rm(list=ls())

#dir <- "C:/Users/averhag2/Dropbox/UVA/JosineEJ/ReplicationPrior/Code/Correlation"
setwd(dir)


# Load and if necessary install the required libraries
library("hypergeo")
source("ReplicationFunctionsCorrelation.R")

# Origineel: r = .934, n = 9
# Replicatie Forstmann: r = .763, n = 12
# replication in Forstmann paper:
r.orig <- .934
n.orig <- 9 
r.rep  <- .763
n.rep  <- 11 


REPBF10 <- CorrelationReplicationBF(r.orig, n.orig, r.rep, n.rep)
REPBF10
#23.84

# To create a plot and compute the Savage Dickey Bayes factor: 
SDRBF <- repposteriorplot(n.orig, r.orig, n.rep, r.rep)
SDRBF


# Now for the Boekel non-replication:
# Replicatie Boekel: r = .03, n = 31
r.orig <- .934
n.orig <- 9 
r.rep  <- .03
n.rep  <- 31 

REPBF10 <-  CorrelationReplicationBF(r.orig, n.orig, r.rep, n.rep, M=1000000)
REPBF10
# REPBF10 = 0.004
# Evidence FOR the null:
1/REPBF10 
#206

SDRBF <- repposteriorplot(n.orig, r.orig, n.rep, r.rep)
SDRBF

###################
# If you want to compute a Bayes factor for each study separately to 
# see whether there is an effect versus the null hypothesis that there is no effect, 
#with an "objective" uniform prior for the effect respresenting that it could be anywhere
# between -1 and 1, you can use the following routine: 

r <- .934
n <- 9 

BF <- corBF.beta(r, n)
BF
SDBF <- PosteriorPlot(n, r)
SDBF

r  <- .03
n  <- 31 

BF <- corBF.beta(r, n)
SDBF <- PosteriorPlot(n, r)
BF
SDBF