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meta_d analysis
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iair-embon committed Oct 26, 2022
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1 change: 1 addition & 0 deletions Analysis/Meta_d_analysis/M_diff.txt
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1 change: 1 addition & 0 deletions Analysis/Meta_d_analysis/M_ratio.txt
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70 changes: 49 additions & 21 deletions Meta_d_preprocessing.R → ...is/Meta_d_analysis/Meta_d_preprocessing.R
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@@ -1,15 +1,22 @@
##### Este script crea el df para preprocesar los datos y correr el meta d' desde matlab


library(dplyr)

# load dataframe
root <- rprojroot::is_rstudio_project
basename(getwd())
load("./Data/All_exp_exclusion_criteria/df_total.Rda")

# stimuli presented
## filter by gender
df_total <- df_total %>%
filter(genero == "Femenino" | genero == "Masculino")

# stimuli presented column
df_total$left_right_stimuli <- ifelse(df_total$dots_num_left > df_total$dots_num_right,
"left","right")

# response key
# response key column
left_right_response_key <- rep(NaN, nrow(df_total))
for (i in 1:nrow(df_total)) {

Expand All @@ -32,10 +39,13 @@ for (i in 1:nrow(df_total)) {

df_total$left_right_response_key <-left_right_response_key

lala <- df_total %>%
# summarise in the column needed
df_summarise <- df_total %>%
group_by(sujetos, left_right_stimuli, left_right_response_key, confidence_key) %>%
summarise(confidence_n = n())
summarise(confidence_n = n()) %>%
ungroup()

# create a df where all preprocessed data will be saved
df_total_preprocessed <- data.frame(sujetos = numeric(),
left_right_stimuli = character(),
left_right_response_key = character(),
Expand All @@ -44,20 +54,21 @@ df_total_preprocessed <- data.frame(sujetos = numeric(),

conf_ratings <- c(1,2,3,4)

nombre_provisorio <- function(lala_per_subj, stimuli, response){
# a function that will add the 0 responses to the missing confidence rating
add_zero_rating <- function(df_summarise_per_subj, stimuli, response){

direction_df <- lala_per_subj %>%
direction_df <- df_summarise_per_subj %>%
filter(left_right_stimuli == stimuli & left_right_response_key == response)

if(nrow(direction_df) < 4){
index <- direction_df$confidence_key != conf_ratings
diff_ratings <- setdiff(conf_ratings, direction_df$confidence_key)


df <- data.frame(sujetos = rep(lala$sujetos[i], length(conf_ratings[index])),
left_right_stimuli = rep(stimuli,length(conf_ratings[index])),
left_right_response_key = rep(response,length(conf_ratings[index])),
confidence_key = conf_ratings[index], ### ERROR, CUANDO HAY MAS DE UN INDICE QUE NO ESTA
confidence_n = rep(0,length(conf_ratings[index])))
df <- data.frame(sujetos = rep(unique(df_summarise$sujetos)[i], length(diff_ratings)),
left_right_stimuli = rep(stimuli,length(diff_ratings)),
left_right_response_key = rep(response,length(diff_ratings)),
confidence_key = diff_ratings,
confidence_n = rep(0,length(diff_ratings)))
df_total_provisorio <- rbind(direction_df, df)

}else{
Expand All @@ -66,45 +77,62 @@ nombre_provisorio <- function(lala_per_subj, stimuli, response){
return(df_total_provisorio)
}

for (i in 1:nrow(lala)) {
# applie the function per subject, stimuli and response
for (i in 1:length(unique(df_summarise$sujetos))) {

lala_per_subj <- lala %>%
filter(sujetos == sujetos[i])
df_summarise_per_subj <- df_summarise %>%
filter(sujetos == unique(df_summarise$sujetos)[i])

if(nrow(lala_per_subj)<16){
if(nrow(df_summarise_per_subj)<16){

# left left
df_total_provisorio <- nombre_provisorio(lala_per_subj = lala_per_subj,
df_total_provisorio <- add_zero_rating(df_summarise_per_subj = df_summarise_per_subj,
stimuli = "left",
response = "left")

# order by confidence_key in descending order
df_total_provisorio <- df_total_provisorio[order(-df_total_provisorio$confidence_key), ]
df_total_preprocessed <- rbind(df_total_preprocessed, df_total_provisorio)

# left right
df_total_provisorio <- nombre_provisorio(lala_per_subj = lala_per_subj,
df_total_provisorio <- add_zero_rating(df_summarise_per_subj = df_summarise_per_subj,
stimuli = "left",
response = "right")

# order by confidence_key in ascending order
df_total_provisorio <- df_total_provisorio[order(df_total_provisorio$confidence_key), ]
df_total_preprocessed <- rbind(df_total_preprocessed, df_total_provisorio)

# right left
df_total_provisorio <- nombre_provisorio(lala_per_subj = lala_per_subj,
df_total_provisorio <- add_zero_rating(df_summarise_per_subj = df_summarise_per_subj,
stimuli = "right",
response = "left")

# order by confidence_key in descending order
df_total_provisorio <- df_total_provisorio[order(-df_total_provisorio$confidence_key), ]
df_total_preprocessed <- rbind(df_total_preprocessed, df_total_provisorio)

# right right
df_total_provisorio <- nombre_provisorio(lala_per_subj = lala_per_subj,
df_total_provisorio <- add_zero_rating(df_summarise_per_subj = df_summarise_per_subj,
stimuli = "right",
response = "right")

# order by confidence_key in ascending order
df_total_provisorio <- df_total_provisorio[order(df_total_provisorio$confidence_key), ]
df_total_preprocessed <- rbind(df_total_preprocessed, df_total_provisorio)

} else {
df_total_preprocessed <- rbind(df_total_preprocessed, lala_per_subj)
df_total_preprocessed <- rbind(df_total_preprocessed, df_summarise_per_subj)
}

}

## In order to deal with 0 responses for some confidence ratings, we follow the
## recomendation of Maniscalco and Lau function, and add a small adjustment factor
## adj_f = 1/(length(nR_S1). In our case it would be 0.125

df_total_preprocessed$confidence_n <- df_total_preprocessed$confidence_n + 0.125

## create a txt file that contain the column df_total_preprocessed$confidence_n

write.table(df_total_preprocessed$confidence_n ,"confidence_n.txt",sep="\t",row.names=FALSE)

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