R/hybridRecursive.R

#------------------------------------------------------------------------------
#' hybridRecursive trimming procedure.
#'
#' \code{hybridRecursive} takes a data frame of RT data and returns trimmed rt
#' data. The returned value is the average returned from the nonRecursive
#' and the modifiedRecursive procedures  as described in van Selst &
#' Jolicoeur (1994).
#'
#' @param data A data frame. It must contain columns named "participant",
#' "condition", "rt", and "accuracy". The RT can be in seconds
#' (e.g., 0.654) or milliseconds (e.g., 654). Typically, "condition" will
#' consist of strings. "accuracy" must be 1 for correct and 0 for error
#' responses.
#' @param minRT The lower criteria for acceptable response time. Must be in
#' the same form as rt column in data frame (e.g., in seconds OR milliseconds).
#' All RTs below this value are removed before proceeding with SD trimming.
#' @param omitErrors If set to TRUE, error trials will be removed before
#' conducting trimming procedure. Final data returned will not be influenced
#' by errors in this case.
#' @param digits How many decimal places to round to after trimming?
#'
#' @references Van Selst, M. & Jolicoeur, P. (1994). A solution to the effect
#' of sample size on outlier elimination. \emph{Quarterly Journal of Experimental
#' Psychology, 47} (A), 631-650.
#'
#' @examples
#' # load the example data that ships with trimr
#' data(exampleData)
#'
#' # perform the trimming, returning mean RT
#' trimmedData <- hybridRecursive(data = exampleData, minRT = 150)
#'
#' @importFrom stats sd
#'
#' @export
hybridRecursive <- function(data, minRT, omitErrors = TRUE, digits = 3){


  # remove errors if the user has asked for it
  if(omitErrors == TRUE){
    trimmedData <- subset(data, data$accuracy == 1)
  } else {
    trimmedData <- data
  }

  # get the list of participant numbers
  participant <- sort(unique(trimmedData$participant))

  # get the list of experimental conditions
  conditionList <- unique(trimmedData$condition)

  # trim the data to remove trials below minRT
  trimmedData <- subset(trimmedData, trimmedData$rt > minRT)

  # ready the final data set
  finalData <- matrix(0, nrow = length(participant),
                      ncol = length(conditionList))

  # give the columns the condition names
  colnames(finalData) <- conditionList

  # add the participant column
  finalData <- cbind(participant, finalData)

  # convert to data frame
  finalData <- data.frame(finalData)

  # intialise looping variable for subjects
  i <- 1

  # loop over all subjects
  for(currSub in participant){

    # intialise looping variable for conditions. It starts at 2 because the
    # first column in the data file containing condition information is the
    # second one.
    j <- 2

    # loop over all conditions
    for(currCond in conditionList){

      # get the relevant data
      tempData <- subset(trimmedData, trimmedData$participant == currSub &
                           trimmedData$condition == currCond)


      # get the nonRecursive mean
      nonR <- nonRecursiveTrim(tempData$rt)

      # get the modifiedRecursive mean
      modR <- modifiedRecursiveTrim(tempData$rt)

      # find the average, and add to the data frame
      finalData[i, j] <- round(mean(c(nonR, modR)), digits = digits)

      # update condition loop counter
      j <- j + 1
    }

    # update participant loop counter
    i <- i + 1
  }
  return(finalData)
}

#------------------------------------------------------------------------------

Try the trimr package in your browser

Any scripts or data that you put into this service are public.

trimr documentation built on May 2, 2019, 5:54 a.m.