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simul_these/RProject/Scripts/aggregation.R

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##############################################################################
#----------------------------------------------------------------------------#
################################## LIBRARIES #################################
#----------------------------------------------------------------------------#
##############################################################################
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(funprog)
library(dplyr)
library(readxl)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
##############################################################################
#----------------------------------------------------------------------------#
############################# ANALYSIS FUNCTIONS #############################
#----------------------------------------------------------------------------#
##############################################################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
truebeta <- eff.size
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- lapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
}
}
returndat <- returndat[,sort_by(listitems, lastChar)]
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=truebeta,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',eff.size=0,difsize=NA,nbdif=0) {
truebeta <- eff.size
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- lapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
}
}
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=truebeta,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
replicate_pcm_analysis<- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',eff.size=0,difsize=NA,nbdif=0) {
j <- max(df$item1)
if(j==1) {
return(replicate_pcm_analysis_m2(df=df,treatment=treatment,irtmodel=irtmodel,method=method,sequence=sequence,eff.size=eff.size,difsize=difsize,nbdif=nbdif))
} else {
return(replicate_pcm_analysis_m4(df=df,treatment=treatment,irtmodel=irtmodel,method=method,sequence=sequence,eff.size=eff.size,difsize=difsize,nbdif=nbdif))
}
}
##############################################################################
#----------------------------------------------------------------------------#
################################# AGGREGATION ################################
#----------------------------------------------------------------------------#
##############################################################################
#### Create data.frame
results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
results <- sort(results)
results2 <- sort(results2)
results <- c(results,results2)
#### Compiler function
compile_simulation <- function(scenario) {
name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50" & name<=4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N50/scenario_',scenario,'.csv'))
}
if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50" & name>4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N50/scenario_',scenario,'.csv'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name<=4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'.csv'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'_nodif.csv'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name<=4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'.csv'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'_nodif.csv'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name<=4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'.csv'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'_nodif.csv'))
}
if (unique(s$J)==4) {
if (unique(s$M)==2) {
a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
)
}
} else {
if (unique(s$M)==2) {
a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4),
m.item5=mean(s$item5),m.item6=mean(s$item6),m.item7=mean(s$item7))
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
)
}
}
N <- ifelse(substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50","50",substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)))
zz <- ifelse(N=="50",substr(scenario,start=0,stop=nchar(scenario)-3),substr(scenario,start=0,stop=nchar(scenario)-4))
b <- data.frame(scenario=zz,
scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
N=N,
J=unique(s$J),
M=unique(s$M),
eff.size=unique(s$eff.size),
nb.dif=unique(s$nb.dif),
dif.size=unique(s$dif.size)
)
z <- data.frame(m.beta=mean(s$beta),
se.empirical.beta=sd(s$beta),
se.analytical.beta=mean(s$se.beta),
m.low.ci.beta=mean(s$low.ci.beta),
m.high.ci.beta=mean(s$high.ci.beta),
true.value.in.ci.p=mean(s$true.value.in.ci),
h0.rejected.p=mean(s$h0.rejected),
beta.same.sign.truebeta.p=mean(s$beta.same.sign.truebeta,na.rm=T),
beta.same.sign.truebeta.signif.p=mean(s[s$h0.rejected==1,]$beta.same.sign.truebeta,na.rm=T))
d <- cbind(b,a,z)
d$prop.
return(d)
}
#### Compiled results
res.dat <- compile_simulation('1A_100')
for (x in results[seq(2,length(results))]) {
y <- compile_simulation(x)
res.dat <- bind_rows(res.dat,y)
}
res.dat[res.dat$scenario.type=='A','dif.size'] <- -res.dat[res.dat$scenario.type=='A','dif.size']
res.dat[is.na(res.dat$dif.size),'dif.size'] <- 0
res.dat[193:417,'nb.dif'] <- 2
res.dat[417:528,'nb.dif'] <- 3
res.dat[res.dat$scenario.type=="B",]$eff.size <- 0.2
res.dat[res.dat$scenario.type=="C" & res.dat$dif.size==0,]$eff.size <- 0.4
res.dat[res.dat$scenario.type=="C" & res.dat$dif.size!=0,]$eff.size <- 0.2
res.dat[res.dat$scenario.type=="D" & res.dat$dif.size==0,]$eff.size <- -0.2
res.dat[res.dat$scenario.type=="D" & res.dat$dif.size!=0,]$eff.size <- 0.4
res.dat[res.dat$scenario.type=="E" & res.dat$dif.size==0,]$eff.size <- -0.4
res.dat[res.dat$scenario.type=="E" & res.dat$dif.size!=0,]$eff.size <- 0.4
res.dat[res.dat$scenario.type=="F",]$eff.size <- -0.2
res.dat[res.dat$scenario.type=="G",]$eff.size <- -0.4
View(res.dat)
res.dat.simple <- res.dat[,c(1:8,13,16:18)]
res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
res.dat.simple
##############################################################################
#----------------------------------------------------------------------------#
########################### AGGREGATION DIF MATRICES #########################
#----------------------------------------------------------------------------#
##############################################################################
#### Create data.frame
results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
results <- sort(results)
results2 <- sort(results2)
results <- c(results,results2)[81:528]
#### Compiler function
compile_simulation2 <- function(scenario) {
name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50" & name>4) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/DIF/N50/',scenario,'.xls'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>4) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/DIF/N100/',scenario,'.xls'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>4) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/DIF/N200/',scenario,'.xls'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>4) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/DIF/N300/',scenario,'.xls'))
}
J <- max(which(sapply(1:7,function(x) paste0('item',x) %in% colnames(s) | paste0('item',x,'_1') %in% colnames(s))))
M <- 1+sum(sapply(1:3,function(x) paste0('item1_',x) %in% colnames(s) ))
if (M==1) {M <- 2}
nb.dif <- max(which(sapply(1:3,function(x) paste0('dif',x) %in% colnames(s) | paste0('dif',x,'_1') %in% colnames(s))))
if (J==4) {
if (M==2) {
a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
)
}
} else {
if (M==2) {
a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4),
m.item5=mean(s$item5),m.item6=mean(s$item6),m.item7=mean(s$item7))
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
)
}
}
N <- ifelse(substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50","50",substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)))
zz <- ifelse(N=="50",substr(scenario,start=0,stop=nchar(scenario)-3),substr(scenario,start=0,stop=nchar(scenario)-4))
eff.size <- unique(res.dat[res.dat$scenario==zz & res.dat$N==N,'eff.size'])
dif.size <- unique(res.dat[res.dat$scenario==zz & res.dat$N==N,'dif.size'])
b <- data.frame(scenario=zz,
scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
N=N,
J=J,
M=M,
eff.size=eff.size,
nb.dif=nb.dif,
dif.size=dif.size
)
true.value.in.ci <- eff.size <= s$beta+1.96*s$se_beta & eff.size >= s$beta-1.96*s$se_beta
beta.same.sign.truebeta.p <- ifelse(rep(eff.size,nrow(s))==0,NA,(rep(eff.size,nrow(s))/s$beta)>0)
num.reject <- which((s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0)
z <- data.frame(m.beta=mean(s$beta),
se.empirical.beta=sd(s$beta),
se.analytical.beta=mean(s$se_beta),
m.low.ci.beta=mean(s$beta-1.96*s$se_beta),
m.high.ci.beta=mean(s$beta+1.96*s$se_beta),
true.value.in.ci.p=mean(true.value.in.ci),
h0.rejected.p=mean( (s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0 ),
beta.same.sign.truebeta.p=mean(beta.same.sign.truebeta.p),
beta.same.sign.truebeta.signif.p=mean(beta.same.sign.truebeta.p[num.reject])
)
d <- cbind(b,a,z)
d$prop.
return(d)
}
#### Compiled results
res.dat.dif <- compile_simulation2('5A_100')
for (x in results[seq(2,length(results))]) {
y <- compile_simulation2(x)
res.dat.dif <- bind_rows(res.dat.dif,y)
}
res.dat$bias <- res.dat$eff.size-res.dat$m.beta
res.dat.dif$bias <- res.dat.dif$eff.size-res.dat.dif$m.beta
##############################################################################
#----------------------------------------------------------------------------#
####################### AGGREGATION DIF MATRICES ROSALI ######################
#----------------------------------------------------------------------------#
##############################################################################
#### Create data.frame
results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
results <- sort(results)
results2 <- sort(results2)
results <- c(results,results2)
#### Compiler function
compile_simulation2_rosali <- function(scenario) {
name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50" & name>0) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF/N50/',scenario,'_original.xls'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>0) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF/N100/',scenario,'_original.xls'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>0) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF/N200/',scenario,'_original.xls'))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>0) {
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF/N300/',scenario,'_original.xls'))
}
J <- max(which(sapply(1:7,function(x) paste0('item',x) %in% colnames(s) | paste0('item',x,'_1') %in% colnames(s))))
M <- 1+sum(sapply(1:3,function(x) paste0('item1_',x) %in% colnames(s) ))
if (M==1) {M <- 2}
corrected rosali bug
8 months ago
nb.dif.true <- ifelse(name<=4,0,ifelse(name<=8,1,ifelse(name<=16,2,3)))
if (name %in% c(3,4,13:20)) {
m.nb.dif.detect <- mean(ifelse(is.na(s$dif_1_1),0,
ifelse(is.na(s$dif_2_1),1,
ifelse(is.na(s$dif_3_1),2,
ifelse(is.na(s$dif_4_1),3,
ifelse(is.na(s$dif_5_1),4,
ifelse(is.na(s$dif_6_1),5,
ifelse(is.na(s$dif_7_1),6,7))))))))
}
if (!(name %in% c(3,4,13:20))) {
m.nb.dif.detect <- mean(ifelse(is.na(s$dif_1_1),0,
ifelse(is.na(s$dif_2_1),1,
ifelse(is.na(s$dif_3_1),2,
ifelse(is.na(s$dif_4_1),3,4)))))
}
File restructure + reproduction guide
8 months ago
if (J==4) {
if (M==2) {
corrected rosali bug
8 months ago
a <- data.frame(m.item1=mean(s$item1_1),m.item2=mean(s$item2_1),m.item3=mean(s$item3_1),m.item4=mean(s$item4_1))
File restructure + reproduction guide
8 months ago
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
)
}
} else {
if (M==2) {
corrected rosali bug
8 months ago
a <- data.frame(m.item1=mean(s$item1_1),m.item2=mean(s$item2_1),m.item3=mean(s$item3_1),m.item4=mean(s$item4_1),
m.item5=mean(s$item5_1),m.item6=mean(s$item6_1),m.item7=mean(s$item7_1))
File restructure + reproduction guide
8 months ago
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
)
}
}
N <- ifelse(substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50","50",substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)))
zz <- ifelse(N=="50",substr(scenario,start=0,stop=nchar(scenario)-3),substr(scenario,start=0,stop=nchar(scenario)-4))
eff.size <- unique(res.dat[res.dat$scenario==zz & res.dat$N==N,'eff.size'])
dif.size <- unique(res.dat[res.dat$scenario==zz & res.dat$N==N,'dif.size'])
b <- data.frame(scenario=zz,
scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
N=N,
J=J,
M=M,
eff.size=eff.size,
corrected rosali bug
8 months ago
nb.dif=nb.dif.true,
m.nb.dif.detect=m.nb.dif.detect,
File restructure + reproduction guide
8 months ago
dif.size=dif.size
)
true.value.in.ci <- eff.size <= s$beta+1.96*s$se_beta & eff.size >= s$beta-1.96*s$se_beta
beta.same.sign.truebeta.p <- ifelse(rep(eff.size,nrow(s))==0,NA,(rep(eff.size,nrow(s))/s$beta)>0)
num.reject <- which((s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0)
corrected rosali bug
8 months ago
dif.d <- mean(sapply(1:1000,function(x) any(!is.na(s[x,paste0("dif_",1:unique(b$J),"_1")]))))
if (nb.dif.true==0) {
prop.perfect <- NA
flexible.detect <- NA
moreflexible.detect <- NA
}
if (nb.dif.true==1 & unique(b$J)==4 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1,s[x,"dif_detect_unif_1"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==4 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==7 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==3 & unique(b$J)==7 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,"dif_detect_unif_3"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]) & s[x,"real_dif_3"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==1 & unique(b$J)==4 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1, s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
Corrected errors in residuals analysis
8 months ago
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:4)]%in%c(s[x,c("real_dif_1")]))/1)
percent.detect <- mean(percent.detect)
corrected rosali bug
8 months ago
}
if (nb.dif.true==2 & unique(b$J)==4 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
Corrected errors in residuals analysis
8 months ago
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:4)]%in%c(s[x,c("real_dif_1","real_dif_2")]))/2)
percent.detect <- mean(percent.detect)
corrected rosali bug
8 months ago
}
if (nb.dif.true==2 & unique(b$J)==7 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
Corrected errors in residuals analysis
8 months ago
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:7)]%in%c(s[x,c("real_dif_1","real_dif_2")]))/2)
percent.detect <- mean(percent.detect)
corrected rosali bug
8 months ago
}
if (nb.dif.true==3 & unique(b$J)==7 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]) & s[x,"real_dif_3"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
Corrected errors in residuals analysis
8 months ago
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:7)]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]))/3)
percent.detect <- mean(percent.detect)
corrected rosali bug
8 months ago
}
File restructure + reproduction guide
8 months ago
z <- data.frame(m.beta=mean(s$beta),
se.empirical.beta=sd(s$beta),
se.analytical.beta=mean(s$se_beta),
m.low.ci.beta=mean(s$beta-1.96*s$se_beta),
m.high.ci.beta=mean(s$beta+1.96*s$se_beta),
true.value.in.ci.p=mean(true.value.in.ci),
h0.rejected.p=mean( (s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0 ),
beta.same.sign.truebeta.p=mean(beta.same.sign.truebeta.p),
corrected rosali bug
8 months ago
beta.same.sign.truebeta.signif.p=mean(beta.same.sign.truebeta.p[num.reject]),
dif.detected=dif.d,
prop.perfect=prop.perfect,
flexible.detect=flexible.detect,
Corrected errors in residuals analysis
8 months ago
moreflexible.detect=moreflexible.detect,
percent.detect=ifelse(name%%2==0,NA,percent.detect)
File restructure + reproduction guide
8 months ago
)
d <- cbind(b,a,z)
d$prop.
return(d)
}
#### Compiled results
res.dat.dif.rosali <- compile_simulation2_rosali('1A_100')
for (x in results[seq(2,length(results))]) {
y <- compile_simulation2_rosali(x)
res.dat.dif.rosali <- bind_rows(res.dat.dif.rosali,y)
}
res.dat.dif.rosali$bias <- res.dat.dif.rosali$eff.size-res.dat.dif.rosali$m.beta
##############################################################################
#----------------------------------------------------------------------------#
####################### AGGREGATION DIF MATRICES RESALI ######################
#----------------------------------------------------------------------------#
##############################################################################
#### Create data.frame
results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
results <- sort(results)
results2 <- sort(results2)
results <- c(results,results2)
#### Compiler function
compile_simulation2_resali <- function(scenario) {
name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50" & name>0) {
Corrected bug in residuals analysis
8 months ago
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Results/N50/',scenario,'_original.xls'))
File restructure + reproduction guide
8 months ago
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>0) {
Corrected bug in residuals analysis
8 months ago
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Results/N100/',scenario,'_original.xls'))
File restructure + reproduction guide
8 months ago
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>0) {
Corrected bug in residuals analysis
8 months ago
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Results/N200/',scenario,'_original.xls'))
File restructure + reproduction guide
8 months ago
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>0) {
Corrected bug in residuals analysis
8 months ago
s <- read_excel(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Results/N300/',scenario,'_original.xls'))
File restructure + reproduction guide
8 months ago
}
J <- max(which(sapply(1:7,function(x) paste0('item',x) %in% colnames(s) | paste0('item',x,'_1') %in% colnames(s))))
M <- 1+sum(sapply(1:3,function(x) paste0('item1_',x) %in% colnames(s) ))
if (M==1) {M <- 2}
Corrected bug in residuals analysis
8 months ago
nb.dif.true <- ifelse(name<=4,0,ifelse(name<=8,1,ifelse(name<=16,2,3)))
if (name %in% c(3,4,13:20)) {
m.nb.dif.detect <- mean(ifelse(is.na(s$dif_1_1),0,
ifelse(is.na(s$dif_2_1),1,
ifelse(is.na(s$dif_3_1),2,
ifelse(is.na(s$dif_4_1),3,
ifelse(is.na(s$dif_5_1),4,
ifelse(is.na(s$dif_6_1),5,
ifelse(is.na(s$dif_7_1),6,7))))))))
}
if (!(name %in% c(3,4,13:20))) {
m.nb.dif.detect <- mean(ifelse(is.na(s$dif_1_1),0,
ifelse(is.na(s$dif_2_1),1,
ifelse(is.na(s$dif_3_1),2,
ifelse(is.na(s$dif_4_1),3,4)))))
}
File restructure + reproduction guide
8 months ago
if (J==4) {
if (M==2) {
Corrected bug in residuals analysis
8 months ago
a <- data.frame(m.item1=mean(s$item1_1),m.item2=mean(s$item2_1),m.item3=mean(s$item3_1),m.item4=mean(s$item4_1))
File restructure + reproduction guide
8 months ago
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
)
}
} else {
if (M==2) {
Corrected bug in residuals analysis
8 months ago
a <- data.frame(m.item1=mean(s$item1_1),m.item2=mean(s$item2_1),m.item3=mean(s$item3_1),m.item4=mean(s$item4_1),
m.item5=mean(s$item5_1),m.item6=mean(s$item6_1),m.item7=mean(s$item7_1))
File restructure + reproduction guide
8 months ago
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
)
}
}
N <- ifelse(substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50","50",substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)))
zz <- ifelse(N=="50",substr(scenario,start=0,stop=nchar(scenario)-3),substr(scenario,start=0,stop=nchar(scenario)-4))
eff.size <- unique(res.dat[res.dat$scenario==zz & res.dat$N==N,'eff.size'])
dif.size <- unique(res.dat[res.dat$scenario==zz & res.dat$N==N,'dif.size'])
b <- data.frame(scenario=zz,
scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
N=N,
J=J,
M=M,
eff.size=eff.size,
Corrected bug in residuals analysis
8 months ago
nb.dif=nb.dif.true,
m.nb.dif.detect=m.nb.dif.detect,
File restructure + reproduction guide
8 months ago
dif.size=dif.size
)
true.value.in.ci <- eff.size <= s$beta+1.96*s$se_beta & eff.size >= s$beta-1.96*s$se_beta
beta.same.sign.truebeta.p <- ifelse(rep(eff.size,nrow(s))==0,NA,(rep(eff.size,nrow(s))/s$beta)>0)
num.reject <- which((s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0)
Corrected bug in residuals analysis
8 months ago
dif.d <- mean(sapply(1:1000,function(x) any(!is.na(s[x,paste0("dif_",1:unique(b$J),"_1")]))))
if (nb.dif.true==0) {
prop.perfect <- NA
flexible.detect <- NA
moreflexible.detect <- NA
}
if (nb.dif.true==1 & unique(b$J)==4 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1,s[x,"dif_detect_unif_1"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==4 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==7 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==3 & unique(b$J)==7 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,"dif_detect_unif_3"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]) & s[x,"real_dif_3"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==1 & unique(b$J)==4 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1, s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:4)]%in%c(s[x,c("real_dif_1")]))/1)
percent.detect <- mean(percent.detect)
}
if (nb.dif.true==2 & unique(b$J)==4 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:4)]%in%c(s[x,c("real_dif_1","real_dif_2")]))/2)
percent.detect <- mean(percent.detect)
}
if (nb.dif.true==2 & unique(b$J)==7 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:7)]%in%c(s[x,c("real_dif_1","real_dif_2")]))/2)
percent.detect <- mean(percent.detect)
}
if (nb.dif.true==3 & unique(b$J)==7 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]) & s[x,"real_dif_3"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
percent.detect <- sapply(1:1000,function(x) sum(s[x,paste0("dif_detect_",1:7)]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]))/3)
percent.detect <- mean(percent.detect)
}
File restructure + reproduction guide
8 months ago
z <- data.frame(m.beta=mean(s$beta),
se.empirical.beta=sd(s$beta),
se.analytical.beta=mean(s$se_beta),
m.low.ci.beta=mean(s$beta-1.96*s$se_beta),
m.high.ci.beta=mean(s$beta+1.96*s$se_beta),
true.value.in.ci.p=mean(true.value.in.ci),
h0.rejected.p=mean( (s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0 ),
beta.same.sign.truebeta.p=mean(beta.same.sign.truebeta.p),
Corrected bug in residuals analysis
8 months ago
beta.same.sign.truebeta.signif.p=mean(beta.same.sign.truebeta.p[num.reject]),
dif.detected=dif.d,
prop.perfect=prop.perfect,
flexible.detect=flexible.detect,
moreflexible.detect=moreflexible.detect,
percent.detect=ifelse(name%%2==0,NA,percent.detect)
File restructure + reproduction guide
8 months ago
)
d <- cbind(b,a,z)
d$prop.
return(d)
}
#### Compiled results
res.dat.dif.resali <- compile_simulation2_resali('1A_100')
for (x in results[seq(2,length(results))]) {
y <- compile_simulation2_resali(x)
res.dat.dif.resali <- bind_rows(res.dat.dif.resali,y)
}
res.dat.dif.resali$bias <- res.dat.dif.resali$eff.size-res.dat.dif.resali$m.beta
##############################################################################
#----------------------------------------------------------------------------#
################################## RASCHPOWER ################################
#----------------------------------------------------------------------------#
##############################################################################
###### Puissance théorique
res.dat$theoretical.power <- 0
### Scénarios N=100
## Scénarios J=4 / M=2
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==100,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==100,]$theoretical.power <- 0.1543
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==100,]$theoretical.power <- 0.1543
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==100,]$theoretical.power <- 0.4627
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==100,]$theoretical.power <- 0.4627
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==100,]$theoretical.power <- 0.1543
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==100,]$theoretical.power <- 0.4627
res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==100,]$theoretical.power <- 0.4627
res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==100,]$theoretical.power <- 0.1543
res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==100,]$theoretical.power <- 0.4627
## Scénarios J=4 / M=4
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==100,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==100,]$theoretical.power <- 0.2177
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==100,]$theoretical.power <- 0.2177
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==100,]$theoretical.power <- 0.6586
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==100,]$theoretical.power <- 0.6586
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==100,]$theoretical.power <- 0.2177
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==100,]$theoretical.power <- 0.6586
res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==100,]$theoretical.power <- 0.6586
res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==100,]$theoretical.power <- 0.2177
res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==100,]$theoretical.power <- 0.6586
## Scénarios J=7 / M=2
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==100,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==100,]$theoretical.power <- 0.1870
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==100,]$theoretical.power <- 0.1870
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==100,]$theoretical.power <- 0.5666
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==100,]$theoretical.power <- 0.5666
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==100,]$theoretical.power <- 0.1870
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==100,]$theoretical.power <- 0.5666
res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==100,]$theoretical.power <- 0.5666
res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==100,]$theoretical.power <- 0.1870
res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==100,]$theoretical.power <- 0.5666
## Scénarios J=7 / M=4
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'A') & res.dat$N==100,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==100,]$theoretical.power <- 0.2450
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==100,]$theoretical.power <- 0.2450
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==100,]$theoretical.power <- 0.7136
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==100,]$theoretical.power <- 0.7136
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==100,]$theoretical.power <- 0.2450
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==100,]$theoretical.power <- 0.7136
res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==100,]$theoretical.power <- 0.7136
res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==100,]$theoretical.power <- 0.2450
res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==100,]$theoretical.power <- 0.7136
### Scénarios N=200
## Scénarios J=4 / M=2
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==200,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==200,]$theoretical.power <- 0.2618
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==200,]$theoretical.power <- 0.2618
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==200,]$theoretical.power <- 0.7507
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==200,]$theoretical.power <- 0.7507
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==200,]$theoretical.power <- 0.2618
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==200,]$theoretical.power <- 0.7507
res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==200,]$theoretical.power <- 0.7507
res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==200,]$theoretical.power <- 0.2618
res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==200,]$theoretical.power <- 0.7507
## Scénarios J=4 / M=4
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==200,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==200,]$theoretical.power <- 0.3875
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==200,]$theoretical.power <- 0.3875
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==200,]$theoretical.power <- 0.9161
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==200,]$theoretical.power <- 0.9161
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==200,]$theoretical.power <- 0.3875
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==200,]$theoretical.power <- 0.9161
res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==200,]$theoretical.power <- 0.9161
res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==200,]$theoretical.power <- 0.3875
res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==200,]$theoretical.power <- 0.9161
## Scénarios J=7 / M=2
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==200,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==200,]$theoretical.power <- 0.3258
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==200,]$theoretical.power <- 0.3258
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==200,]$theoretical.power <- 0.8538
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==200,]$theoretical.power <- 0.8538
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==200,]$theoretical.power <- 0.3258
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==200,]$theoretical.power <- 0.8538
res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==200,]$theoretical.power <- 0.8538
res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==200,]$theoretical.power <- 0.3258
res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==200,]$theoretical.power <- 0.8538
## Scénarios J=7 / M=4
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'A') & res.dat$N==200,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==200,]$theoretical.power <- 0.4321
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==200,]$theoretical.power <- 0.4321
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==200,]$theoretical.power <- 0.9471
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==200,]$theoretical.power <- 0.9471
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==200,]$theoretical.power <- 0.4321
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==200,]$theoretical.power <- 0.9471
res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==200,]$theoretical.power <- 0.9471
res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==200,]$theoretical.power <- 0.4321
res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==200,]$theoretical.power <- 0.9471
### Scénarios N=300
## Scénarios J=4 / M=2
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==300,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==300,]$theoretical.power <- 0.3660
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==300,]$theoretical.power <- 0.3660
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==300,]$theoretical.power <- 0.8981
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==300,]$theoretical.power <- 0.8981
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==300,]$theoretical.power <- 0.3660
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==300,]$theoretical.power <- 0.8981
res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==300,]$theoretical.power <- 0.8981
res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==300,]$theoretical.power <- 0.3660
res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==300,]$theoretical.power <- 0.8981
## Scénarios J=4 / M=4
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==300,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==300,]$theoretical.power <- 0.5373
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==300,]$theoretical.power <- 0.5373
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==300,]$theoretical.power <- 0.9834
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==300,]$theoretical.power <- 0.9834
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==300,]$theoretical.power <- 0.5373
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==300,]$theoretical.power <- 0.9834
res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==300,]$theoretical.power <- 0.9834
res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==300,]$theoretical.power <- 0.5373
res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==300,]$theoretical.power <- 0.9834
## Scénarios J=7 / M=2
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==300,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==300,]$theoretical.power <- 0.4550
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==300,]$theoretical.power <- 0.4550
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==300,]$theoretical.power <- 0.9584
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==300,]$theoretical.power <- 0.9584
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==300,]$theoretical.power <- 0.4550
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==300,]$theoretical.power <- 0.9584
res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==300,]$theoretical.power <- 0.9584
res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==300,]$theoretical.power <- 0.4550
res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==300,]$theoretical.power <- 0.9584
## Scénarios J=7 / M=4
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'A') & res.dat$N==300,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==300,]$theoretical.power <- 0.5907
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==300,]$theoretical.power <- 0.5907
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==300,]$theoretical.power <- 0.9919
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==300,]$theoretical.power <- 0.9919
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==300,]$theoretical.power <- 0.5907
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==300,]$theoretical.power <- 0.9919
res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==300,]$theoretical.power <- 0.9919
res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==300,]$theoretical.power <- 0.5907
res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==300,]$theoretical.power <- 0.9919
### Scénarios N=50
## Scénarios J=4 / M=2
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==50,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==50,]$theoretical.power <- 0.1013
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==50,]$theoretical.power <- 0.1013
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==50,]$theoretical.power <- 0.2615
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==50,]$theoretical.power <- 0.2615
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==50,]$theoretical.power <- 0.1013
res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==50,]$theoretical.power <- 0.2615
res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==50,]$theoretical.power <- 0.2615
res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==50,]$theoretical.power <- 0.1013
res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==50,]$theoretical.power <- 0.2615
## Scénarios J=4 / M=4
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==50,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==50,]$theoretical.power <- 0.1339
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==50,]$theoretical.power <- 0.1339
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==50,]$theoretical.power <- 0.3863
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==50,]$theoretical.power <- 0.3863
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==50,]$theoretical.power <- 0.1339
res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==50,]$theoretical.power <- 0.3863
res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==50,]$theoretical.power <- 0.3863
res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==50,]$theoretical.power <- 0.1339
res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==50,]$theoretical.power <- 0.3863
## Scénarios J=7 / M=2
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==50,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==50,]$theoretical.power <- 0.1171
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==50,]$theoretical.power <- 0.1171
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==50,]$theoretical.power <- 0.3236
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==50,]$theoretical.power <- 0.3236
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==50,]$theoretical.power <- 0.1171
res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==50,]$theoretical.power <- 0.3236
res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==50,]$theoretical.power <- 0.3236
res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==50,]$theoretical.power <- 0.1171
res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==50,]$theoretical.power <- 0.3236
## Scénarios J=7 / M=4
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'A') & res.dat$N==50,]$theoretical.power <- 0.05
res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==50,]$theoretical.power <- 0.1448
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==50,]$theoretical.power <- 0.1448
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==50,]$theoretical.power <- 0.4328
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==50,]$theoretical.power <- 0.4328
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==50,]$theoretical.power <- 0.1448
res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==50,]$theoretical.power <- 0.4328
res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==50,]$theoretical.power <- 0.4328
res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==50,]$theoretical.power <- 0.1448
res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==50,]$theoretical.power <- 0.4328
### DIF scenarios
res.dat.dif$theoretical.power <- res.dat[81:nrow(res.dat),]$theoretical.power