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nSims <- 100000 #number of simulated experiments (the more, the more accurate the numbers you get, but the longer it takes. I used 1000000 simulations for my blog) | |
N<-32 #number of participants | |
lowp<-0.04 | |
highp<-0.05 | |
#set up some variables | |
p<-numeric(nSims) | |
p2<-numeric(nSims) | |
obs_pwr<-numeric(nSims) | |
t<-numeric(nSims) |
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#The necessary packages (please ensure that these have been #installed before running the following code). | |
#Many of the analyses are performed with both packages to double-check the calculations | |
library(meta) | |
library(metafor) | |
#The data for the full sample. All outputs that end in ".all" #are based on the full sample. | |
r=0.5 #set correlation between dv's to 0.5, using formula's for variance from dependent test for one-sample t-test | |
#effect sizes | |
d.all<-c(-0.054458115, 0.093112835, 0.206774549, 0.478004184, 0.141213676, 0.272767619, 0.251, 0.195142683, 0.258913272, 0.204022676, 0.223, 0.145336391, 0.092630988, 0.192, 0.418607214, 0.290090298, 0.108461538, 0.316418861, 0.417283465, -0.043799784, 0.147113469, 0.139283883, 0.060796002, -0.113389342, 0, 0.114947393, 0.169535252, -0.048878416, -0.029138576, -0.004628788, 0.086060345, 0.231428571, 0.161220346, -0.050911688, 0.044376016, 0.185846777, 0.251, 0.52, 0.355321158, 0.02665009, 0.052177581, -0.011716899, -0.023772371, 0.252476027, 0.210898339, 0.067797354, |
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nSims <- 1000000 #number of simulated experiments | |
p <-numeric(nSims) #set up empty container for all simulated p-values | |
d <-numeric(nSims) #set up empty container for all simulated d's | |
n=20 | |
for(i in 1:nSims){ #for each simulated experiment | |
x<-rnorm(n = n, mean = 0.68, sd = 1) #produce n simulated participants | |
#with mean=100 and SD=20 | |
y<-rnorm(n = n, mean = 0, sd = 1) #produce n simulated participants |
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#Simulate bias in eta-squared, omega-squared, and epsilon-squared. | |
#R script by Kensuke Okada from: Okada, K. (2013). Is omega squared less biased? A comparison of three major effect size indices in one-way ANOVA. Behaviormetrika, 40(2), 129-147. | |
muvec <- c(0.00,0.00,0.8,0.8) | |
meanmu <- mean(muvec) | |
sigb <- sum((muvec-meanmu)^2)/4 | |
eta2p <- sigb/(sigb+1) | |
k <- length(muvec) | |
nsim <- 1000000 #Bias should decrease as sample size increases. Set simulations to 1000000 for best results | |
njs <- c(10,20,30,40,50,60,70,80,90,100) |
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#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) |
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set.seed(2) | |
options(scipen=20) #disable scientific notation for numbers | |
nSim<-10 #numbber of simulated studies | |
library(pwr) | |
library(MBESS) | |
library(gsDesign) # The group sequential design package | |
library(BayesFactor) |
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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 |
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#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 |
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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 |
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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 |