
    nGrpsCols <- length(grpCols)
    for (i in grpCols) {
      tmpConds <- unique(df.RRWout[[i]])
      for(j in tmpConds) {
        #plot with individual simulations
        if(numSimsToPlot > 0) {
          plotRRWFit2(df.RRWout[df.RRWout[[i]] == j,], dataRtCol, dataPhitCol,  rtFitCol, pHitFitCol, correctCol, overlapCol, condCol ="cond",  plotFilename = paste("withSims-",i,"-",j, plotFilename, sep=""), yMinMixRT=yMinMixRT, numSimsToPlot=numSimsToPlot)
        }
        #plot without individual simulations
        plotRRWFit2(df.RRWout[df.RRWout[[i]] == j,], dataRtCol, dataPhitCol,  rtFitCol, pHitFitCol, correctCol, overlapCol, condCol ="cond",  plotFilename = paste(i,"-",j, plotFilename, sep=""), yMinMixRT=yMinMixRT, numSimsToPlot=0)
      }
    }
  }

  #for the condition variable, plot the data with all the simulations and without all the simulations (only the mean simulation).
  if ("cond" %in% colnames(df.RRWout)) {
    if(numSimsToPlot > 0) {
      #plot with individual simulations
      plotRRWFit2(df.RRWout, dataRtCol, dataPhitCol,  rtFitCol, pHitFitCol, correctCol, overlapCol, condCol = "cond", plotFilename = paste("withSim-cond", plotFilename), yMinMixRT=yMinMixRT, numSimsToPlot=numSimsToPlot)
    }
    #plot without individual simulations
    plotRRWFit2(df.RRWout, dataRtCol, dataPhitCol,  rtFitCol, pHitFitCol, correctCol, overlapCol, condCol = "cond", plotFilename = paste("cond", plotFilename), yMinMixRT=yMinMixRT, numSimsToPlot=0)
  } else {
    if(numSimsToPlot > 0) {
      #plot with individual simulations
      plotRRWFit2(df.RRWout, dataRtCol, dataPhitCol,  rtFitCol, pHitFitCol, correctCol, overlapCol,  plotFilename = paste("withSim-simple", plotFilename), yMinMixRT=yMinMixRT, numSimsToPlot=numSimsToPlot)
    }
    #plot without individual simulations
    plotRRWFit2(df.RRWout, dataRtCol, dataPhitCol,  rtFitCol, pHitFitCol, correctCol, overlapCol,  plotFilename = paste("simple", plotFilename), yMinMixRT=yMinMixRT, numSimsToPlot=0)
  }

}