rm(list=ls(all=TRUE))  # clear all variables
graphics.off()  # clear all graphics
# Weapons priming of aggressive words
# Greg Francis
# PSY 626
# 22 September 2020

# Bayesian dependent t-test


# load data file
WPdata<-read.csv(file="WeaponPrime.csv",header=TRUE,stringsAsFactors=TRUE)

# Dummy variable to indicate condition
WPdata$ConditionIndex <-0* WPdata$Time
WPdata$ConditionIndex[WPdata$Prime =="Weapon"] =1
WPdata$ConditionIndex[WPdata$Prime =="Neutral"] =2


#----------------------
# Run traditional dependent t-test
traditional <- t.test(Time ~ Prime, data=WPdata, paired=TRUE)

print(traditional)


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

# load the rethinking library
library(rethinking)

# Null model: one mean for both prime conditions
# Different baseline (intercept) for each participant
WPmodel0 <- map(
			alist( Time ~ dnorm(mu, sigma), 
			mu <- a[Subject],
			a[Subject] ~ dnorm(500, 500),
			sigma ~ dunif(0, 1000)
			), data= WPdata , control=list(maxit=10000) ) 
cat("Finished WPmodel0\n")

# Alternative model 1: different mean for each condition	
WPmodel1 <- map(
			alist( Time ~ dnorm(mu, sigma), 
			mu <- a[Subject] +b[ConditionIndex],
			a[Subject] ~ dnorm(500, 500),
			b[ConditionIndex] ~ dnorm(0, 100),
			sigma ~ dunif(0, 1000)
			), data= WPdata , control=list(maxit=10000) ) 

cat("Finished WPmodel1\n")


# Compare models

print(compare(WPmodel0, WPmodel1) )