Lakens

if(!require(meta)){install.packages('meta')}
library(meta)

nSims <- 1000000 #number of simulated experiments
numberstudies<-4 # nSim/numberofstudies should be whole number
p <-numeric(nSims) #set up empty container for all simulated p-values
metapran <-numeric(nSims/numberstudies) #set up empty container for all simulated p-values for random effects MA
metapfix <-numeric(nSims/numberstudies) #set up empty container for all simulated p-values for fixed effects MA
heterog.p<-numeric(nSims/numberstudies) #set up empty container for test for heterogeneity
d <-numeric(nSims) #set up empty container for all simulated d's

Lakens

df1<-1
df2<-100
critF<-qf(.95, df1=df1, df2=df2) #determine critical F-value
critT<-qt(.975, df2) #determine critical F-value
critF #critical F-value
critT^2 #Critical t squared is the same as critical F-value
critT #critical t-value

x=seq(0,10,length=10000)
maxy<-ifelse(max(df(x,df1,df2))==Inf,1, max(df(x,df1,df2))) #set maximum y axis

Lakens

setwd("C:/Users/Daniel/Downloads/Gilbert, King, Pettigrew, Wilson 2016 replication files/variability analysis replication files/data")
load("many labs replication cis.RData")
## Drop the top rows which are statistics from pooling together all the replications
res <- lapply(res, function(x) x[-c(1:2),])
res[[12]] <- res[[12]][-1,]
names(res[[16]])[3:5] <- names(res[[15]])[3:5]


## For each replicated study, get the number of the other replicated
## studies that were outside the CI

Lakens

if(!require(ggplot2)){install.packages('ggplot2')}
library(ggplot2)

n=20 #set sample size
nSims<-100000 #set number of simulations

x<-rnorm(n = n, mean = 100, sd = 15) #create sample from normal distribution

#95%CI
CIU<-mean(x)+qt(0.975, df = n-1)*sd(x)*sqrt(1/n)

Lakens

if(!require(ggplot2)){install.packages('ggplot2')}
library(ggplot2)
if(!require(MBESS)){install.packages('MBESS')}
library(MBESS)
#Set color palette
cbbPalette<-c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")


N<-20
#Set mean and SD

Lakens

#Bayesian Power Analysis

if(!require(BayesFactor)){install.packages('BayesFactor')}
library(BayesFactor) 

D<-0.0 #Set the true effect size
n<-50 #Set sample size of your study (number in each group)
nSim<-100000 #Set number of simulations (it takes a while, be patient)
rscaleBF<-sqrt(2)/2 #Set effect size of alternative hypothesis (default = sqrt(2)/2, or 0.707)
threshold<-3 #Set threshold for 'support' - e.g., 3, 10, or 30

Lakens

library(reshape2)
library(mvtnorm) 
library(ez) 
#Install multtest# try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("multtest")
library(mutoss) #load multiple testing library for Holm function

#2x2x2 within design
N<-50 #sample size per group

Lakens

require(ggplot2)

#Save downloaded Scopus data in your working directory
scopusdata<-read.csv("scopusPS2010_2015.csv")

plot1<-ggplot(scopusdata, aes(x=Cited.by))  + 
  geom_histogram(colour="#535353", fill="#84D5F0", binwidth=2) +
  xlab("Number of Citations") + ylab("Number of Articles")  + 
  ggtitle("Citation Data for Psychological Science 2011-2015") +
  coord_cartesian(xlim = c(-5, 250))

Lakens

#Additional Analyses of Nuijten et al: https://mbnuijten.files.wordpress.com/2013/01/nuijtenetal_2015_reportingerrorspsychology1.pdf
#First run the original script to read in the data: https://osf.io/e9qbp/


#Select only errors.
subdata<-subset(data, data$Error == TRUE) 
subdata$pdif<-subdata$Reported.P.Value-subdata$Computed #Compute difference in p-values.
#Plot differences in reported and computed p-values for all errors
ggplot(as.data.frame(subdata$pdif), aes(subdata$pdif))  + 
  geom_histogram(colour="black", fill="grey", binwidth = 0.01) + ggtitle("All Errors") + xlab("Reported P-value minus Computed P-value") + ylab("Frequency")  + theme_bw(base_size=20) 

Lakens

# # # # # # # # # # #
#Initial settings----
# # # # # # # # # # #
if(!require(ggplot2)){install.packages('ggplot2')}
library(ggplot2)
if(!require(MBESS)){install.packages('MBESS')}
library(MBESS)
if(!require(pwr)){install.packages('pwr')}
library(pwr)
if(!require(meta)){install.packages('meta')}