rm(list=ls(all=TRUE))  # clear all variables
graphics.off()  # clear all graphics
# Proactive Interference
# Greg Francis
# PSY 626
# 01 February 2024

# Bayesian dependent t-test


# load data file
PIdata<-read.csv(file="ProactiveInterference.csv",sep="\t", header=TRUE,stringsAsFactors=TRUE)

# Dummy variable to indicate condition
PIdata$ConditionIndex <-0* PIdata$NumCorrect
PIdata$ConditionIndex[PIdata$Trial =="1"] =1
PIdata$ConditionIndex[PIdata$Trial =="5"] =2


# Full data set
#----------------------
# Run traditional dependent t-test
traditional <- t.test(NumCorrect ~ Trial, data=PIdata, paired=TRUE)

print(traditional)


#------------------------
# Bayesian models

# load the rethinking library
library(rethinking)

# Null model: one mean for both conditions
# Different baseline (intercept) for each participant
PImodel0 <- map(
			alist( NumCorrect ~ dnorm(mu, sigma), 
			mu <- a[Participant],
			a[Participant] ~ dnorm(5, 5),
			sigma ~ dunif(0, 7)
			), data= PIdata , control=list(maxit=10000) ) 
cat("Finished PImodel0\n")

# Alternative model 1: different mean for each condition	
PImodel1 <- map(
			alist( NumCorrect ~ dnorm(mu, sigma), 
			mu <- a[Participant] +b[ConditionIndex],
			a[Participant] ~ dnorm(5, 5),
			b[ConditionIndex] ~ dnorm(5, 5),
			sigma ~ dunif(0, 7)
			), data= PIdata , control=list(maxit=10000) ) 

cat("Finished PImodel1\n")


# Compare models

print(compare(PImodel0, PImodel1) )