StimulationResponseTimingPython/run_TOJ_stats.R at master · uwgridlab/StimulationResponseTimingPython · GitHub

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setwd('C:/Users/david/SharedCode/StimulationResponseTimingPython')

library('here')
library('ggplot2')
library('FSA')
library('rcompanion')
library("dplyr")
library('nortest')
library('onewaytests')

rootDir = here()
dataDir = here()

sidVec <- c('a1355e','3ada8b','822e26')
#sidVec <- c('a1355e')

savePlot = 0
figWidth = 8
figHeight = 6

dataList = list()
kruskalList = list()
adList= list()
summaryList = list()
cldSubjs = list()
ptList = list()
forsytheList = list()

index = 1
for (sid in sidVec){

  if(sid == 'a1355e'){
  dataRT <- read.table(here(paste0(sid,'_TOJ.csv')),header=TRUE,sep = ",",stringsAsFactors=F,
                       colClasses=c("firstFelt"="factor","firstFeltSame"="factor","firstFeelNameSame"="factor"))
  dataRT <- na.omit(dataRT)

  summaryList[[index]] <- dataRT %>%
    group_by(firstFeelNameSame) %>%
    select(firstFeelNameSame,responseTimes..ms.) %>%
    summarise_all(funs(n = n(),
                       min = min,
                       q25 = quantile(., 0.25),
                       median = median,
                       q75 = quantile(., 0.75),
                       max = max,
                       mean = mean,
                       sd = sd))

  dataRT <- dataRT %>%
    add_count(firstFeelNameSame)

  adList[[index]] <- dataRT %>%
    group_by(firstFeelNameSame) %>%
    filter(n>7) %>%
    summarize(adTest = ad.test(responseTimes..ms.)[[2]])

  dataForsythe <- dataRT %>%
    filter(n>5)


  kruskalList[[index]] <- kruskal.test(responseTimes..ms. ~ firstFeelNameSame,data=dataForsythe)
  summary


  forsytheList[[index]] <- bf.test(responseTimes..ms. ~ firstFeelNameSame, data=dataForsythe, alpha = 0.05, na.rm = TRUE, verbose = TRUE)

    PT = dunnTest(responseTimes..ms. ~ firstFeelNameSame,data=dataRT,
                  method="sidak")    # Can adjust p-values;
    PT = PT$res

    ptList[[index]] = PT

    cldSubjs[[index]] <-cldList(comparison = PT$Comparison,
                                p.value    = PT$P.adj,
                                threshold  = 0.05)

  }else{
    dataRT <- read.table(here(paste0(sid,'_TOJ.csv')),header=TRUE,sep = ",",stringsAsFactors=F,
                         colClasses=c("firstFeelName"="factor","firstFelt"="factor"))
    dataRT <- na.omit(dataRT)

    summaryList[[index]] <- dataRT %>%
      group_by(firstFeelName) %>%
      select(firstFeelName,responseTimes..ms.) %>%
      summarise_all(funs(n = n(),
                         min = min,
                         q25 = quantile(., 0.25),
                         median = median,
                         q75 = quantile(., 0.75),
                         max = max,
                         mean = mean,
                         sd = sd))

    dataRT <- dataRT %>%
      add_count(firstFeelName)

    adList[[index]] <- dataRT %>%
      group_by(firstFeelName) %>%
      filter(n>7) %>%
      summarize(adTest = ad.test(responseTimes..ms.)[[2]])

    kruskalList[[index]] <- kruskal.test(responseTimes..ms. ~ firstFeelName,data=dataRT)
    summary

    dataForsythe <- dataRT %>%
      filter(n>5)

    forsytheList[[index]] <- bf.test(responseTimes..ms. ~ firstFeelName, data=dataForsythe, alpha = 0.05, na.rm = TRUE, verbose = TRUE)

    PT = dunnTest(responseTimes..ms. ~ firstFeelName,data=dataRT,
                  method="sidak")    # Can adjust p-values;
    PT = PT$res

    ptList[[index]] = PT

    cldSubjs[[index]] <-cldList(comparison = PT$Comparison,
                                p.value    = PT$P.adj,
                                threshold  = 0.05)

  }
  index = index + 1
}