Analysis_R_code.R

# Loading a required package for Bayesian analysis
require(BayesMed)

## Loading required package: BayesMed

## Loading required package: R2jags

## Loading required package: rjags

## Loading required package: coda

## Linked to JAGS 4.1.0

## Loaded modules: basemod,bugs

## 
## Attaching package: 'R2jags'

## The following object is masked from 'package:coda':
## 
##     traceplot

## Loading required package: QRM

## Warning: package 'QRM' was built under R version 3.2.4

## Loading required package: gsl

## Loading required package: Matrix

## Warning: package 'Matrix' was built under R version 3.2.5

## Loading required package: mvtnorm

## Loading required package: numDeriv

## Warning: package 'numDeriv' was built under R version 3.2.5

## Loading required package: timeSeries

## Loading required package: timeDate

## 
## Attaching package: 'QRM'

## The following object is masked from 'package:base':
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##     lbeta

## Loading required package: polspline

## Loading required package: MCMCpack

## Warning: package 'MCMCpack' was built under R version 3.2.5

## Loading required package: MASS

## ##
## ## Markov Chain Monte Carlo Package (MCMCpack)

## ## Copyright (C) 2003-2016 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park

## ##
## ## Support provided by the U.S. National Science Foundation

## ## (Grants SES-0350646 and SES-0350613)
## ##

# Setting the working directory and loading the .csv data
setwd("/Users/ivanropovik/OneDrive/Statistics/Lakens Coursera/Replication Documentation/Processing and Analysis/Importable Data/")
data <- read.csv(file = "Data.csv", header = TRUE, sep = ";")

# Correlation test
with(na.omit(data), cor.test(year, rating, alternative = "greater"))

## 
##  Pearson's product-moment correlation
## 
## data:  year and rating
## t = 1.3637, df = 30, p-value = 0.09141
## alternative hypothesis: true correlation is greater than 0
## 95 percent confidence interval:
##  -0.05890589  1.00000000
## sample estimates:
##       cor 
## 0.2415952

# Calculates BF01
1/with(na.omit(data),
       jzs_cor(year, rating, alternative = "greater", n.iter = 3e5, n.burnin = 1.5e3))$BayesFactor

## module glm loaded

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 32
##    Unobserved stochastic nodes: 4
##    Total graph size: 130
## 
## Initializing model

## [1] 1.668745