#=========================================================#
# FLANKER RELIABILITY ESTIMATE
#=========================================================#

#-----------
# LIBS
#-----------
library(plyr)
source("C://Dropbox//PHD_MultisensoryNeglect//BasicFunctions.R")

#-----------
# DATA
#-----------
dp <- "C://Dropbox//EFT_Project//Internship_EFT_HH//CO 12//Data 1.0//FLANKER//"

FLANK <- ReadTextFile(dp, sep = "\t")

#-----------
# OUTLIERS
#-----------
participants <- ddply(FLANK, c("participant"), function(df)c(Acc = mean(df$Acc[df$Congruency != "incongruent"])))
FLANK <- FLANK[FLANK$participant %in% participants$participant[participants$Acc > (mean(participants$Acc) - 2.5 * sd(participants$Acc))], ]
length(unique(FLANK$participant))
remove(participants)

#----------------------------------
# CALCULATE DV FOR SEPERATE HALVES
#----------------------------------
FLANK$TrialNumber <- rep(seq(1,120,1), 130)

# Summarize:
RT_Summary <- ddply(FLANK, c("participant"), 
                    function(df)c(RTIncongr1 = mean(df$RT[df$Acc == 1 & df$Congruency == "congruent" & df$TrialNumber <= 60]),
                                  RTIncongr2 = mean(df$RT[df$Acc == 1 & df$Congruency == "congruent" & df$TrialNumber > 60]),
                                  RTCongr1 = mean(df$RT[df$Acc == 1 & df$Congruency == "incongruent" & df$TrialNumber <= 60]),
                                  RTCongr2 = mean(df$RT[df$Acc == 1 & df$Congruency == "incongruent" & df$TrialNumber > 60]),
                                  RTBase1 = mean(df$RT[df$Acc == 1 & df$Congruency == "neutral" & df$TrialNumber <= 60]),
                                  RTBase2 = mean(df$RT[df$Acc == 1 & df$Congruency == "neutral" & df$TrialNumber > 60])))

RT_Summary$FlankEffect1 <- (RT_Summary$RTIncongr1/RT_Summary$RTBase1) - (RT_Summary$RTCongr1/RT_Summary$RTBase1)
RT_Summary$FlankEffect2 <- (RT_Summary$RTIncongr2/RT_Summary$RTBase2) - (RT_Summary$RTCongr2/RT_Summary$RTBase2)

#----------------------------------
# RELIABILITY
#----------------------------------
plot(RT_Summary$FlankEffect1, RT_Summary$FlankEffect2)
cor.test(RT_Summary$FlankEffect1, RT_Summary$FlankEffect2)

spb <- (2 * cor(RT_Summary$FlankEffect1, RT_Summary$FlankEffect2)) / (1 + (2 - 1) * cor(RT_Summary$FlankEffect1, RT_Summary$FlankEffect2))