initial commit

LICENSE

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 MIT License
 
-Copyright (c) 2021 Milou Sep
+Copyright (c) 2020 Milou Sep
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

R/FGT_descriptives.R

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+# Descriptive statistics of the data from the Fear Generalization Task (FGT) in the SAM study
+# Written by Milou Sep.
+
+# load packages
+library(dplyr)
+library(Rmisc) # For Mean and CI calculation of raw data
+library(haven) # to load SPSS file in r
+
+# Shock Intensities -------------------------------------------------------
+# Load FGT data
+FGT_batch <- read.csv2("data/SAM_FGT.csv", na.strings = c("NaN","5555","8888","9999"))
+# load shock amperage values
+FGT_SWU <- read.csv2("data/SAM_FGT_Amperage.csv")
+
+# Select only participants that completed the FGT (n=117)
+FGT_participants <- FGT_batch$subjName
+# Changes characters to patterns for grepl(). Info on matching multiple patterns https://stackoverflow.com/questions/6947587/matching-multiple-patterns
+FGT_pattern<-paste(FGT_participants, collapse ="|") 
+FGT_SWU <- FGT_SWU[grepl(FGT_pattern, as.character(FGT_SWU$Participantnummer)),]
+
+# Calculate mean, standard deviation, range of shock intensities
+summary(FGT_SWU$`Stroomsterkte..mA.`) # mean & range
+sd(FGT_SWU$`Stroomsterkte..mA.`) # standard deviation
+# Calculate means CI / Condition
+SWU.Mean.Condition.CI <- group.CI(`Stroomsterkte..mA.`~Conditie, FGT_SWU, ci = 0.95)
+oneway.test(`Stroomsterkte..mA.`~Conditie, FGT_SWU) # NS
+
+
+# Count missing FPS data per type missing (for paper) ----------------------------
+# Note Missing codes (provided with Matlab code):
+# 5555 % Digital registration error, message on screen & MissingValue 5555.
+# 8888 % Excessive baseline activity. (More than 2 SD deviation)
+# 9999 % Latency onset not valid (Note, this only affects onset scores, so not present in magnitude variables)
+
+FGT_batch_count.missing <- read.csv2("data/SAM_FGT.csv")
+Umag_for_missing <- subset.data.frame(FGT_batch_count.missing, select = c(grep("Umag", names(FGT_batch_count.missing))))
+# Number of missing values in original data
+n.missing.Technical<-sum(Umag_for_missing == '5555') #= 798
+n.missing.Noise<-sum(Umag_for_missing == '8888') #= 256
+# Total Number of observations
+n.observations<-nrow(Umag_for_missing)*ncol(Umag_for_missing)
+# percent.missing
+(n.missing.Technical/n.observations)*100  # = 7.105
+(n.missing.Noise/n.observations)*100  # = 2.279 %
+# 0-responses
+n.nulltesponses<-sum(Umag_for_missing == "0") #= 240
+(n.nulltesponses/n.observations)*100 # 2.137 %
+
+
+# FGT Contingency ---------------------------------------------------------
+# load data
+SAM_questionnaires <- read_sav("data/SAM_questionnaires.sav") # Questionnaires
+SAM_versions <- read.csv("data/SAM_Codes_Task_Protocol_Versions.csv") # Information on task versions
+# select required variables
+SAM_questionnaires %>% select(SubjectNumber,Condition,shock_indicator)->SAM_questionnaires2 # NOTE, 59 missing values in 'shock indicator', because this question was added to the questionnaire later. 
+SAM_versions %>% select(SubjectNumber,FGTversion)->SAM_versions2
+# merge information
+full_join(SAM_versions2, SAM_questionnaires2, by="SubjectNumber")->FGT_contingency
+# Create variable to indicate if threat context was identified correctly.
+FGT_contingency %>% mutate(Correct_FGT_contingency = case_when(
+  shock_indicator == FGTversion ~ 1, # correct response
+  shock_indicator != FGTversion ~ 0 # incorrect response
+))-> FGT_contingency
+# Differences between experimental groups?
+group.CI(Correct_FGT_contingency~Condition, FGT_contingency, ci = 0.95)
+oneway.test(Correct_FGT_contingency~Condition, FGT_contingency) # NS
+# save participants with correct responses
+FGT_contingency%>% filter(Correct_FGT_contingency == 1) %>% select(SubjectNumber) %>% saveRDS(.,"processed_data/participants.for.contingency.sensitivity.analyses.rda")

R/FGT_mids_transformations.R

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+# Functions for the transformation and manipulation of mids objects in the analysis of the data from the Fear Generalization Task (FGT) in the SAM study
+# written by Milou Sep
+
+# Info on how to perform manipulations/calculations on midsobject: 
+# https://stackoverflow.com/questions/26667162/perform-operation-on-each-imputed-dataset-in-rs-mice
+
+library(dplyr)
+
+# Function to calculate Means on subset mids object data (bv early trials only..)
+means_EML_2way <- function(data_subset){
+  data_subset.means<-aggregate(data_subset$FPS,
+                               by=list(data_subset$.imp, data_subset$pp, data_subset$Condition, data_subset$trialtype), 
+                               FUN=mean)
+  names(data_subset.means)<-c(".imp", "pp", "Condition", "trialtype", "FPS")
+  # Some checks
+  print(unique(data_subset.means$Condition))
+  print(unique(data_subset.means$pp))
+  print(unique(data_subset.means$.imp))
+
+  data_subset.means.mids <- as.mids(data_subset.means) # Make mids.
+  return(data_subset.means.mids)
+}
+
+# Function to log-transform FPS data if necessary (checked in Assumption Checks)
+log.transform.mids <- function(data_set){
+  data_set.long<-complete(data_set, action = "long", include = T) # Change mids to long format
+  data_set.long$FPS <- log(data_set.long$FPS + 1) # Add 1 to deal with 0-responses
+  log.mids.dataset <- as.mids(data_set.long)
+  return(log.mids.dataset)
+}
+
+
+# Select one imputed dataset for testing (Note function returns dataframe!)
+only_one_imputation <- function(data_mids, imp){
+  # 1) Mids to dataframe
+  data_mids.long <- complete(data_mids, action = "long", include = T)
+  tibble(data_mids.long)
+  # 2) Perform manipulations: 
+  data_mids.long %>% filter(.imp == imp) ->x
+  return(x)
+}
+
+
+# For sensitivity analyses ------------------------------------------------
+
+# Sensitivity analyses to check the effect of potential influential participants:
+remove.influential.points <- function(data_mids, influentialpoints){
+  # Note numbers should be assigned to influential points e.g. c(1,2,3)
+  # Mids to dataframe
+  data_mids.long <- complete(data_mids, action = "long", include = T)
+  # Perform manipulations: search & exclude rows from influential points (or participants)
+  indices <- which(data_mids.long$pp %in% influentialpoints)
+  data_mids.long.noinfl <- data_mids.long[-c(indices),]
+  # Back to mids.
+  data_mids.noinfl <- as.mids(data_mids.long.noinfl)
+  return(data_mids.noinfl)
+}
+
+# Function to change factor Trial and/or Trialtype to continuous variables 
+Within.continuous.mids <- function(data_set){ 
+  # This was recommended for analyses of fear gradients by https://www.sciencedirect.com/science/article/pii/S0005791618300612 & http://www.frontiersin.org/Quantitative_Psychology_and_Measurement/10.3389/fpsyg.2015.00652/abstract
+  data_set.long<-complete(data_set, action = "long", include = T) # Change mids to long format
+  # manipulations:
+  data_set.long$trialtype <- as.numeric(data_set.long$trialtype)
+  data_set.long$trial <- as.numeric(data_set.long$trial)
+  str(data_set.long)
+  # transform back to mids
+  mids.dataset <- as.mids(data_set.long)
+  return(mids.dataset)
+}
+
+# Sensitivity analyses on participants that detected the threat stimulus correctly (according to self-report)
+Sensitivity_contingency <- function(midsobject){
+  long_df<-complete(midsobject, action = "long", include = T)
+  pp_to_include <- which(long_df$pp %in% pp_Sensitivity$X1)
+  long_df_included <- long_df[c(pp_to_include),]
+  mids_included <- as.mids(long_df_included)
+  return(mids_included)
+}