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simul_these/RProject/Scripts/Analysis/article.R

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A few calculations for article statistics
7 months ago
Cleaned up code
7 months ago
source(paste0(getwd(),"/functions/resali.R"))
A few calculations for article statistics
7 months ago
Update to article code
2 months ago
# A la place faire un boxplot par sample size avec une boite par méthode
boxplot(as.numeric(res.dat.article$typeIerror)~as.numeric(res.dat.article$N),
ylim=c(0,1),xlab="N",ylab="Type-I error",col="#ff7777",pch=3)
abline(h=0.05,col="red",lty=2,lwd=2)
A few calculations for article statistics
7 months ago
##########################
# DETECTION
##########################
# Which performed better
summary(res.dat.dif.rosali$prop.perfect-res.dat.dif.resali$prop.perfect)
# ROSALI better more than 10% ?
res.dat.dif.rosali$better <- res.dat.dif.rosali$prop.perfect-res.dat.dif.resali$prop.perfect>0.1
table(res.dat.dif.rosali[res.dat.dif.rosali$better & res.dat.dif.rosali$nb.dif!=0,"scenario.type"])
# ROSALI worse more than 10% ?
res.dat.dif.rosali$worse <- res.dat.dif.rosali$prop.perfect-res.dat.dif.resali$prop.perfect< -0.1
res.dat.dif.rosali[res.dat.dif.rosali$worse & res.dat.dif.rosali$nb.dif!=0,]
# ROSALI perf per subsc
summary(res.dat.dif.rosali[ res.dat.dif.rosali$N==300 & res.dat.dif.rosali$nb.dif>0 & res.dat.dif.rosali$scenario.type%in%c("C","E"),]$prop.perfect)
summary(res.dat.dif.rosali[ res.dat.dif.rosali$N==300 & res.dat.dif.rosali$nb.dif>0 & res.dat.dif.rosali$scenario.type%in%c("B","D","F","G"),]$prop.perfect)
# AHRM perf per subsc
summary(res.dat.dif.resali[ res.dat.dif.resali$N==300 & res.dat.dif.resali$nb.dif>0 & res.dat.dif.resali$scenario.type%in%c("C","E"),]$prop.perfect)
summary(res.dat.dif.resali[ res.dat.dif.resali$N==300 & res.dat.dif.resali$nb.dif>0 & res.dat.dif.resali$scenario.type%in%c("B","D","F","G"),]$prop.perfect)
# False DIF detect
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0,"dif.detected"])
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0,"dif.detected"])
# Causal inference NO DIF
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0,"bias"])
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0,"bias"])
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0 & res.dat.dif.rosali$eff.size==0,"h0.rejected.p"])
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0 & res.dat.dif.resali$eff.size==0,"h0.rejected.p"])
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0,"true.value.in.ci.p"])
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0,"true.value.in.ci.p"])
##########################
# STATS DIF DETECTION
##########################
# sample size
summary(tab3$moreflexible.detect.50.rosali)
summary(tab3$moreflexible.detect.50.residuals)
tab3[which.max(tab3$prop.perfect.50.residuals),]
summary(tab3$moreflexible.detect.100.rosali)
summary(tab3$moreflexible.detect.100.residuals)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.100.residuals)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.100.rosali)
summary(tab3$moreflexible.detect.200.rosali)
summary(tab3$moreflexible.detect.200.residuals)
summary(tab3$moreflexible.detect.300.rosali)
summary(tab3$moreflexible.detect.300.residuals)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.300.rosali)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.300.residuals)
summary(tab3[tab3$nb.dif==3 & tab3$J==7,]$flexible.detect.300.rosali)
summary(tab3[tab3$nb.dif==3 & tab3$J==7,]$flexible.detect.300.residuals)
summary(tab3[tab3$nb.dif==2 & tab3$J==4,]$prop.perfect.300.rosali)
summary(tab3[tab3$nb.dif==2 & tab3$J==4,]$prop.perfect.300.residuals)
summary(tab3[tab3$nb.dif==2 & tab3$J==7,]$prop.perfect.300.rosali)
summary(tab3[tab3$nb.dif==2 & tab3$J==7,]$prop.perfect.300.residuals)
nrow(tab3[tab3$nb.dif==2 & tab3$prop.perfect.300.residuals>0.5,])
nrow(tab3[tab3$nb.dif==2 & tab3$prop.perfect.300.rosali>0.5,])
summary(tab3[tab3$M==2,]$prop.perfect.300.rosali)
summary(tab3[tab3$M==2,]$prop.perfect.300.residuals)
summary(tab3[tab3$M==4,]$prop.perfect.300.rosali)
summary(tab3[tab3$M==4,]$prop.perfect.300.residuals)
summary(tab3[tab3$M==2,]$prop.perfect.200.rosali)
summary(tab3[tab3$M==2,]$prop.perfect.200.residuals)
summary(tab3[tab3$M==4,]$prop.perfect.200.rosali)
summary(tab3[tab3$M==4,]$prop.perfect.200.residuals)
Final code before cleaning
7 months ago
summary(tab3[tab3$dif.size==0.3,]$prop.perfect.300.rosali)
summary(tab3[tab3$dif.size==0.3,]$prop.perfect.300.residuals)
summary(tab3[tab3$dif.size==0.5,]$prop.perfect.300.rosali)
summary(tab3[tab3$dif.size==0.5,]$prop.perfect.300.residuals)
summary(tab3[tab3$dif.dir==sign(tab3$eff.size),]$prop.perfect.300.rosali)
summary(tab3[tab3$dif.dir!=sign(tab3$eff.size),]$prop.perfect.300.rosali)
summary(tab3[tab3$dif.dir==sign(tab3$eff.size),]$prop.perfect.300.residuals)
summary(tab3[tab3$dif.dir==-sign(tab3$eff.size),]$prop.perfect.300.residuals)
summary(tab3$moreflexible.detect.300.rosali-tab3$flexible.detect.300.rosali)
summary(tab3$moreflexible.detect.300.residuals-tab3$flexible.detect.300.residuals)
res.dat[res.dat$N=="300" & res.dat$scenario.type=="A" & abs(res.dat$dif.size)==0.5 &
res.dat$nb.dif==2 & res.dat$J==4,]
summary(tab3[tab3$eff.size==0,]$prop.perfect.300.residuals)
summary(tab3[tab3$eff.size==0,]$prop.perfect.300.rosali)
length(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif>0 &
res.dat.dif.rosali$prop.perfect>0.3,]$prop.perfect)/nrow(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif>0,])
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif>0 &
res.dat.dif.rosali$prop.perfect>0.3,]$prop.perfect)
length(res.dat.dif.resali[res.dat.dif.resali$nb.dif>0 &
res.dat.dif.resali$prop.perfect>0.3,]$prop.perfect)/nrow(res.dat.dif.resali[res.dat.dif.resali$nb.dif>0,])
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif>0 &
res.dat.dif.resali$prop.perfect>0.3,]$prop.perfect)
Update to article code
2 months ago
##########################
# ICC / CCC BASE
##########################
plot.tam.2 <- function(x, items=1:x$nitems, type="expected",
low=-3, high=3, ngroups=6, groups_by_item=FALSE,
wle=NULL, export=TRUE, export.type="png",
export.args=list(), observed=TRUE, overlay=FALSE,
ask=FALSE, package="lattice",
fix.devices=TRUE, nnodes=100, ...)
{
require_namespace_msg("grDevices")
if ( package=="lattice"){
require_namespace_msg("lattice")
}
# device.Option <- getOption("device")
time1 <- NULL
pall <- c('#200c23', '#62403d', '#a87b5e', '#e9bf98'
)
if ( fix.devices ){
old.opt.dev <- getOption("device")
old.opt.err <- c( getOption("show.error.messages"))
old.par.ask <- graphics::par("ask")
# remember new pars' values
old.par.xpd <- graphics::par("xpd")
old.par.mar <- graphics::par("mar")
on.exit( options("device"=old.opt.dev))
on.exit( options("show.error.messages"=old.opt.err), add=TRUE)
on.exit( graphics::par("ask"=old.par.ask), add=TRUE)
# restore new pars' values
on.exit( graphics::par("xpd"=old.par.xpd), add=TRUE)
on.exit( graphics::par("mar"=old.par.mar), add=TRUE)
}
tamobj <- x
ndim <- tamobj$ndim
tammodel <- "mml"
if(is.null(ndim)) {
ndim <- 1
tammodel <- "jml"
}
if (ndim > 1 ) {
if ( type=="expected"){
stop ("Expected scores curves are only available for uni-dimensional models")
}
}
nitems <- tamobj$nitems
if (ndim==1 ){
theta <- matrix(seq(low, high, length=nnodes), nrow=nnodes, ncol=ndim)
} else {
nodes <- seq(low, high, length=nnodes)
theta <- as.matrix( expand.grid( as.data.frame( matrix( rep(nodes, ndim), ncol=ndim ) ) ) )
nnodes <- nrow(theta)
B <- tamobj$B
}
iIndex <- 1:nitems
A <- tamobj$A
B <- tamobj$B
if (tammodel=="mml") {
xsi <- tamobj$xsi$xsi
} else {
xsi <- tamobj$xsi
}
maxK <- tamobj$maxK
resp <- tamobj$resp
resp.ind <- tamobj$resp.ind
resp[resp.ind==0] <- NA
AXsi <- matrix(0,nrow=nitems,ncol=maxK )
res <- tam_mml_calc_prob(iIndex=iIndex, A=A, AXsi=AXsi, B=B, xsi=xsi, theta=theta,
nnodes=nnodes, maxK=maxK, recalc=TRUE )
rprobs <- res[["rprobs"]]
AXsi <- res[["AXsi"]]
cat <- 1:maxK - 1
#@@@ define initial empty objects
expScore <- obScore <- wle_intervals <- NULL
theta2 <- NULL
#**** type='expected'
if ( type=="expected" ){
expScore <- sapply(1:nitems, function(i) colSums(cat*rprobs[i,,], na.rm=TRUE))
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
wle_intervals <- res$wle_intervals
#-- compute observed scores
obScore <- apply(d2,2, function(x){
stats::aggregate(x, list(groupnumber), mean, na.rm=TRUE)
} )
}
#----------------------------------------------------
# adds observed score for type="items"
if (type=="items") {
require_namespace_msg("plyr")
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
obScore <- lapply(d2, function(item) {
comp_case=stats::complete.cases(item)
item=item[comp_case]
uniq_cats=sort(unique(item))
plyr::ldply(split(item, groupnumber[comp_case]), .id="group",
function (group) {
ngroup=length(group)
cat_freq=list()
for (catt in uniq_cats) {
cat_freq[[paste0("cat_", catt)]]=sum(group==catt)/ngroup
}
data.frame(cat_freq)
})
})
}
#*************************************************
# begin plot function
probs_plot <- as.list(1:nitems)
names(probs_plot) <- items
for (i in (1:nitems)[items]) {
#***********************************************************
#** expected item response curves
if ( type=="expected"){
if (i==1 || !overlay) {
ylim2 <- c(0,max( tamobj$resp[,i], na.rm=TRUE ) )
graphics::plot(theta, expScore[,i],,col=12, type="l", lwd=3, las=1, ylab="Score", xlab="Ability",
main=paste("Expected Scores Curve - Item ", colnames(tamobj$resp)[i] ) ,
ylim=ylim2, ... )
} else {
graphics::lines(theta, expScore[,i],type="l", col=i, lwd=3, pch=1)
}
if (observed){
theta2_i <- theta2
obScore_i <- obScore[[i]]$x
if (groups_by_item){
ind_i <- ! is.na(resp[,i])
resp_i <- resp[ind_i, i, drop=FALSE]
wle_i <- wle[ ind_i ]
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle_i, ngroups=ngroups, resp=resp_i )
theta2_i <- res$theta2
groupnumber_i <- res$groupnumber
aggr <- stats::aggregate(resp_i, list(groupnumber_i), mean, na.rm=TRUE )
obScore_i <- aggr[,2]
}
graphics::lines(theta2_i, obScore_i, type="o", lwd=2, pch=1)
}
}
#***********************************************************
if ( ndim==1 ){ theta0 <- theta }
if ( type=="items"){
rprobs.ii <- rprobs[i,,]
rprobs.ii <- rprobs.ii[ rowMeans( is.na(rprobs.ii) ) < 1, ]
K <- nrow(rprobs.ii)
dat2 <- NULL
#************
if ( ndim > 1 ){
B.ii <- B[i,,]
ind.ii <- which( colSums( B.ii ) > 0 )[1]
rprobs0.ii <- rprobs.ii
rprobs0.ii <- stats::aggregate( t(rprobs0.ii), list( theta[,ind.ii] ), mean )
theta0 <- rprobs0.ii[,1,drop=FALSE]
rprobs.ii <- t( rprobs0.ii[,-1] )
}
probs_plot[[i]] <- rprobs.ii
#**************
for (kk in 1:K){
dat2a <- data.frame( "Theta"=theta0[,1], "cat"=kk, "P"=rprobs.ii[kk,] )
dat2 <- rbind(dat2, dat2a)
}
auto.key <- NULL
simple.key <- paste0("Cat", 1:K - 1)
auto.key <- simple.key
dat2$time <- dat2$cat
dat2$time1 <- paste0("Cat", dat2$time )
simple.key <- FALSE
Kpercol <- K
# package graphics
if ( package=="graphics" ){
kk <- 1
dfr <- dat2
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::plot( dfr1a$Theta, dfr1a$P, ylim=c(-.1,1.1),
xlab=expression(theta),
col=pall[kk], type="l", xpd=TRUE,axes=F, ...
)
axis(1)
axis(2)
grid(nx = NA,
ny = NULL,
lty = 3, col = "lightgray", lwd = 1)
graphics::lines( dfr1a$Theta, dfr1a$P,
col=pall[kk], type="l", ...
)
for (kk in seq(2,K) ){
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::lines( dfr1a$Theta, dfr1a$P, col=pall[kk] )
# graphics::points( dfr1a$Theta, dfr1a$P, pch=kk, col=kk+1 )
}
}
#***************************************
}
#***************
graphics::par(ask=ask)
} # end item ii
#*************************************************
}
# get all the function names of the given package "mypack"
r <- unclass(lsf.str(envir = asNamespace("TAM"), all = T))
# create functions in the Global Env. with the same name
for(name in r) eval(parse(text=paste0(name, '<-TAM:::', name)))
#### CCC
zaz <- read.csv("/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_2A_100.csv")
zaz <- zaz[zaz$replication==1,]
zaza <- tam.mml(resp = zaz[,paste0("item",1:4)])
CurlyBraces <- function(x0, x1, y0, y1, pos = 1, direction = 1, depth = 1) {
a=c(1,2,3,48,50) # set flexion point for spline
b=c(0,.2,.28,.7,.8) # set depth for spline flexion point
curve = spline(a, b, n = 50, method = "natural")$y * depth
curve = c(curve,rev(curve))
if (pos == 1){
a_sequence = seq(x0,x1,length=100)
b_sequence = seq(y0,y1,length=100)
}
if (pos == 2){
b_sequence = seq(x0,x1,length=100)
a_sequence = seq(y0,y1,length=100)
}
# direction
if(direction==1)
a_sequence = a_sequence+curve
if(direction==2)
a_sequence = a_sequence-curve
# pos
if(pos==1)
lines(a_sequence,b_sequence, lwd=1.5, xpd=NA) # vertical
if(pos==2)
lines(b_sequence,a_sequence, lwd=1.5, xpd=NA) # horizontal
}
# CCC de base
pdf(file = '/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/PDF/ccc_base.pdf')
par(xpd=F,mar=c(6.1,5.1,7.6,2.1))
plot.tam.2(zaza,type = "items",export=F,ylab="Probability of response",main=NULL,package = "graphics",items = 3)
segments(x0=zaza$xsi[7,]$xsi,x1=zaza$xsi[7,]$xsi,y0=0,y1=1.1,lty=3)
segments(x0=zaza$xsi[8,]$xsi,x1=zaza$xsi[8,]$xsi,y0=0,y1=1.1,lty=3)
segments(x0=zaza$xsi[9,]$xsi,x1=zaza$xsi[9,]$xsi,y0=0,y1=1.1,lty=3)
text(x=-2.5,y=0.85,"0",col="#200c23")
text(x=-0.65,y=0.55,"1",col="#62403d")
text(x=0.95,y=0.55,"2",col="#a87b5e")
text(x=2.5,y=0.85,"3",col="#e9bf98")
par(xpd=T,mar=c(5.1,4.1,4.1,2.1))
segments(x0=-3,x1=zaza$xsi[7,]$xsi,y0=1.1,y1=1.1,col="#200c23",lwd=2)
segments(x0=zaza$xsi[7,]$xsi,x1=zaza$xsi[8,]$xsi,y0=1.1,y1=1.1,col="#62403d",lwd=2)
segments(x0=zaza$xsi[8,]$xsi,x1=zaza$xsi[9,]$xsi,y0=1.1,y1=1.1,col="#a87b5e",lwd=2)
segments(x0=zaza$xsi[9,]$xsi,x1=3,y0=1.1,y1=1.1,col="#e9bf98",lwd=2)
points(x = zaza$xsi[7,]$xsi, y=1.1,pch=9,cex=1)
points(x = zaza$xsi[8,]$xsi, y=1.1,pch=9,cex=1)
points(x = zaza$xsi[9,]$xsi, y=1.1,pch=9,cex=1)
text( x=mean(c(-3,zaza$xsi[7,]$xsi)), y=1.2,"0",col="#200c23" )
text( x=mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)), y=1.2,"1",col="#62403d" )
text( x=mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)), y=1.2,"2",col="#a87b5e" )
text( x=mean(c(zaza$xsi[9,]$xsi,3)), y=1.2,"3",col="#e9bf98" )
text( x=mean(c(-3,zaza$xsi[7,]$xsi)),cex=0.7, y=1.15,"Much less than usual",col="#200c23" )
text( x=mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)),cex=0.7, y=1.15,"Less so than usual",col="#62403d" )
text( x=mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)),cex=0.7, y=1.15,"As much as usual",col="#a87b5e" )
text( x=mean(c(zaza$xsi[9,]$xsi,3)),cex=0.7, y=1.15,"More so than usual",col="#e9bf98" )
text(x=zaza$xsi[7,]$xsi,y=1.15,expression(delta["j,1"]))
text(x=zaza$xsi[8,]$xsi,y=1.15,expression(delta["j,2"]))
text(x=zaza$xsi[9,]$xsi,y=1.15,expression(delta["j,3"]))
text(x = 0,y=1.3,"Most probable response catgory")
CurlyBraces(x0=-2.5, x1=2.5, y0=1.225, y1=1.225, pos = 2, direction = 1, depth=0.05)
arrows(x0=-2.65,x1=-3,y0=-0.35,length = 0.15,lwd = 2)
arrows(x0=2.65,x1=3,y0=-0.35,length = 0.15,lwd = 2)
text(x=-2.55,y=-0.35,"Worse\nmental\nhealth",adj=0)
text(x=2.5,y=-0.35,"Better\nmental\nhealth",adj=1)
rect(xleft = 3,xright=5,ybottom = 0,ytop=1.1,col = "white",border = "white")
lines(x=c(zaza$xsi[7,]$xsi,zaza$xsi[7,]$xsi),y=c(0,-0.15),lty=3)
lines(x=c(zaza$xsi[8,]$xsi,zaza$xsi[8,]$xsi),y=c(0,-0.15),lty=3)
lines(x=c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(0,-0.15),lty=3)
title(main='Example item: \n "Have you been able to enjoy your normal daily activities?" ', font.main=2)
par(xpd=F)
dev.off()
##########################
# ICC / CCC DIF
##########################
plot.tam.dif <- function(x, items=1:x$nitems, type="expected",
low=-3, high=3, ngroups=6, groups_by_item=FALSE,
wle=NULL, export=TRUE, export.type="png",
export.args=list(), observed=TRUE, overlay=FALSE,
ask=FALSE, package="lattice",
fix.devices=TRUE, nnodes=100, ...)
{
require_namespace_msg("grDevices")
if ( package=="lattice"){
require_namespace_msg("lattice")
}
# device.Option <- getOption("device")
pall <- c('#200c23', '#62403d', '#a87b5e', '#e9bf98'
)
low <- low-2
high <- high+2
time1 <- NULL
if ( fix.devices ){
old.opt.dev <- getOption("device")
old.opt.err <- c( getOption("show.error.messages"))
old.par.ask <- graphics::par("ask")
# remember new pars' values
old.par.xpd <- graphics::par("xpd")
old.par.mar <- graphics::par("mar")
on.exit( options("device"=old.opt.dev))
on.exit( options("show.error.messages"=old.opt.err), add=TRUE)
on.exit( graphics::par("ask"=old.par.ask), add=TRUE)
# restore new pars' values
on.exit( graphics::par("xpd"=old.par.xpd), add=TRUE)
on.exit( graphics::par("mar"=old.par.mar), add=TRUE)
}
tamobj <- x
ndim <- tamobj$ndim
tammodel <- "mml"
if(is.null(ndim)) {
ndim <- 1
tammodel <- "jml"
}
if (ndim > 1 ) {
if ( type=="expected"){
stop ("Expected scores curves are only available for uni-dimensional models")
}
}
nitems <- tamobj$nitems
if (ndim==1 ){
theta <- matrix(seq(low, high, length=nnodes), nrow=nnodes, ncol=ndim)
} else {
nodes <- seq(low, high, length=nnodes)
theta <- as.matrix( expand.grid( as.data.frame( matrix( rep(nodes, ndim), ncol=ndim ) ) ) )
nnodes <- nrow(theta)
B <- tamobj$B
}
iIndex <- 1:nitems
A <- tamobj$A
B <- tamobj$B
if (tammodel=="mml") {
xsi <- tamobj$xsi$xsi
} else {
xsi <- tamobj$xsi
}
maxK <- tamobj$maxK
resp <- tamobj$resp
resp.ind <- tamobj$resp.ind
resp[resp.ind==0] <- NA
AXsi <- matrix(0,nrow=nitems,ncol=maxK )
res <- tam_mml_calc_prob(iIndex=iIndex, A=A, AXsi=AXsi, B=B, xsi=xsi, theta=theta,
nnodes=nnodes, maxK=maxK, recalc=TRUE )
rprobs <- res[["rprobs"]]
AXsi <- res[["AXsi"]]
cat <- 1:maxK - 1
#@@@ define initial empty objects
expScore <- obScore <- wle_intervals <- NULL
theta2 <- NULL
#**** type='expected'
if ( type=="expected" ){
expScore <- sapply(1:nitems, function(i) colSums(cat*rprobs[i,,], na.rm=TRUE))
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
wle_intervals <- res$wle_intervals
#-- compute observed scores
obScore <- apply(d2,2, function(x){
stats::aggregate(x, list(groupnumber), mean, na.rm=TRUE)
} )
}
#----------------------------------------------------
# adds observed score for type="items"
if (type=="items") {
require_namespace_msg("plyr")
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
obScore <- lapply(d2, function(item) {
comp_case=stats::complete.cases(item)
item=item[comp_case]
uniq_cats=sort(unique(item))
plyr::ldply(split(item, groupnumber[comp_case]), .id="group",
function (group) {
ngroup=length(group)
cat_freq=list()
for (catt in uniq_cats) {
cat_freq[[paste0("cat_", catt)]]=sum(group==catt)/ngroup
}
data.frame(cat_freq)
})
})
}
#*************************************************
# begin plot function
probs_plot <- as.list(1:nitems)
names(probs_plot) <- items
for (i in (1:nitems)[items]) {
#***********************************************************
#** expected item response curves
if ( type=="expected"){
if (i==1 || !overlay) {
ylim2 <- c(0,max( tamobj$resp[,i], na.rm=TRUE ) )
graphics::plot(theta, expScore[,i],,col=12, type="l", lwd=3, las=1, ylab="Score", xlab="Ability",
main=paste("Expected Scores Curve - Item ", colnames(tamobj$resp)[i] ) ,
ylim=ylim2, ... )
} else {
graphics::lines(theta, expScore[,i],type="l", col=i, lwd=3, pch=1)
}
if (observed){
theta2_i <- theta2
obScore_i <- obScore[[i]]$x
if (groups_by_item){
ind_i <- ! is.na(resp[,i])
resp_i <- resp[ind_i, i, drop=FALSE]
wle_i <- wle[ ind_i ]
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle_i, ngroups=ngroups, resp=resp_i )
theta2_i <- res$theta2
groupnumber_i <- res$groupnumber
aggr <- stats::aggregate(resp_i, list(groupnumber_i), mean, na.rm=TRUE )
obScore_i <- aggr[,2]
}
graphics::lines(theta2_i, obScore_i, type="o", lwd=2, pch=1)
}
}
#***********************************************************
if ( ndim==1 ){ theta0 <- theta }
if ( type=="items"){
rprobs.ii <- rprobs[i,,]
rprobs.ii <- rprobs.ii[ rowMeans( is.na(rprobs.ii) ) < 1, ]
K <- nrow(rprobs.ii)
dat2 <- NULL
#************
if ( ndim > 1 ){
B.ii <- B[i,,]
ind.ii <- which( colSums( B.ii ) > 0 )[1]
rprobs0.ii <- rprobs.ii
rprobs0.ii <- stats::aggregate( t(rprobs0.ii), list( theta[,ind.ii] ), mean )
theta0 <- rprobs0.ii[,1,drop=FALSE]
rprobs.ii <- t( rprobs0.ii[,-1] )
}
probs_plot[[i]] <- rprobs.ii
#**************
for (kk in 1:K){
dat2a <- data.frame( "Theta"=theta0[,1], "cat"=kk, "P"=rprobs.ii[kk,] )
dat2 <- rbind(dat2, dat2a)
}
auto.key <- NULL
simple.key <- paste0("Cat", 1:K - 1)
auto.key <- simple.key
dat2$time <- dat2$cat
dat2$time1 <- paste0("Cat", dat2$time )
simple.key <- FALSE
Kpercol <- K
# package graphics
if ( package=="graphics" ){
kk <- 1
dfr <- dat2
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::plot( ifelse(dfr1a$Theta+0.5>-3,dfr1a$Theta+0.5,NA), dfr1a$P, ylim=c(-.1,1.1),xlim=c(-3,3),
xlab=expression(theta),
col=pall[kk], type="l", xpd=TRUE,axes=F, ...
)
axis(1)
axis(2)
grid(nx = NA,
ny = NULL,
lty = 3, col = "lightgray", lwd = 1)
graphics::lines( ifelse(dfr1a$Theta+0.5>-3,dfr1a$Theta+0.5,NA), dfr1a$P,xlim=c(-3,3),
col=pall[kk], type="l", ...
)
for (kk in seq(2,K) ){
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::lines( ifelse(dfr1a$Theta+0.5>-3,dfr1a$Theta+0.5,NA), dfr1a$P, col=pall[kk] )
# graphics::points( dfr1a$Theta, dfr1a$P, pch=kk, col=kk+1 )
}
}
#***************************************
}
#***************
graphics::par(ask=ask)
} # end item ii
#*************************************************
}
#### CCC
zaz <- read.csv("/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_2A_100.csv")
zaz <- zaz[zaz$replication==1,]
zaza <- tam.mml(resp = zaz[,paste0("item",1:4)])
# CCC DIF
# base
pdf(file = '/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/PDF/ccc_dif_1.pdf')
par(xpd=F,mar=c(6.1,5.1,7.6,2.1))
plot.tam.2(zaza,type = "items",export=F,ylab="Probability of response",main=NULL,package = "graphics",items = 3)
segments(x0=zaza$xsi[7,]$xsi,x1=zaza$xsi[7,]$xsi,y0=0,y1=1.1,lty=3)
segments(x0=zaza$xsi[8,]$xsi,x1=zaza$xsi[8,]$xsi,y0=0,y1=1.1,lty=3)
segments(x0=zaza$xsi[9,]$xsi,x1=zaza$xsi[9,]$xsi,y0=0,y1=1.1,lty=3)
text(x=-2.5,y=0.85,"0",col="#200c23")
text(x=-0.65,y=0.55,"1",col="#62403d")
text(x=0.95,y=0.55,"2",col="#a87b5e")
text(x=2.5,y=0.85,"3",col="#e9bf98")
par(xpd=T,mar=c(5.1,4.1,4.1,2.1))
segments(x0=-3,x1=zaza$xsi[7,]$xsi,y0=1.1,y1=1.1,col="#200c23",lwd=2)
segments(x0=zaza$xsi[7,]$xsi,x1=zaza$xsi[8,]$xsi,y0=1.1,y1=1.1,col="#62403d",lwd=2)
segments(x0=zaza$xsi[8,]$xsi,x1=zaza$xsi[9,]$xsi,y0=1.1,y1=1.1,col="#a87b5e",lwd=2)
segments(x0=zaza$xsi[9,]$xsi,x1=3,y0=1.1,y1=1.1,col="#e9bf98",lwd=2)
points(x = zaza$xsi[7,]$xsi, y=1.1,pch=9,cex=1)
points(x = zaza$xsi[8,]$xsi, y=1.1,pch=9,cex=1)
points(x = zaza$xsi[9,]$xsi, y=1.1,pch=9,cex=1)
text( x=mean(c(-3,zaza$xsi[7,]$xsi)), y=1.2,"0",col="#200c23" )
text( x=mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)), y=1.2,"1",col="#62403d" )
text( x=mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)), y=1.2,"2",col="#a87b5e" )
text( x=mean(c(zaza$xsi[9,]$xsi,3)), y=1.2,"3",col="#e9bf98" )
text( x=mean(c(-3,zaza$xsi[7,]$xsi)),cex=0.7, y=1.15,"Much less than usual",col="#200c23" )
text( x=mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)),cex=0.7, y=1.15,"Less so than usual",col="#62403d" )
text( x=mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)),cex=0.7, y=1.15,"As much as usual",col="#a87b5e" )
text( x=mean(c(zaza$xsi[9,]$xsi,3)),cex=0.7, y=1.15,"More so than usual",col="#e9bf98" )
text(x=zaza$xsi[7,]$xsi,y=1.15,expression(delta["j,1"]))
text(x=zaza$xsi[8,]$xsi,y=1.15,expression(delta["j,2"]))
text(x=zaza$xsi[9,]$xsi,y=1.15,expression(delta["j,3"]))
text(x = 0,y=1.3,"Most probable response catgory")
CurlyBraces(x0=-2.5, x1=2.5, y0=1.225, y1=1.225, pos = 2, direction = 1, depth=0.05)
arrows(x0=-2.65,x1=-3,y0=-0.35,length = 0.15,lwd = 2)
arrows(x0=2.65,x1=3,y0=-0.35,length = 0.15,lwd = 2)
text(x=-2.55,y=-0.35,"Worse\nmental\nhealth",adj=0)
text(x=2.5,y=-0.35,"Better\nmental\nhealth",adj=1)
rect(xleft = 3,xright=5,ybottom = 0,ytop=1.1,col = "white",border = "white")
lines(x=c(zaza$xsi[7,]$xsi,zaza$xsi[7,]$xsi),y=c(0,-0.6),lty=3)
lines(x=c(zaza$xsi[8,]$xsi,zaza$xsi[8,]$xsi),y=c(0,-0.6),lty=3)
lines(x=c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(0,-0.6),lty=3)
title(main='Example item: \n "Have you been able to enjoy your normal daily activities?" ', font.main=2)
par(xpd=F)
dev.off()
# DIF homogène
pdf(file = '/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/PDF/ccc_dif_2.pdf')
par(xpd=F,mar=c(12.6,5.1,1.1,2.1))
plot.tam.dif(zaza,type = "items",export=F,ylab="Probability of response",main=NULL,package = "graphics",items = 3)
text(x=-2,y=0.85,"0",col="#200c23")
text(x=-.05,y=0.55,"1",col="#62403d")
text(x=1.45,y=0.55,"2",col="#a87b5e")
text(x=2.5,y=0.7,"3",col="#e9bf98")
par(xpd=T,mar=c(5.1,4.1,4.1,2.1))
segments(x0=-3,x1=.5+zaza$xsi[7,]$xsi,y0=-0.5,col="#200c23",lwd=2)
segments(x0=.5+zaza$xsi[7,]$xsi,x1=.5+zaza$xsi[8,]$xsi,y0=-0.5,col="#62403d",lwd=2)
segments(x0=.5+zaza$xsi[8,]$xsi,x1=.5+zaza$xsi[9,]$xsi,y0=-0.5,col="#a87b5e",lwd=2)
segments(x0=.5+zaza$xsi[9,]$xsi,x1=3,y0=-0.5,col="#e9bf98",lwd=2)
points(x =.5 + zaza$xsi[7,]$xsi, y=-0.5,pch=9,cex=1)
points(x =.5 + zaza$xsi[8,]$xsi, y=-0.5,pch=9,cex=1)
points(x =.5 + zaza$xsi[9,]$xsi, y=-0.5,pch=9,cex=1)
text( x=mean(c(-2.5,zaza$xsi[7,]$xsi)), y=-0.65,"0",col="#200c23" )
text( x=0.5+mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)), y=-0.65,"1",col="#62403d" )
text( x=0.5+mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)), y=-0.65,"2",col="#a87b5e" )
text( x=0.5+mean(c(zaza$xsi[9,]$xsi,2.5)), y=-0.65,"3",col="#e9bf98" )
text( x=mean(c(-2.5,zaza$xsi[7,]$xsi)),cex=0.7, y=-0.6,"Much less than usual",col="#200c23" )
text( x=.5+mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)),cex=0.7, y=-0.6,"Less so than usual",col="#62403d" )
text( x=.5+mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)),cex=0.7, y=-0.6,"As much as usual",col="#a87b5e" )
text( x=.5+mean(c(zaza$xsi[9,]$xsi,2.5)),cex=0.7, y=-0.6,"More so than usual",col="#e9bf98" )
text(x=0.5+zaza$xsi[7,]$xsi,y=-0.55,expression(delta["j,1"]))
text(x=0.5+zaza$xsi[8,]$xsi,y=-0.55,expression(delta["j,2"]))
text(x=0.5+zaza$xsi[9,]$xsi,y=-0.55,expression(delta["j,3"]))
arrows(x0=zaza$xsi[7,]$xsi+0.05,x1=zaza$xsi[7,]$xsi+0.5-0.05,y0=0.625,length = 0.1, lwd = 2)
arrows(x0=zaza$xsi[8,]$xsi+0.05,x1=zaza$xsi[8,]$xsi+0.5-0.05,y0=0.625,length = 0.1, lwd = 2)
arrows(x0=zaza$xsi[9,]$xsi+0.05,x1=zaza$xsi[9,]$xsi+0.5-0.05,y0=0.625,length = 0.1, lwd = 2)
text(x=0.25+zaza$xsi[7,]$xsi,y=0.675,expression(gamma["j,1"]))
text(x=0.25+zaza$xsi[8,]$xsi,y=0.675,expression(gamma["j,2"]))
text(x=0.25+zaza$xsi[9,]$xsi,y=0.675,expression(gamma["j,3"]))
text(x = 0.25,y=-0.75,"Most probable response catgory")
CurlyBraces(x0=-2.25, x1=2.75, y0=-0.675, y1=-0.675, pos = 2, direction = 2, depth=0.05)
arrows(x0=-2.65,x1=-3,y0=-0.35,length = 0.15,lwd = 2)
arrows(x0=2.65,x1=3,y0=-0.35,length = 0.15,lwd = 2)
text(x=-2.55,y=-0.35,"Worse\nmental\nhealth",adj=0)
text(x=2.5,y=-0.35,"Better\nmental\nhealth",adj=1)
segments(x0=zaza$xsi[7,]$xsi,x1=zaza$xsi[7,]$xsi,y0=0.6,y1=1.6,lty=3)
segments(x0=zaza$xsi[8,]$xsi,x1=zaza$xsi[8,]$xsi,y0=0.6,y1=1.6,lty=3)
segments(x0=zaza$xsi[9,]$xsi,x1=zaza$xsi[9,]$xsi,y0=0.6,y1=1.6,lty=3)
rect(xleft = 3,xright=5,ybottom = -.1,ytop=1.1,col = "white",border = "white")
lines(x=.5+c(zaza$xsi[7,]$xsi,zaza$xsi[7,]$xsi),y=c(0.65,-0.5),lty=3)
lines(x=.5+c(zaza$xsi[8,]$xsi,zaza$xsi[8,]$xsi),y=c(0.65,-0.5),lty=3)
lines(x=.5+c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(0.65,-0.25),lty=3)
lines(x=.5+c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(-0.45,-0.5),lty=3)
par(xpd=F)
dev.off()
# CCC DIF HETEROGENE 1
plot.tam.difhet1 <- function(x, items=1:x$nitems, type="expected",
low=-3, high=3, ngroups=6, groups_by_item=FALSE,
wle=NULL, export=TRUE, export.type="png",
export.args=list(), observed=TRUE, overlay=FALSE,
ask=FALSE, package="lattice",
fix.devices=TRUE, nnodes=100, ...)
{
require_namespace_msg("grDevices")
if ( package=="lattice"){
require_namespace_msg("lattice")
}
low <- low-2
high <- high+2
# device.Option <- getOption("device")
pall <- c('#200c23', '#62403d', '#a87b5e', '#e9bf98'
)
time1 <- NULL
if ( fix.devices ){
old.opt.dev <- getOption("device")
old.opt.err <- c( getOption("show.error.messages"))
old.par.ask <- graphics::par("ask")
# remember new pars' values
old.par.xpd <- graphics::par("xpd")
old.par.mar <- graphics::par("mar")
on.exit( options("device"=old.opt.dev))
on.exit( options("show.error.messages"=old.opt.err), add=TRUE)
on.exit( graphics::par("ask"=old.par.ask), add=TRUE)
# restore new pars' values
on.exit( graphics::par("xpd"=old.par.xpd), add=TRUE)
on.exit( graphics::par("mar"=old.par.mar), add=TRUE)
}
tamobj <- x
ndim <- tamobj$ndim
tammodel <- "mml"
if(is.null(ndim)) {
ndim <- 1
tammodel <- "jml"
}
if (ndim > 1 ) {
if ( type=="expected"){
stop ("Expected scores curves are only available for uni-dimensional models")
}
}
nitems <- tamobj$nitems
if (ndim==1 ){
theta <- matrix(seq(low, high, length=nnodes), nrow=nnodes, ncol=ndim)
} else {
nodes <- seq(low, high, length=nnodes)
theta <- as.matrix( expand.grid( as.data.frame( matrix( rep(nodes, ndim), ncol=ndim ) ) ) )
nnodes <- nrow(theta)
B <- tamobj$B
}
iIndex <- 1:nitems
A <- tamobj$A
B <- tamobj$B
if (tammodel=="mml") {
xsi <- tamobj$xsi$xsi
} else {
xsi <- tamobj$xsi
}
maxK <- tamobj$maxK
resp <- tamobj$resp
resp.ind <- tamobj$resp.ind
resp[resp.ind==0] <- NA
AXsi <- matrix(0,nrow=nitems,ncol=maxK )
res <- tam_mml_calc_prob(iIndex=iIndex, A=A, AXsi=AXsi, B=B, xsi=xsi, theta=theta,
nnodes=nnodes, maxK=maxK, recalc=TRUE )
rprobs <- res[["rprobs"]]
AXsi <- res[["AXsi"]]
cat <- 1:maxK - 1
#@@@ define initial empty objects
expScore <- obScore <- wle_intervals <- NULL
theta2 <- NULL
#**** type='expected'
if ( type=="expected" ){
expScore <- sapply(1:nitems, function(i) colSums(cat*rprobs[i,,], na.rm=TRUE))
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
wle_intervals <- res$wle_intervals
#-- compute observed scores
obScore <- apply(d2,2, function(x){
stats::aggregate(x, list(groupnumber), mean, na.rm=TRUE)
} )
}
#----------------------------------------------------
# adds observed score for type="items"
if (type=="items") {
require_namespace_msg("plyr")
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
obScore <- lapply(d2, function(item) {
comp_case=stats::complete.cases(item)
item=item[comp_case]
uniq_cats=sort(unique(item))
plyr::ldply(split(item, groupnumber[comp_case]), .id="group",
function (group) {
ngroup=length(group)
cat_freq=list()
for (catt in uniq_cats) {
cat_freq[[paste0("cat_", catt)]]=sum(group==catt)/ngroup
}
data.frame(cat_freq)
})
})
}
#*************************************************
# begin plot function
probs_plot <- as.list(1:nitems)
names(probs_plot) <- items
for (i in (1:nitems)[items]) {
#***********************************************************
#** expected item response curves
if ( type=="expected"){
if (i==1 || !overlay) {
ylim2 <- c(0,max( tamobj$resp[,i], na.rm=TRUE ) )
graphics::plot(theta, expScore[,i],,col=12, type="l", lwd=3, las=1, ylab="Score", xlab="Ability",
main=paste("Expected Scores Curve - Item ", colnames(tamobj$resp)[i] ) ,
ylim=ylim2, ... )
} else {
graphics::lines(theta, expScore[,i],type="l", col=i, lwd=3, pch=1)
}
if (observed){
theta2_i <- theta2
obScore_i <- obScore[[i]]$x
if (groups_by_item){
ind_i <- ! is.na(resp[,i])
resp_i <- resp[ind_i, i, drop=FALSE]
wle_i <- wle[ ind_i ]
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle_i, ngroups=ngroups, resp=resp_i )
theta2_i <- res$theta2
groupnumber_i <- res$groupnumber
aggr <- stats::aggregate(resp_i, list(groupnumber_i), mean, na.rm=TRUE )
obScore_i <- aggr[,2]
}
graphics::lines(theta2_i, obScore_i, type="o", lwd=2, pch=1)
}
}
#***********************************************************
if ( ndim==1 ){ theta0 <- theta }
if ( type=="items"){
rprobs.ii <- rprobs[i,,]
rprobs.ii <- rprobs.ii[ rowMeans( is.na(rprobs.ii) ) < 1, ]
K <- nrow(rprobs.ii)
dat2 <- NULL
#************
if ( ndim > 1 ){
B.ii <- B[i,,]
ind.ii <- which( colSums( B.ii ) > 0 )[1]
rprobs0.ii <- rprobs.ii
rprobs0.ii <- stats::aggregate( t(rprobs0.ii), list( theta[,ind.ii] ), mean )
theta0 <- rprobs0.ii[,1,drop=FALSE]
rprobs.ii <- t( rprobs0.ii[,-1] )
}
probs_plot[[i]] <- rprobs.ii
#**************
for (kk in 1:K){
dat2a <- data.frame( "Theta"=theta0[,1], "cat"=kk, "P"=rprobs.ii[kk,] )
dat2 <- rbind(dat2, dat2a)
}
auto.key <- NULL
simple.key <- paste0("Cat", 1:K - 1)
auto.key <- simple.key
dat2$time <- dat2$cat
dat2$time1 <- paste0("Cat", dat2$time )
simple.key <- FALSE
Kpercol <- K
# package graphics
if ( package=="graphics" ){
kk <- 1
dfr <- dat2
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::plot( ifelse(dfr1a$Theta+0.5>-3 & dfr1a$Theta+0.5<3, dfr1a$Theta+0.5, NA), dfr1a$P, ylim=c(-.1,1.1),xlim=c(-3,3),
xlab=expression(theta),
col=pall[kk], type="l", xpd=TRUE,axes=F, ...
)
axis(1)
axis(2)
grid(nx = NA,
ny = NULL,
lty = 3, col = "lightgray", lwd = 1)
graphics::lines( ifelse(dfr1a$Theta+0.5>-3 & dfr1a$Theta+0.5<3, dfr1a$Theta+0.5, NA), dfr1a$P,xlim=c(-3,3),
col=pall[kk], type="l", ...
)
for (kk in seq(2,K) ){
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::lines(ifelse(dfr1a$Theta+ifelse(kk==3,1,ifelse(kk==4,0.35,0.5))>-3 & dfr1a$Theta+ifelse(kk==3,1,ifelse(kk==4,0.35,0.5))<3, dfr1a$Theta, NA)+ifelse(kk==3,1,ifelse(kk==4,0.35,0.5)), dfr1a$P, col=pall[kk] )
# graphics::points( dfr1a$Theta, dfr1a$P, pch=kk, col=kk+1 )
}
}
#***************************************
}
#***************
graphics::par(ask=ask)
} # end item ii
#*************************************************
}
# DIF heterogene convergent
pdf(file = '/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/PDF/ccc_dif_het1.pdf')
par(xpd=F,mar=c(12.6,5.1,1.1,2.1))
plot.tam.difhet1(zaza,type = "items",export=F,ylab="Probability of response",main=NULL,package = "graphics",items = 3)
text(x=-2,y=0.85,"0",col="#200c23")
text(x=-0.15,y=0.55,"1",col="#62403d")
text(x=1.45+0.475,y=0.55,"2",col="#a87b5e")
text(x=2.5,y=0.7,"3",col="#e9bf98")
par(xpd=T,mar=c(5.1,4.1,4.1,2.1))
segments(x0=-3,x1=.5+zaza$xsi[7,]$xsi,y0=-0.5,col="#200c23",lwd=2)
segments(x0=.5+zaza$xsi[7,]$xsi,x1=.5+zaza$xsi[8,]$xsi+0.25,y0=-0.5,col="#62403d",lwd=2)
segments(x0=.5+zaza$xsi[8,]$xsi+0.25,x1=.5+zaza$xsi[9,]$xsi,y0=-0.5,col="#a87b5e",lwd=2)
segments(x0=.5+zaza$xsi[9,]$xsi,x1=3,y0=-0.5,col="#e9bf98",lwd=2)
points(x =.5 + zaza$xsi[7,]$xsi, y=-0.5,pch=9,cex=1)
points(x =.5 + zaza$xsi[8,]$xsi+0.25, y=-0.5,pch=9,cex=1)
points(x =.5 + zaza$xsi[9,]$xsi, y=-0.5,pch=9,cex=1)
rect(xleft = 3,xright=5,ybottom = 0,ytop=1.1,col = "white",border = "white")
text( x=mean(c(-2.5,zaza$xsi[7,]$xsi)), y=-0.65,"0",col="#200c23" )
text( x=0.125+0.5+mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)), y=-0.65,"1",col="#62403d" )
text( x=0.5+0.125+mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)), y=-0.65,"2",col="#a87b5e" )
text( x=0.5+mean(c(zaza$xsi[9,]$xsi,2.5)), y=-0.65,"3",col="#e9bf98" )
text( x=mean(c(-2.5,zaza$xsi[7,]$xsi)),cex=0.7, y=-0.6,"Much less than usual",col="#200c23" )
text( x=0.125+.5+mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)),cex=0.7, y=-0.6,"Less so than usual",col="#62403d" )
text( x=.125+.5+mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)),cex=0.7, y=-0.6,"As much as usual",col="#a87b5e" )
text( x=.5+mean(c(zaza$xsi[9,]$xsi,2.5)),cex=0.7, y=-0.6,"More so than usual",col="#e9bf98" )
text(x=0.5+zaza$xsi[7,]$xsi,y=-0.55,expression(delta["j,1"]))
text(x=0.5+zaza$xsi[8,]$xsi+0.25,y=-0.55,expression(delta["j,2"]))
text(x=0.5+zaza$xsi[9,]$xsi,y=-0.55,expression(delta["j,3"]))
arrows(x0=zaza$xsi[7,]$xsi+0.05,x1=zaza$xsi[7,]$xsi+0.5-0.05,y0=0.625,length = 0.1, lwd = 2)
arrows(x0=zaza$xsi[8,]$xsi+0.05,x1=zaza$xsi[8,]$xsi+0.75-0.05,y0=0.625,length = 0.1, lwd = 2,col="darkred")
arrows(x0=zaza$xsi[9,]$xsi+0.05,x1=zaza$xsi[9,]$xsi+0.5-0.05,y0=0.625,length = 0.1, lwd = 2)
text(x=0.25+zaza$xsi[7,]$xsi,y=0.675,expression(gamma["j,1"]))
text(x=0.25+zaza$xsi[8,]$xsi+0.125,y=0.675,expression(gamma["j,2"]),col="darkred")
text(x=0.25+zaza$xsi[9,]$xsi,y=0.675,expression(gamma["j,3"]))
text(x = 0.25,y=-0.75,"Most probable response catgory")
CurlyBraces(x0=-2.25, x1=2.75, y0=-0.675, y1=-0.675, pos = 2, direction = 2, depth=0.05)
arrows(x0=-2.65,x1=-3,y0=-0.35,length = 0.15,lwd = 2)
arrows(x0=2.65,x1=3,y0=-0.35,length = 0.15,lwd = 2)
text(x=-2.55,y=-0.35,"Worse\nmental\nhealth",adj=0)
text(x=2.5,y=-0.35,"Better\nmental\nhealth",adj=1)
segments(x0=zaza$xsi[7,]$xsi,x1=zaza$xsi[7,]$xsi,y0=0.6,y1=1.6,lty=3)
segments(x0=zaza$xsi[8,]$xsi,x1=zaza$xsi[8,]$xsi,y0=0.6,y1=1.6,lty=3)
segments(x0=zaza$xsi[9,]$xsi,x1=zaza$xsi[9,]$xsi,y0=0.6,y1=1.6,lty=3)
rect(xleft = 3,xright=5,ybottom = -.1,ytop=1.1,col = "white",border = "white")
lines(x=.5+c(zaza$xsi[7,]$xsi,zaza$xsi[7,]$xsi),y=c(0.65,-0.5),lty=3)
lines(x=.5+c(zaza$xsi[8,]$xsi+0.25,zaza$xsi[8,]$xsi+0.25),y=c(0.65,-0.5),lty=3)
lines(x=.5+c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(0.65,-0.25),lty=3)
lines(x=.5+c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(-0.45,-0.5),lty=3)
par(xpd=F)
dev.off()
# CCC DIF HETEROGENE 2
plot.tam.difhet2 <- function(x, items=1:x$nitems, type="expected",
low=-3, high=3, ngroups=6, groups_by_item=FALSE,
wle=NULL, export=TRUE, export.type="png",
export.args=list(), observed=TRUE, overlay=FALSE,
ask=FALSE, package="lattice",
fix.devices=TRUE, nnodes=100, ...)
{
require_namespace_msg("grDevices")
if ( package=="lattice"){
require_namespace_msg("lattice")
}
low <- low-2
high <- high+2
# device.Option <- getOption("device")
pall <- c('#200c23', '#62403d', '#a87b5e', '#e9bf98'
)
time1 <- NULL
if ( fix.devices ){
old.opt.dev <- getOption("device")
old.opt.err <- c( getOption("show.error.messages"))
old.par.ask <- graphics::par("ask")
# remember new pars' values
old.par.xpd <- graphics::par("xpd")
old.par.mar <- graphics::par("mar")
on.exit( options("device"=old.opt.dev))
on.exit( options("show.error.messages"=old.opt.err), add=TRUE)
on.exit( graphics::par("ask"=old.par.ask), add=TRUE)
# restore new pars' values
on.exit( graphics::par("xpd"=old.par.xpd), add=TRUE)
on.exit( graphics::par("mar"=old.par.mar), add=TRUE)
}
tamobj <- x
ndim <- tamobj$ndim
tammodel <- "mml"
if(is.null(ndim)) {
ndim <- 1
tammodel <- "jml"
}
if (ndim > 1 ) {
if ( type=="expected"){
stop ("Expected scores curves are only available for uni-dimensional models")
}
}
nitems <- tamobj$nitems
if (ndim==1 ){
theta <- matrix(seq(low, high, length=nnodes), nrow=nnodes, ncol=ndim)
} else {
nodes <- seq(low, high, length=nnodes)
theta <- as.matrix( expand.grid( as.data.frame( matrix( rep(nodes, ndim), ncol=ndim ) ) ) )
nnodes <- nrow(theta)
B <- tamobj$B
}
iIndex <- 1:nitems
A <- tamobj$A
B <- tamobj$B
if (tammodel=="mml") {
xsi <- tamobj$xsi$xsi
} else {
xsi <- tamobj$xsi
}
maxK <- tamobj$maxK
resp <- tamobj$resp
resp.ind <- tamobj$resp.ind
resp[resp.ind==0] <- NA
AXsi <- matrix(0,nrow=nitems,ncol=maxK )
res <- tam_mml_calc_prob(iIndex=iIndex, A=A, AXsi=AXsi, B=B, xsi=xsi, theta=theta,
nnodes=nnodes, maxK=maxK, recalc=TRUE )
rprobs <- res[["rprobs"]]
AXsi <- res[["AXsi"]]
cat <- 1:maxK - 1
#@@@ define initial empty objects
expScore <- obScore <- wle_intervals <- NULL
theta2 <- NULL
#**** type='expected'
if ( type=="expected" ){
expScore <- sapply(1:nitems, function(i) colSums(cat*rprobs[i,,], na.rm=TRUE))
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
wle_intervals <- res$wle_intervals
#-- compute observed scores
obScore <- apply(d2,2, function(x){
stats::aggregate(x, list(groupnumber), mean, na.rm=TRUE)
} )
}
#----------------------------------------------------
# adds observed score for type="items"
if (type=="items") {
require_namespace_msg("plyr")
#-- compute WLE score groups
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle, ngroups=ngroups, resp=resp )
wle <- res$wle
theta2 <- res$theta2
d <- res$d
d1 <- res$d1
d2 <- res$d2
groupnumber <- res$groupnumber
ngroups <- res$ngroups
obScore <- lapply(d2, function(item) {
comp_case=stats::complete.cases(item)
item=item[comp_case]
uniq_cats=sort(unique(item))
plyr::ldply(split(item, groupnumber[comp_case]), .id="group",
function (group) {
ngroup=length(group)
cat_freq=list()
for (catt in uniq_cats) {
cat_freq[[paste0("cat_", catt)]]=sum(group==catt)/ngroup
}
data.frame(cat_freq)
})
})
}
#*************************************************
# begin plot function
probs_plot <- as.list(1:nitems)
names(probs_plot) <- items
for (i in (1:nitems)[items]) {
#***********************************************************
#** expected item response curves
if ( type=="expected"){
if (i==1 || !overlay) {
ylim2 <- c(0,max( tamobj$resp[,i], na.rm=TRUE ) )
graphics::plot(theta, expScore[,i],,col=12, type="l", lwd=3, las=1, ylab="Score", xlab="Ability",
main=paste("Expected Scores Curve - Item ", colnames(tamobj$resp)[i] ) ,
ylim=ylim2, ... )
} else {
graphics::lines(theta, expScore[,i],type="l", col=i, lwd=3, pch=1)
}
if (observed){
theta2_i <- theta2
obScore_i <- obScore[[i]]$x
if (groups_by_item){
ind_i <- ! is.na(resp[,i])
resp_i <- resp[ind_i, i, drop=FALSE]
wle_i <- wle[ ind_i ]
res <- plot_tam_grouped_wle( tamobj=tamobj, tammodel=tammodel,
wle=wle_i, ngroups=ngroups, resp=resp_i )
theta2_i <- res$theta2
groupnumber_i <- res$groupnumber
aggr <- stats::aggregate(resp_i, list(groupnumber_i), mean, na.rm=TRUE )
obScore_i <- aggr[,2]
}
graphics::lines(theta2_i, obScore_i, type="o", lwd=2, pch=1)
}
}
#***********************************************************
if ( ndim==1 ){ theta0 <- theta }
if ( type=="items"){
rprobs.ii <- rprobs[i,,]
rprobs.ii <- rprobs.ii[ rowMeans( is.na(rprobs.ii) ) < 1, ]
K <- nrow(rprobs.ii)
dat2 <- NULL
#************
if ( ndim > 1 ){
B.ii <- B[i,,]
ind.ii <- which( colSums( B.ii ) > 0 )[1]
rprobs0.ii <- rprobs.ii
rprobs0.ii <- stats::aggregate( t(rprobs0.ii), list( theta[,ind.ii] ), mean )
theta0 <- rprobs0.ii[,1,drop=FALSE]
rprobs.ii <- t( rprobs0.ii[,-1] )
}
probs_plot[[i]] <- rprobs.ii
#**************
for (kk in 1:K){
dat2a <- data.frame( "Theta"=theta0[,1], "cat"=kk, "P"=rprobs.ii[kk,] )
dat2 <- rbind(dat2, dat2a)
}
auto.key <- NULL
simple.key <- paste0("Cat", 1:K - 1)
auto.key <- simple.key
dat2$time <- dat2$cat
dat2$time1 <- paste0("Cat", dat2$time )
simple.key <- FALSE
Kpercol <- K
# package graphics
if ( package=="graphics" ){
kk <- 1
dfr <- dat2
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::plot(ifelse(dfr1a$Theta-1.125>-3,dfr1a$Theta-1.125,NA) , dfr1a$P, ylim=c(-.1,1.1),xlim=c(-3,3),
xlab=expression(theta),
col=pall[kk], type="l", xpd=TRUE,axes=F, ...
)
axis(1)
axis(2)
grid(nx = NA,
ny = NULL,
lty = 3, col = "lightgray", lwd = 1)
graphics::lines( ifelse(dfr1a$Theta-1.125>-3,dfr1a$Theta-1.125,NA), dfr1a$P,xlim=c(-3,3),
col=pall[kk], type="l", xlim=c(-3,3), ...
)
for (kk in seq(2,K) ){
dfr1a <- dfr[ dfr$cat==kk, ]
graphics::lines( ifelse(dfr1a$Theta+ifelse(kk==3,1,ifelse(kk==4,0.35,0.5))>-3,dfr1a$Theta,NA)+ifelse(kk==3,1,ifelse(kk==4,0.35,0.5)), dfr1a$P, col=pall[kk] , xlim=c(-3,3))
# graphics::points( dfr1a$Theta, dfr1a$P, pch=kk, col=kk+1 )
}
}
#***************************************
}
#***************
graphics::par(ask=ask)
} # end item ii
#*************************************************
}
# DIF heterogene divergent
pdf(file = '/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/PDF/ccc_dif_het2.pdf')
par(xpd=F,mar=c(12.6,5.1,1.1,2.1))
plot.tam.difhet2(zaza,type = "items",export=F,ylab="Probability of response",main=NULL,package = "graphics",items = 3)
text(x=-2.5,y=0.6,"0",col="#200c23")
text(x=-0.15,y=0.55,"1",col="#62403d")
text(x=1.45+0.475,y=0.55,"2",col="#a87b5e")
text(x=2.5,y=0.7,"3",col="#e9bf98")
par(xpd=T,mar=c(5.1,4.1,4.1,2.1))
segments(x0=-3,x1=-.5+zaza$xsi[7,]$xsi,y0=-0.5,col="#200c23",lwd=2)
segments(x0=-.5+zaza$xsi[7,]$xsi,x1=.5+zaza$xsi[8,]$xsi+0.25,y0=-0.5,col="#62403d",lwd=2)
segments(x0=.5+zaza$xsi[8,]$xsi+0.25,x1=.5+zaza$xsi[9,]$xsi,y0=-0.5,col="#a87b5e",lwd=2)
segments(x0=.5+zaza$xsi[9,]$xsi,x1=3,y0=-0.5,col="#e9bf98",lwd=2)
points(x =.5 + zaza$xsi[7,]$xsi-1, y=-0.5,pch=9,cex=1)
points(x =.5 + zaza$xsi[8,]$xsi+0.25, y=-0.5,pch=9,cex=1)
points(x =.5 + zaza$xsi[9,]$xsi, y=-0.5,pch=9,cex=1)
rect(xleft = 3,xright=5,ybottom = 0,ytop=1.1,col = "white",border = "white")
text( x=-0.25+mean(c(-3,zaza$xsi[7,]$xsi)), y=-0.65,"0",col="#200c23" )
text( x=-0.5+0.125+0.5+mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)), y=-0.65,"1",col="#62403d" )
text( x=0.5+0.125+mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)), y=-0.65,"2",col="#a87b5e" )
text( x=0.5+mean(c(zaza$xsi[9,]$xsi,2.5)), y=-0.65,"3",col="#e9bf98" )
text( x=-0.25+mean(c(-3,zaza$xsi[7,]$xsi)),cex=0.7, y=-0.6,"Much less than usual",col="#200c23" )
text( x=-0.5+0.125+.5+mean(c(zaza$xsi[7,]$xsi,zaza$xsi[8,]$xsi)),cex=0.7, y=-0.6,"Less so than usual",col="#62403d" )
text( x=.125+.5+mean(c(zaza$xsi[8,]$xsi,zaza$xsi[9,]$xsi)),cex=0.7, y=-0.6,"As much as usual",col="#a87b5e" )
text( x=.5+mean(c(zaza$xsi[9,]$xsi,2.5)),cex=0.7, y=-0.6,"More so than usual",col="#e9bf98" )
text(x=-0.5+zaza$xsi[7,]$xsi,y=-0.55,expression(delta["j,1"]))
text(x=0.5+zaza$xsi[8,]$xsi+0.25,y=-0.55,expression(delta["j,2"]))
text(x=0.5+zaza$xsi[9,]$xsi,y=-0.55,expression(delta["j,3"]))
arrows(x0=zaza$xsi[7,]$xsi-0.05,x1=zaza$xsi[7,]$xsi-0.5+0.05,y0=0.625,length = 0.1, lwd = 2,col="darkred")
arrows(x0=zaza$xsi[8,]$xsi+0.05,x1=zaza$xsi[8,]$xsi+0.75-0.05,y0=0.625,length = 0.1, lwd = 2,col="darkred")
arrows(x0=zaza$xsi[9,]$xsi+0.05,x1=zaza$xsi[9,]$xsi+0.5-0.05,y0=0.625,length = 0.1, lwd = 2)
text(x=-0.25+zaza$xsi[7,]$xsi,y=0.675,expression(gamma["j,1"]),col="darkred")
text(x=0.25+zaza$xsi[8,]$xsi+0.125,y=0.675,expression(gamma["j,2"]),col="darkred")
text(x=0.25+zaza$xsi[9,]$xsi,y=0.675,expression(gamma["j,3"]))
text(x = 0.25,y=-0.75,"Most probable response catgory")
CurlyBraces(x0=-2.75, x1=2.75, y0=-0.675, y1=-0.675, pos = 2, direction = 2, depth=0.05)
arrows(x0=-2.65,x1=-3,y0=-0.35,length = 0.15,lwd = 2)
arrows(x0=2.65,x1=3,y0=-0.35,length = 0.15,lwd = 2)
text(x=-2.55,y=-0.35,"Worse\nmental\nhealth",adj=0)
text(x=2.5,y=-0.35,"Better\nmental\nhealth",adj=1)
rect(xleft = 3,xright=5,ybottom = -.1,ytop=1.1,col = "white",border = "white")
segments(x0=zaza$xsi[7,]$xsi,x1=zaza$xsi[7,]$xsi,y0=0.6,y1=1.6,lty=3)
segments(x0=zaza$xsi[8,]$xsi,x1=zaza$xsi[8,]$xsi,y0=0.6,y1=1.6,lty=3)
segments(x0=zaza$xsi[9,]$xsi,x1=zaza$xsi[9,]$xsi,y0=0.6,y1=1.6,lty=3)
lines(x=-.25+c(zaza$xsi[7,]$xsi,zaza$xsi[7,]$xsi)-.25,y=c(0.65,-0.325),lty=3)
lines(x=-.25+c(zaza$xsi[7,]$xsi,zaza$xsi[7,]$xsi)-.25,y=c(-0.375,-0.5),lty=3)
lines(x=.5+c(zaza$xsi[8,]$xsi+0.25,zaza$xsi[8,]$xsi+0.25),y=c(0.65,-0.5),lty=3)
lines(x=.5+c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(0.65,-0.25),lty=3)
lines(x=.5+c(zaza$xsi[9,]$xsi,zaza$xsi[9,]$xsi),y=c(-0.45,-0.5),lty=3)
par(xpd=F)
dev.off()
##########################
# LM FACTEURS IGNORE DIF
##########################
####### SCENARIOS SANS TE
res.dat.article.ignore.h0 <- res.dat.article.ignore[res.dat.article.ignore$true.beta==0,]
res.dat.article.ignore.h0$prop.dif <- res.dat.article.ignore.h0$nb.dif/res.dat.article.ignore.h0$J
res.dat.article.ignore.h0.long <- reshape(res.dat.article.ignore.h0,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.ignore.h0.long) <- NULL
colnames(res.dat.article.ignore.h0.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.ignore.h0.long$prop.dif <- as.numeric(res.dat.article.ignore.h0.long$prop.dif)
res.dat.article.ignore.h0.long$N <- as.numeric(res.dat.article.ignore.h0.long$N)
res.dat.article.ignore.h0.long$true.gamma <- as.numeric(res.dat.article.ignore.h0.long$true.gamma)
res.dat.article.ignore.h0.long$J <- as.numeric(res.dat.article.ignore.h0.long$J)
res.dat.article.ignore.h0$true.gamma <- as.numeric(res.dat.article.ignore.h0$true.gamma)
# bias
res.dat.article.ignore.h0.long$abs.bias <- abs(res.dat.article.ignore.h0.long$bias)
summary(lm(abs.bias~true.gamma+prop.dif+N+J,data = res.dat.article.ignore.h0.long))
summary(lm(abs.bias~true.gamma+prop.dif,data = res.dat.article.ignore.h0.long))
summary(res.dat.article.ignore.h1.long[res.dat.article.ignore.h1.long$abs.gamma==0.5,"abs.bias"])
summary(res.dat.article.ignore.h1.long[res.dat.article.ignore.h1.long$abs.gamma==0.3 & res.dat.article.ignore.h1.long$prop.dif<0.3,"abs.bias"])
# type I
summary(lm(typeIerror~J+true.gamma+prop.dif+N,data = res.dat.article.ignore.h0.long))
summary(lm(typeIerror~true.gamma+prop.dif+N,data = res.dat.article.ignore.h0.long))
res.dat.article.ignore.h0.long$abs.gamma <- abs(res.dat.article.ignore.h0.long$true.gamma)
summary(lm(typeIerror~abs.gamma+prop.dif+N,data = res.dat.article.ignore.h0.long))
res.dat.article.ignore.h0[res.dat.article.ignore.h0$true.gamma==-0.5 &
res.dat.article.ignore.h0$prop.dif>0.3,]$typeIerror.300
res.dat.article.ignore.h0[res.dat.article.ignore.h0$true.gamma==-0.3 &
res.dat.article.ignore.h0$prop.dif<0.3,]$typeIerror.50
# coverage
summary(lm(coverage~abs.gamma+prop.dif+N,data = res.dat.article.ignore.h0.long))
####### SCENARIOS AVEC TE
res.dat.article.ignore.h1 <- res.dat.article.ignore[res.dat.article.ignore$true.beta!=0,]
res.dat.article.ignore.h1$prop.dif <- res.dat.article.ignore.h1$nb.dif/res.dat.article.ignore.h1$J
res.dat.article.ignore.h1.long <- reshape(res.dat.article.ignore.h1,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.ignore.h1.long) <- NULL
colnames(res.dat.article.ignore.h1.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.ignore.h1.long$prop.dif <- as.numeric(res.dat.article.ignore.h1.long$prop.dif)
res.dat.article.ignore.h1.long$N <- as.numeric(res.dat.article.ignore.h1.long$N)
res.dat.article.ignore.h1.long$true.gamma <- as.numeric(res.dat.article.ignore.h1.long$true.gamma)
res.dat.article.ignore.h1.long$J <- as.numeric(res.dat.article.ignore.h1.long$J)
res.dat.article.ignore.h1$true.gamma <- as.numeric(res.dat.article.ignore.h1$true.gamma)
# bias
res.dat.article.ignore.h1.long$abs.bias <- abs(res.dat.article.ignore.h1.long$bias)
res.dat.article.ignore.h1.long$abs.gamma <- abs(res.dat.article.ignore.h1.long$true.gamma)
res.dat.article.ignore.h1.long$sign.gamma <- sign(res.dat.article.ignore.h1.long$true.gamma)
summary(lm(abs.bias~abs.gamma+sign.gamma+prop.dif+true.beta+N,data = res.dat.article.ignore.h1.long))
summary(lm(abs.bias~abs.gamma+prop.dif,data = res.dat.article.ignore.h1.long))
summary(res.dat.article.ignore.h1.long$abs.bias)
# coverage
res.dat.article.ignore.h1.long$masks <- res.dat.article.ignore.h1.long$true.beta/res.dat.article.ignore.h1.long$true.gamma>0
res.dat.article.ignore.h1.long$masks <- 1*res.dat.article.ignore.h1.long$masks
summary(lm(coverage~abs.gamma+prop.dif+true.beta+N+masks,data = res.dat.article.ignore.h1.long))
summary(lm(coverage~abs.gamma+prop.dif+N+masks,data = res.dat.article.ignore.h1.long))
summary(lm(coverage~abs.gamma+prop.dif+N,data = res.dat.article.ignore.h1.long))
summary(res.dat.article.ignore.h1.long$coverage)
# power
res.dat.article.ignore.h1.long$powerdif <- as.numeric(res.dat.article.ignore.h1.long$power)-as.numeric(res.dat.article.ignore.h1.long$theoretical.power)
summary(lm(powerdif~masks*prop.dif+true.beta+N+masks*abs.gamma,data = res.dat.article.ignore.h1.long))
summary(res.dat.article.ignore.h1.long[res.dat.article.ignore.h1.long$masks==0,]$powerdif)
summary(res.dat.article.ignore.h1.long[res.dat.article.ignore.h1.long$masks==1,]$powerdif)
# bias +-
summary(lm(bias~abs.gamma+prop.dif+true.beta+N+masks,data = res.dat.article.ignore.h1.long))
summary(lm(bias~masks,data = res.dat.article.ignore.h1.long))
summary(res.dat.article.ignore.h1.long[res.dat.article.ignore.h1.long$masks==1,]$bias)
summary(res.dat.article.ignore.h1.long[res.dat.article.ignore.h1.long$masks==0,]$bias)
##########################
# DESCRIPTION PCM-DIF
##########################
####### SCENARIOS SANS TE
res.dat.article.dif.h0 <- res.dat.article.dif[res.dat.article.dif$true.beta==0,]
res.dat.article.dif.h0$prop.dif <- res.dat.article.dif.h0$nb.dif/res.dat.article.dif.h0$J
res.dat.article.dif.h0.long <- reshape(res.dat.article.dif.h0,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.dif.h0.long) <- NULL
colnames(res.dat.article.dif.h0.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.dif.h0.long$prop.dif <- as.numeric(res.dat.article.dif.h0.long$prop.dif)
res.dat.article.dif.h0.long$N <- as.numeric(res.dat.article.dif.h0.long$N)
res.dat.article.dif.h0.long$true.gamma <- as.numeric(res.dat.article.dif.h0.long$true.gamma)
res.dat.article.dif.h0.long$J <- as.numeric(res.dat.article.dif.h0.long$J)
res.dat.article.dif.h0$true.gamma <- as.numeric(res.dat.article.dif.h0$true.gamma)
# typeI
summary(as.numeric(res.dat.article.dif.h0.long$typeIerror))
# bias
summary(abs(as.numeric(res.dat.article.dif.h0.long$bias)))
# coverage
summary(as.numeric(res.dat.article.dif.h0.long$coverage))
####### SCENARIOS AVEC TE
res.dat.article.dif.h1 <- res.dat.article.dif[res.dat.article.dif$true.beta!=0,]
res.dat.article.dif.h1$prop.dif <- res.dat.article.dif.h1$nb.dif/res.dat.article.dif.h1$J
res.dat.article.dif.h1.long <- reshape(res.dat.article.dif.h1,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.dif.h1.long) <- NULL
colnames(res.dat.article.dif.h1.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.dif.h1.long$prop.dif <- as.numeric(res.dat.article.dif.h1.long$prop.dif)
res.dat.article.dif.h1.long$N <- as.numeric(res.dat.article.dif.h1.long$N)
res.dat.article.dif.h1.long$true.gamma <- as.numeric(res.dat.article.dif.h1.long$true.gamma)
res.dat.article.dif.h1.long$J <- as.numeric(res.dat.article.dif.h1.long$J)
res.dat.article.dif.h1$true.gamma <- as.numeric(res.dat.article.dif.h1$true.gamma)
res.dat.article.dif.h1.long$powerdif <- as.numeric(res.dat.article.dif.h1.long$power)-as.numeric(res.dat.article.dif.h1.long$theoretical.power)
# powerdif
summary(as.numeric(res.dat.article.dif.h1.long$powerdif))
summary(lm(powerdif~1,data=res.dat.article.dif.h1.long))
# bias
summary(abs(as.numeric(res.dat.article.dif.h1.long$bias)))
# coverage
summary(as.numeric(res.dat.article.dif.h1.long$coverage))
##########################
# LM FACTEURS ROSALI
##########################
####### SCENARIOS SANS TE
res.dat.article.rosali.dif.h0 <- res.dat.article.rosali.dif[res.dat.article.rosali.dif$true.beta==0,]
res.dat.article.rosali.dif.h0$prop.dif <- res.dat.article.rosali.dif.h0$nb.dif/res.dat.article.rosali.dif.h0$J
res.dat.article.rosali.dif.h0.long <- reshape(res.dat.article.rosali.dif.h0,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.rosali.dif.h0.long) <- NULL
colnames(res.dat.article.rosali.dif.h0.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.rosali.dif.h0.long$prop.dif <- as.numeric(res.dat.article.rosali.dif.h0.long$prop.dif)
res.dat.article.rosali.dif.h0.long$N <- as.numeric(res.dat.article.rosali.dif.h0.long$N)
res.dat.article.rosali.dif.h0.long$true.gamma <- as.numeric(res.dat.article.rosali.dif.h0.long$true.gamma)
res.dat.article.rosali.dif.h0.long$J <- as.numeric(res.dat.article.rosali.dif.h0.long$J)
res.dat.article.rosali.dif.h0$true.gamma <- as.numeric(res.dat.article.rosali.dif.h0$true.gamma)
# bias
res.dat.article.rosali.dif.h0.long$abs.gamma <- abs(res.dat.article.rosali.dif.h0.long$true.gamma)
res.dat.article.rosali.dif.h0.long$abs.bias <- abs(res.dat.article.rosali.dif.h0.long$bias)
summary(lm(abs.bias~abs.gamma+prop.dif+N+J,data = res.dat.article.rosali.dif.h0.long))
summary(lm(abs.bias~abs.gamma+prop.dif+N,data = res.dat.article.rosali.dif.h0.long))
summary(res.dat.article.rosali.dif.h0.long$abs.bias)
# type I
summary(lm(typeIerror~abs.gamma+prop.dif+N+J,data = res.dat.article.rosali.dif.h0.long))
summary(lm(typeIerror~abs.gamma+prop.dif+N,data = res.dat.article.rosali.dif.h0.long))
summary(as.numeric(res.dat.article.rosali.dif.h0.long$typeIerror))
# coverage
summary(lm(coverage~abs.gamma+prop.dif+N,data = res.dat.article.ignore.h0.long))
####### SCENARIOS AVEC TE
res.dat.article.rosali.dif.h1 <- res.dat.article.rosali.dif[res.dat.article.rosali.dif$true.beta!=0,]
res.dat.article.rosali.dif.h1$prop.dif <- res.dat.article.rosali.dif.h1$nb.dif/res.dat.article.rosali.dif.h1$J
res.dat.article.rosali.dif.h1.long <- reshape(res.dat.article.rosali.dif.h1,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.rosali.dif.h1.long) <- NULL
colnames(res.dat.article.rosali.dif.h1.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.rosali.dif.h1.long$prop.dif <- as.numeric(res.dat.article.rosali.dif.h1.long$prop.dif)
res.dat.article.rosali.dif.h1.long$N <- as.numeric(res.dat.article.rosali.dif.h1.long$N)
res.dat.article.rosali.dif.h1.long$true.gamma <- as.numeric(res.dat.article.rosali.dif.h1.long$true.gamma)
res.dat.article.rosali.dif.h1.long$J <- as.numeric(res.dat.article.rosali.dif.h1.long$J)
res.dat.article.rosali.dif.h1$true.gamma <- as.numeric(res.dat.article.rosali.dif.h1$true.gamma)
# bias
res.dat.article.rosali.dif.h1.long$abs.bias <- abs(res.dat.article.rosali.dif.h1.long$bias)
res.dat.article.rosali.dif.h1.long$abs.gamma <- abs(res.dat.article.rosali.dif.h1.long$true.gamma)
res.dat.article.rosali.dif.h1.long$sign.gamma <- sign(res.dat.article.rosali.dif.h1.long$true.gamma)
summary(lm(abs.bias~abs.gamma+sign.gamma+prop.dif+true.beta+N+J,data = res.dat.article.rosali.dif.h1.long))
summary(lm(abs.bias~abs.gamma+prop.dif+N,data = res.dat.article.rosali.dif.h1.long))
summary(res.dat.article.rosali.dif.h1.long$abs.bias)
# coverage
res.dat.article.rosali.dif.h1.long$masks <- res.dat.article.rosali.dif.h1.long$true.beta/res.dat.article.rosali.dif.h1.long$true.gamma>0
res.dat.article.rosali.dif.h1.long$masks <- 1*res.dat.article.rosali.dif.h1.long$masks
summary(lm(coverage~abs.gamma+prop.dif+true.beta+N+masks+J,data = res.dat.article.rosali.dif.h1.long))
summary(lm(coverage~abs.gamma+prop.dif+N,data = res.dat.article.rosali.dif.h1.long))
summary(res.dat.article.rosali.dif.h1.long$coverage)
# power
res.dat.article.rosali.dif.h1.long$powerdif <- as.numeric(res.dat.article.rosali.dif.h1.long$power)-as.numeric(res.dat.article.rosali.dif.h1.long$theoretical.power)
summary(lm(powerdif~masks+true.beta+N,data = res.dat.article.rosali.dif.h1.long))
summary(lm(abs(powerdif)~N+prop.dif,data = res.dat.article.rosali.dif.h1.long))
summary(res.dat.article.rosali.dif.h1.long[res.dat.article.rosali.dif.h1.long$masks==0,]$powerdif)
summary(res.dat.article.rosali.dif.h1.long[res.dat.article.rosali.dif.h1.long$masks==1,]$powerdif)
# bias +-
summary(lm(bias~abs.gamma+prop.dif+true.beta+N+masks,data = res.dat.article.rosali.dif.h1.long))
summary(lm(bias~masks,data = res.dat.article.rosali.dif.h1.long))
summary(res.dat.article.rosali.dif.h1.long[res.dat.article.rosali.dif.h1.long$masks==1,]$bias)
summary(res.dat.article.rosali.dif.h1.long[res.dat.article.rosali.dif.h1.long$masks==0,]$bias)
##########################
# Plots ROSALI vs ignore
##########################
par(mfrow=c(2,2))
plot(res.dat.article.ignore.h0.long$typeIerror,res.dat.article.rosali.dif.h0.long$typeIerror,
pch=3,col="#CD5E35",xlim=c(0,1),ylim=c(0,1),cex=1.5,
xlab = "Type-I error when ignoring DIF",ylab="Type-I error after ROSALI DIF detection",
main="Type-I error",axes = F)
segments(x0=0,y0=0,x1=1,y1=1,lty=2)
axis(1)
axis(2)
plot(c(res.dat.article.ignore.h0.long$abs.bias,res.dat.article.ignore.h1.long$abs.bias),
c(res.dat.article.rosali.dif.h0.long$abs.bias,res.dat.article.rosali.dif.h1.long$abs.bias),
pch=3,col="#CD5E35",xlim=c(0,0.4),ylim=c(0,0.4),cex=1.5,
xlab = "Absolute bias when ignoring DIF",ylab="Absolute bias after ROSALI DIF detection",
main="Absolute bias",axes = F)
segments(x0=0,y0=0,x1=0.4,y1=0.4,lty=2)
axis(1)
axis(2)
plot(c(res.dat.article.ignore.h0.long$coverage,res.dat.article.ignore.h1.long$coverage),
c(res.dat.article.rosali.dif.h0.long$coverage,res.dat.article.rosali.dif.h1.long$coverage),
pch=3,col="#CD5E35",xlim=c(0,1),ylim=c(0,1),cex=1.5,
xlab = "Coverage when ignoring DIF",ylab="Coverage after ROSALI DIF detection",
main="Coverage",axes = F)
segments(x0=0,y0=0,x1=1,y1=1,lty=2)
axis(1)
axis(2)
plot(c(res.dat.article.ignore.h0.long$powerdif,res.dat.article.ignore.h1.long$powerdif),
c(res.dat.article.rosali.dif.h0.long$powerdif,res.dat.article.rosali.dif.h1.long$powerdif),
pch=3,col="#CD5E35",xlim=c(-1,1),ylim=c(-1,1),cex=1.5,
xlab = "Power difference when ignoring DIF",ylab="Power difference after ROSALI DIF detection",
main="Difference between expected and observed power",axes = F)
segments(x0=-1,y0=-1,x1=1,y1=1,lty=2)
axis(1)
axis(2)
par(mfrow=c(1,1))
which.typeI <- which(as.numeric(res.dat.article.rosali.dif.h0.long$typeIerror)-as.numeric(res.dat.article.ignore.h0.long$typeIerror)<=-0.1)
res.dat.article.ignore.h0.long[which.typeI,]
df_temp_rosali <- rbind(res.dat.article.rosali.dif.h0.long[,c(1:6,13)],res.dat.article.rosali.dif.h1.long[,c(1:6,13)])
df_temp_ignore <- rbind(res.dat.article.ignore.h0.long[,c(1:6,13)],res.dat.article.ignore.h1.long[,c(1:6,13)])
which.bias <- which(as.numeric(df_temp_rosali$abs.bias)-as.numeric(df_temp_ignore$abs.bias)<=-0.05)
df_temp_rosali[which.bias,]
df_temp_rosali <- rbind(res.dat.article.rosali.dif.h0.long[,c(1:6,12)],res.dat.article.rosali.dif.h1.long[,c(1:6,12)])
df_temp_ignore <- rbind(res.dat.article.ignore.h0.long[,c(1:6,12)],res.dat.article.ignore.h1.long[,c(1:6,12)])
which.coverage <- which(as.numeric(df_temp_rosali$coverage)-as.numeric(df_temp_ignore$coverage)>=0.1)
df_temp_rosali[which.coverage,]
df_temp_rosali <- rbind(res.dat.article.rosali.dif.h1.long)
df_temp_ignore <- rbind(res.dat.article.ignore.h1.long)
which.power <- which(abs(as.numeric(df_temp_rosali$powerdif))-abs(as.numeric(df_temp_ignore$powerdif))<=-0.1)
df_temp_rosali[which.power,]
##########################
# LM FACTEURS RESIDIF
##########################
####### SCENARIOS SANS TE
res.dat.article.residif.dif.h0 <- res.dat.article.residif.dif[res.dat.article.residif.dif$true.beta==0,]
res.dat.article.residif.dif.h0$prop.dif <- res.dat.article.residif.dif.h0$nb.dif/res.dat.article.residif.dif.h0$J
res.dat.article.residif.dif.h0.long <- reshape(res.dat.article.residif.dif.h0,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.residif.dif.h0.long) <- NULL
colnames(res.dat.article.residif.dif.h0.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.residif.dif.h0.long$prop.dif <- as.numeric(res.dat.article.residif.dif.h0.long$prop.dif)
res.dat.article.residif.dif.h0.long$N <- as.numeric(res.dat.article.residif.dif.h0.long$N)
res.dat.article.residif.dif.h0.long$true.gamma <- as.numeric(res.dat.article.residif.dif.h0.long$true.gamma)
res.dat.article.residif.dif.h0.long$J <- as.numeric(res.dat.article.residif.dif.h0.long$J)
res.dat.article.residif.dif.h0$true.gamma <- as.numeric(res.dat.article.residif.dif.h0$true.gamma)
# bias
res.dat.article.residif.dif.h0.long$abs.gamma <- abs(res.dat.article.residif.dif.h0.long$true.gamma)
res.dat.article.residif.dif.h0.long$abs.bias <- abs(res.dat.article.residif.dif.h0.long$bias)
summary(lm(abs.bias~abs.gamma+prop.dif+N+J,data = res.dat.article.residif.dif.h0.long))
summary(lm(abs.bias~abs.gamma+prop.dif+N,data = res.dat.article.residif.dif.h0.long))
summary(res.dat.article.residif.dif.h0.long$abs.bias)
# type I
summary(lm(typeIerror~abs.gamma+prop.dif+N+J,data = res.dat.article.residif.dif.h0.long))
summary(lm(typeIerror~abs.gamma+prop.dif+N,data = res.dat.article.residif.dif.h0.long))
summary(as.numeric(res.dat.article.residif.dif.h0.long$typeIerror))
# coverage
summary(lm(coverage~abs.gamma+prop.dif+N,data = res.dat.article.ignore.h0.long))
####### SCENARIOS AVEC TE
res.dat.article.residif.dif.h1 <- res.dat.article.residif.dif[res.dat.article.residif.dif$true.beta!=0,]
res.dat.article.residif.dif.h1$prop.dif <- res.dat.article.residif.dif.h1$nb.dif/res.dat.article.residif.dif.h1$J
res.dat.article.residif.dif.h1.long <- reshape(res.dat.article.residif.dif.h1,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.residif.dif.h1.long) <- NULL
colnames(res.dat.article.residif.dif.h1.long)[7:12] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.residif.dif.h1.long$prop.dif <- as.numeric(res.dat.article.residif.dif.h1.long$prop.dif)
res.dat.article.residif.dif.h1.long$N <- as.numeric(res.dat.article.residif.dif.h1.long$N)
res.dat.article.residif.dif.h1.long$true.gamma <- as.numeric(res.dat.article.residif.dif.h1.long$true.gamma)
res.dat.article.residif.dif.h1.long$J <- as.numeric(res.dat.article.residif.dif.h1.long$J)
res.dat.article.residif.dif.h1$true.gamma <- as.numeric(res.dat.article.residif.dif.h1$true.gamma)
# bias
res.dat.article.residif.dif.h1.long$abs.bias <- abs(res.dat.article.residif.dif.h1.long$bias)
res.dat.article.residif.dif.h1.long$abs.gamma <- abs(res.dat.article.residif.dif.h1.long$true.gamma)
res.dat.article.residif.dif.h1.long$sign.gamma <- sign(res.dat.article.residif.dif.h1.long$true.gamma)
res.dat.article.residif.dif.h1.long$masks <- res.dat.article.residif.dif.h1.long$true.beta/res.dat.article.residif.dif.h1.long$true.gamma>0
res.dat.article.residif.dif.h1.long$masks <- 1*res.dat.article.residif.dif.h1.long$masks
summary(lm(abs.bias~abs.gamma+masks+prop.dif+true.beta+N+J,data = res.dat.article.residif.dif.h1.long))
summary(lm(abs.bias~abs.gamma+masks+prop.dif+N,data = res.dat.article.residif.dif.h1.long))
summary(res.dat.article.residif.dif.h1.long$abs.bias)
summary(res.dat.article.residif.dif.h1.long[res.dat.article.residif.dif.h1.long$masks==1,]$abs.bias)
summary(res.dat.article.residif.dif.h1.long[res.dat.article.residif.dif.h1.long$masks==0,]$abs.bias)
res.dat.article.rosali.dif.h1.long$masks <- 1*(res.dat.article.rosali.dif.h1.long$true.gamma>0)
res.dat.article.rosali.dif.h1.long$abs.bias <- abs(as.numeric(res.dat.article.rosali.dif.h1.long$bias))
summary(res.dat.article.rosali.dif.h1.long[res.dat.article.rosali.dif.h1.long$masks==1,]$abs.bias)
summary(res.dat.article.rosali.dif.h1.long[res.dat.article.rosali.dif.h1.long$masks==0,]$abs.bias)
# coverage
summary(lm(coverage~abs.gamma+prop.dif+true.beta+N+masks+J,data = res.dat.article.residif.dif.h1.long))
summary(lm(coverage~abs.gamma+prop.dif+N+masks,data = res.dat.article.residif.dif.h1.long))
summary(res.dat.article.residif.dif.h1.long$coverage)
# power
res.dat.article.residif.dif.h1.long$powerdif <- as.numeric(res.dat.article.residif.dif.h1.long$power)-as.numeric(res.dat.article.residif.dif.h1.long$theoretical.power)
summary(lm(powerdif~masks+true.beta+N+abs.gamma+prop.dif+J,data = res.dat.article.residif.dif.h1.long))
summary(lm(powerdif~masks+true.beta+N,data = res.dat.article.residif.dif.h1.long))
summary(res.dat.article.residif.dif.h1.long[res.dat.article.residif.dif.h1.long$masks==0,]$powerdif)
summary(res.dat.article.residif.dif.h1.long[res.dat.article.residif.dif.h1.long$masks==1,]$powerdif)
# bias +-
summary(lm(bias~abs.gamma+prop.dif+true.beta+N+masks,data = res.dat.article.residif.dif.h1.long))
summary(lm(bias~masks,data = res.dat.article.residif.dif.h1.long))
summary(res.dat.article.residif.dif.h1.long[res.dat.article.residif.dif.h1.long$masks==1,]$bias)
summary(res.dat.article.residif.dif.h1.long[res.dat.article.residif.dif.h1.long$masks==0,]$bias)
##########################
# Plots RESIDIF vs ignore
##########################
par(mfrow=c(2,2))
plot(res.dat.article.ignore.h0.long$typeIerror,res.dat.article.residif.dif.h0.long$typeIerror,
pch=3,col="#CD5E35",xlim=c(0,1),ylim=c(0,1),cex=1.5,
xlab = "Type-I error when ignoring DIF",ylab="Type-I error after RESIDIF DIF detection",
main="Type-I error",axes = F)
segments(x0=0,y0=0,x1=1,y1=1,lty=2)
axis(1)
axis(2)
plot(c(res.dat.article.ignore.h0.long$abs.bias,res.dat.article.ignore.h1.long$abs.bias),
c(res.dat.article.residif.dif.h0.long$abs.bias,res.dat.article.residif.dif.h1.long$abs.bias),
pch=3,col="#CD5E35",xlim=c(0,0.4),ylim=c(0,0.4),cex=1.5,
xlab = "Absolute bias when ignoring DIF",ylab="Absolute bias after RESIDIF DIF detection",
main="Absolute bias",axes = F)
segments(x0=0,y0=0,x1=0.4,y1=0.4,lty=2)
axis(1)
axis(2)
plot(c(res.dat.article.ignore.h0.long$coverage,res.dat.article.ignore.h1.long$coverage),
c(res.dat.article.residif.dif.h0.long$coverage,res.dat.article.residif.dif.h1.long$coverage),
pch=3,col="#CD5E35",xlim=c(0,1),ylim=c(0,1),cex=1.5,
xlab = "Coverage when ignoring DIF",ylab="Coverage after RESIDIF DIF detection",
main="Coverage",axes = F)
segments(x0=0,y0=0,x1=1,y1=1,lty=2)
axis(1)
axis(2)
plot(c(res.dat.article.ignore.h0.long$powerdif,res.dat.article.ignore.h1.long$powerdif),
c(res.dat.article.residif.dif.h0.long$powerdif,res.dat.article.residif.dif.h1.long$powerdif),
pch=3,col="#CD5E35",xlim=c(-1,1),ylim=c(-1,1),cex=1.5,
xlab = "Power difference when ignoring DIF",ylab="Power difference after RESIDIF DIF detection",
main="Difference between expected and observed power",axes = F)
segments(x0=-1,y0=-1,x1=1,y1=1,lty=2)
axis(1)
axis(2)
par(mfrow=c(1,1))
which.typeI <- which(as.numeric(res.dat.article.residif.dif.h0.long$typeIerror)-as.numeric(res.dat.article.ignore.h0.long$typeIerror)<=-0.1)
res.dat.article.ignore.h0.long[which.typeI,]
res.dat.article.residif.dif.h0.long$masks <- NA
res.dat.article.ignore.h0.long$masks <- NA
df_temp_residif <- rbind(res.dat.article.residif.dif.h0.long[,c(1:6,14:15)],res.dat.article.residif.dif.h1.long[,c(1:6,13,16)])
df_temp_ignore <- rbind(res.dat.article.ignore.h0.long[,c(1:6,13,15)],res.dat.article.ignore.h1.long[,c(1:6,13,16)])
which.bias <- which(as.numeric(df_temp_residif$abs.bias)-as.numeric(df_temp_ignore$abs.bias)<=-0.05)
df_temp_residif[which.bias,]
table(df_temp_residif[which.bias,]$masks)
which.bias <- which(as.numeric(df_temp_residif$abs.bias)-as.numeric(df_temp_ignore$abs.bias)>0.05)
df_temp_residif[which.bias,]
df_temp_residif <- rbind(res.dat.article.residif.dif.h0.long[,c(1:6,12,15)],res.dat.article.residif.dif.h1.long[,c(1:6,12,16)])
df_temp_ignore <- rbind(res.dat.article.ignore.h0.long[,c(1:6,12,15)],res.dat.article.ignore.h1.long[,c(1:6,12,16)])
which.bias <- which(as.numeric(df_temp_residif$coverage)-as.numeric(df_temp_ignore$coverage)<=-0.1)
df_temp_residif[which.coverage,]
table(df_temp_residif[which.coverage,]$masks)
df_temp_residif <- rbind(res.dat.article.residif.dif.h1.long)
df_temp_ignore <- rbind(res.dat.article.ignore.h1.long)
which.power <- which(abs(as.numeric(df_temp_residif$powerdif))-abs(as.numeric(df_temp_ignore$powerdif))<=-0.1)
df_temp_residif[which.power,]
table(df_temp_residif[which.power,]$masks)
##########################
# BOXPLOT PERF VS NODIF
##########################
par(mfrow=c(2,2))
par(bg = "white")
res.dat.article.nodif <- res.dat.article[res.dat$nb.dif==0,]
### Type I error
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",120),
rep("ROSALI",120),
rep("RESIDIF",120),
rep("PCM-DIF",120) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab="Analysis strategy",pch=3,main="Type-I error rate (TE = 0)",
ylab="RCT type-I error",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
### BIAS
bp.dat.bias.ignore <- as.numeric(res.dat.article[res.dat.article$nb.dif!=0,"bias"])
bp.dat.bias.nodif <- as.numeric(res.dat.article.nodif[,"bias"])
bp.dat.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$nb.dif!=0,"bias"])
bp.dat.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$nb.dif!=0,"bias"])
bp.dat.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$nb.dif!=0,"bias"])
bp.dat.bias <- data.frame(bias=c(bp.dat.bias.nodif,bp.dat.bias.ignore,bp.dat.bias.rosali,bp.dat.bias.residif,
bp.dat.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",120),
rep("ROSALI",120),
rep("RESIDIF",120),
rep("PCM-DIF",120) ) )
bp.dat.bias$method <- factor(bp.dat.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
bp.dat.bias$bias <- abs(bp.dat.bias$bias)
boxplot(bp.dat.bias$bias~bp.dat.bias$method,xlab="Analysis strategy",pch=3,main="Absolute bias",
ylab="Absolute bias",ylim=c(0,.5),yaxt="n",
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"),
cex.lab=1.45,cex.main=1.5,cex.axis=1.45)
axis(2,seq(-.5,.5,0.25),cex.axis=1.45)
abline(h=0,lty=2,col='#777777',lwd=2)
### COVERAGE
bp.dat.coverage.ignore <- as.numeric(res.dat.article[res.dat.article$nb.dif!=0,"coverage"])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",120),
rep("ROSALI",120),
rep("RESIDIF",120),
rep("PCM-DIF",120) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab="Analysis strategy",pch=3,main="Coverage",
ylab="Coverage",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(-1,1,0.25),cex=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
### POWER
bp.dat.power.ignore.mask <- as.numeric(res.dat.article[res.dat.article$true.gamma>0 & res.dat.article$true.beta>0 & res.dat.article$nb.dif!=0,"power"])-as.numeric(res.dat.article[res.dat.article$true.gamma>0 & res.dat.article$true.beta>0 & res.dat.article$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article[res.dat.article$true.gamma<0 & res.dat.article$true.beta>0 & res.dat.article$nb.dif!=0,"power"])-as.numeric(res.dat.article[res.dat.article$true.gamma<0 & res.dat.article$true.beta>0 & res.dat.article$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif[res.dat.article.nodif$true.beta>0,"power"])-as.numeric(res.dat.article.nodif[res.dat.article.nodif$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",48),rep("AMPLIFY1",48),
rep("MASK2",48),rep("AMPLIFY2",48),
rep("MASK3",48),rep("AMPLIFY3",48),
rep("MASK4",48),rep("AMPLIFY4",48) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="Power (TE ≠ 0)",
ylab="RCT power - expected power",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
title(cex.lab=1.45,xlab="Analysis strategy")
# legend("topright",fill = c("#798A5D","#D4E8B5"),c('DIF masks treatment effect','DIF amplifies treatment effect'))
par(mfrow=c(1,1))
par(bg = "white")
Final code before cleaning
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##########################
Update to article code
2 months ago
# DESCRIPTION NO DIF
Final code before cleaning
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##########################
Update to article code
2 months ago
####### SCENARIOS SANS TE
res.dat.article.nodif.long.h0 <- res.dat.article.nodif.long[res.dat.article.nodif.long$true.beta==0,]
res.dat.article.nodif.long.h0$prop.dif <- res.dat.article.nodif.long.h0$nb.dif/res.dat.article.nodif.long.h0$J
res.dat.article.nodif.long.h0$prop.dif <- as.numeric(res.dat.article.nodif.long.h0$prop.dif)
res.dat.article.nodif.long.h0$N <- as.numeric(res.dat.article.nodif.long.h0$N)
res.dat.article.nodif.long.h0$true.gamma <- as.numeric(res.dat.article.nodif.long.h0$true.gamma)
res.dat.article.nodif.long.h0$J <- as.numeric(res.dat.article.nodif.long.h0$J)
res.dat.article.nodif.long.h0$true.gamma <- as.numeric(res.dat.article.nodif.long.h0$true.gamma)
# typeI
summary(as.numeric(res.dat.article.dif.h0.long$typeIerror))
####### SCENARIOS AVEC TE
res.dat.article.nodif.long.h1 <- res.dat.article.nodif.long[res.dat.article.nodif.long$true.beta!=0,]
res.dat.article.nodif.long.h1$prop.dif <- res.dat.article.nodif.long.h1$nb.dif/res.dat.article.nodif.long.h1$J
Final code before cleaning
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Update to article code
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res.dat.article.nodif.long.h1$prop.dif <- as.numeric(res.dat.article.nodif.long.h1$prop.dif)
res.dat.article.nodif.long.h1$N <- as.numeric(res.dat.article.nodif.long.h1$N)
res.dat.article.nodif.long.h1$true.gamma <- as.numeric(res.dat.article.nodif.long.h1$true.gamma)
res.dat.article.nodif.long.h1$J <- as.numeric(res.dat.article.nodif.long.h1$J)
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Update to article code
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res.dat.article.nodif.long.h1$true.gamma <- as.numeric(res.dat.article.nodif.long.h1$true.gamma)
res.dat.article.nodif.long.h1$powerdif <- as.numeric(res.dat.article.nodif.long.h1$power)-as.numeric(res.dat.article.nodif.long.h1$theoretical.power)
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# powerdif
summary(as.numeric(res.dat.article.nodif.long.h1$powerdif))
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####### Overall
Final code before cleaning
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#coverage
res.dat.article.nodif.long$bias <- as.numeric(res.dat.article.nodif.long$bias)
res.dat.article.nodif.long$abs.bias <- abs(res.dat.article.nodif.long$bias)
summary(res.dat.article.nodif.long$abs.bias)
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#coverage
summary(res.dat.article.nodif.long$coverage)
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####### ROSALI
Final code before cleaning
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# SCENARIOS SANS TE
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# typeI
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summary(as.numeric(res.dat.article.rosali.2.nodif$typeIerror))
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res.dat.article.rosali.2.nodif$typeIerror <- as.numeric(res.dat.article.rosali.2.nodif$typeIerror)
res.dat.article.rosali.2.nodif$N <- as.numeric(res.dat.article.rosali.2.nodif$N)
summary(lm(typeIerror~N+J,data = res.dat.article.rosali.2.nodif))
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# bias
res.dat.article.rosali.2.nodif$abs.bias <- abs(as.numeric(res.dat.article.rosali.2.nodif$bias))
summary(as.numeric(res.dat.article.rosali.2.nodif[res.dat.article.rosali.2.nodif$true.beta==0,]$abs.bias))
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# SCENARIOS AVEC TE
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# bias
summary(as.numeric(res.dat.article.rosali.2.nodif[res.dat.article.rosali.2.nodif$true.beta!=0,]$abs.bias))
Final code before cleaning
7 months ago
Update to article code
2 months ago
# coverage
res.dat.article.rosali.2.nodif$coverage <- as.numeric(res.dat.article.rosali.2.nodif$coverage)
summary(as.numeric(res.dat.article.rosali.2.nodif[res.dat.article.rosali.2.nodif$true.beta!=0,]$coverage))
Final code before cleaning
7 months ago
Update to article code
2 months ago
# power
res.dat.article.rosali.2.nodif$powerdif <- as.numeric(res.dat.article.rosali.2.nodif$power)-as.numeric(res.dat.article.rosali.2.nodif$theoretical.power)
summary(as.numeric(res.dat.article.rosali.2.nodif[res.dat.article.rosali.2.nodif$true.beta!=0,]$powerdif))
Final code before cleaning
7 months ago
Update to article code
2 months ago
####### RESIDIF
Final code before cleaning
7 months ago
Update to article code
2 months ago
# SCENARIOS SANS TE
# typeI
summary(as.numeric(res.dat.article.residif.2.nodif$typeIerror))
res.dat.article.residif.2.nodif$typeIerror <- as.numeric(res.dat.article.residif.2.nodif$typeIerror)
res.dat.article.residif.2.nodif$N <- as.numeric(res.dat.article.residif.2.nodif$N)
summary(lm(typeIerror~N+J,data = res.dat.article.residif.2.nodif))
# bias
res.dat.article.residif.2.nodif$abs.bias <- abs(as.numeric(res.dat.article.residif.2.nodif$bias))
summary(as.numeric(res.dat.article.residif.2.nodif[res.dat.article.residif.2.nodif$true.beta==0,]$abs.bias))
# SCENARIOS AVEC TE
# bias
summary(as.numeric(res.dat.article.residif.2.nodif[res.dat.article.residif.2.nodif$true.beta!=0,]$abs.bias))
# coverage
res.dat.article.residif.2.nodif$coverage <- as.numeric(res.dat.article.residif.2.nodif$coverage)
summary(as.numeric(res.dat.article.residif.2.nodif[res.dat.article.residif.2.nodif$true.beta!=0,]$coverage))
# power
res.dat.article.residif.2.nodif$powerdif <- as.numeric(res.dat.article.residif.2.nodif$power)-as.numeric(res.dat.article.residif.2.nodif$theoretical.power)
summary(as.numeric(res.dat.article.residif.2.nodif[res.dat.article.residif.2.nodif$true.beta!=0,]$powerdif))
# Scenarios avec + perf
res.dat.article.ignore.long <- reshape(res.dat.article.ignore,idvar=c("J",'true.beta',"true.gamma","nb.dif","prop.dif"),v.names=c('betahat','bias','typeIerror','power',"coverage"))
rownames(res.dat.article.ignore.long) <- NULL
colnames(res.dat.article.ignore.long)[6:11] <- c("betahat","bias","typeIerror",'power','theoretical.power','coverage')
res.dat.article.ignore.long$abs.bias <- abs(res.dat.article.ignore.long$bias)
res.dat.article.residif.2$abs.bias <- abs(res.dat.article.residif.2$bias)
res.dat.article.residif.2.dif <- res.dat.article.residif.2[res.dat.article.residif.2$nb.dif>0,]
res.dat.article.rosali.2$abs.bias <- abs(res.dat.article.rosali.2$bias)
res.dat.article.rosali.2.dif <- res.dat.article.rosali.2[res.dat.article.rosali.2$nb.dif>0,]
res.dat.article.ignore.long.dif <- res.dat.article.ignore.long[res.dat.article.ignore.long$nb.dif>0,]
summary(res.dat.article.residif.2.dif[res.dat.article.residif.2.dif$abs.bias-res.dat.article.ignore.long.dif$abs.bias<=-0.1,]$abs.bias)
summary(res.dat.article.ignore.long.dif[res.dat.article.residif.2.dif$abs.bias-res.dat.article.ignore.long.dif$abs.bias<=-0.1,]$abs.bias)
summary(res.dat.article.rosali.2.dif[res.dat.article.rosali.2.dif$abs.bias-res.dat.article.ignore.long.dif$abs.bias<=-0.1,]$abs.bias)
summary(res.dat.article.ignore.long.dif[res.dat.article.rosali.2.dif$abs.bias-res.dat.article.ignore.long.dif$abs.bias<=-0.1,]$abs.bias)
##########################
# BOXPLOT PERF VS NODIF FACET TYPE1
##########################
Final code before cleaning
7 months ago
Update to article code
2 months ago
par(mfrow=c(3,4))
par(bg = "white")
res.dat.article.2$abs.gamma <- abs(as.numeric(res.dat.article.2$true.gamma))
res.dat.article.rosali.2$abs.gamma <- abs(as.numeric(res.dat.article.rosali.2$true.gamma))
res.dat.article.residif.2$abs.gamma <- abs(as.numeric(res.dat.article.residif.2$true.gamma))
res.dat.article.dif.2$abs.gamma <- abs(as.numeric(res.dat.article.dif.2$true.gamma))
res.dat.article.2$prop.dif <- res.dat.article.2$nb.dif/res.dat.article.2$J
res.dat.article.rosali.2$prop.dif <- res.dat.article.rosali.2$nb.dif/res.dat.article.rosali.2$J
res.dat.article.residif.2$prop.dif <- res.dat.article.residif.2$nb.dif/res.dat.article.residif.2$J
res.dat.article.dif.2$prop.dif <- res.dat.article.dif.2$nb.dif/res.dat.article.dif.2$J
############# Type I error
# N50
## DIF 03 25%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="Weak DIF - 25% of items",
ylab="Type-I error rate",ylim=c(0,1),yaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 03 50%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="Weak DIF - 50% of items",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 05 25%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="Medium DIF - 25% of items",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 05 50%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="Medium DIF - 50% of items",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
# N100
## DIF 03 25%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab="Type-I error rate",ylim=c(0,1),yaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 03 50%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 05 25%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 05 50%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
# N300
## DIF 03 25%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab="Type-I error rate",ylim=c(0,1),yaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 03 50%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 05 25%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
## DIF 05 50%
bp.dat.typeIerror.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("typeIerror")])
bp.dat.typeIerror.nodif <- as.numeric(res.dat.article.nodif.2[,"typeIerror"])
bp.dat.typeIerror.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"typeIerror"])
bp.dat.typeIerror <- data.frame(typeIerror=c(bp.dat.typeIerror.nodif,bp.dat.typeIerror.ignore,bp.dat.typeIerror.rosali,bp.dat.typeIerror.residif,
bp.dat.typeIerror.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.typeIerror$method <- factor(bp.dat.typeIerror$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.typeIerror$typeIerror~bp.dat.typeIerror$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.05,lty=2,col='#777777',lwd=2)
##########################
# BOXPLOT PERF VS NODIF FACET BIAS
##########################
par(mfrow=c(3,4))
par(bg = "white")
res.dat.article.2$abs.gamma <- abs(as.numeric(res.dat.article.2$true.gamma))
res.dat.article.rosali.2$abs.gamma <- abs(as.numeric(res.dat.article.rosali.2$true.gamma))
res.dat.article.residif.2$abs.gamma <- abs(as.numeric(res.dat.article.residif.2$true.gamma))
res.dat.article.dif.2$abs.gamma <- abs(as.numeric(res.dat.article.dif.2$true.gamma))
res.dat.article.2$prop.dif <- res.dat.article.2$nb.dif/res.dat.article.2$J
res.dat.article.rosali.2$prop.dif <- res.dat.article.rosali.2$nb.dif/res.dat.article.rosali.2$J
res.dat.article.residif.2$prop.dif <- res.dat.article.residif.2$nb.dif/res.dat.article.residif.2$J
res.dat.article.dif.2$prop.dif <- res.dat.article.dif.2$nb.dif/res.dat.article.dif.2$J
res.dat.article.2$abs.bias <- abs(as.numeric(res.dat.article.2$bias))
res.dat.article.rosali.2$abs.bias <- abs(as.numeric(res.dat.article.rosali.2$bias))
res.dat.article.residif.2$abs.bias <- abs(as.numeric(res.dat.article.residif.2$bias))
res.dat.article.dif.2$abs.bias <- abs(as.numeric(res.dat.article.dif.2$bias))
res.dat.article.nodif.2$abs.bias <- abs(as.numeric(res.dat.article.nodif.2$bias))
############# Bias
# N50
## DIF 03 25%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="Weak DIF - 25% of items",
ylab="Absolute bias",ylim=c(0,0.5),yaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 03 50%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="Weak DIF - 50% of items",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 05 25%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="Medium DIF - 25% of items",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 05 50%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="Medium DIF - 50% of items",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
# N100
## DIF 03 25%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab="Absolute bias",ylim=c(0,0.5),yaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 03 50%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 05 25%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 05 50%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
# N300
## DIF 03 25%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab="Absolute bias",ylim=c(0,0.5),yaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 03 50%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 05 25%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
## DIF 05 50%
bp.dat.abs.bias.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("abs.bias")])
bp.dat.abs.bias.nodif <- as.numeric(res.dat.article.nodif.2[,"abs.bias"])
bp.dat.abs.bias.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"abs.bias"])
bp.dat.abs.bias <- data.frame(abs.bias=c(bp.dat.abs.bias.nodif,bp.dat.abs.bias.ignore,bp.dat.abs.bias.rosali,bp.dat.abs.bias.residif,
bp.dat.abs.bias.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.abs.bias$method <- factor(bp.dat.abs.bias$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.abs.bias$abs.bias~bp.dat.abs.bias$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,0.5),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
##########################
# BOXPLOT PERF VS NODIF FACET COVERAGE
##########################
par(mfrow=c(3,4))
par(bg = "white")
res.dat.article.2$abs.gamma <- abs(as.numeric(res.dat.article.2$true.gamma))
res.dat.article.rosali.2$abs.gamma <- abs(as.numeric(res.dat.article.rosali.2$true.gamma))
res.dat.article.residif.2$abs.gamma <- abs(as.numeric(res.dat.article.residif.2$true.gamma))
res.dat.article.dif.2$abs.gamma <- abs(as.numeric(res.dat.article.dif.2$true.gamma))
res.dat.article.2$prop.dif <- res.dat.article.2$nb.dif/res.dat.article.2$J
res.dat.article.rosali.2$prop.dif <- res.dat.article.rosali.2$nb.dif/res.dat.article.rosali.2$J
res.dat.article.residif.2$prop.dif <- res.dat.article.residif.2$nb.dif/res.dat.article.residif.2$J
res.dat.article.dif.2$prop.dif <- res.dat.article.dif.2$nb.dif/res.dat.article.dif.2$J
############# Bias
# N50
## DIF 03 25%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="Weak DIF - 25% of items",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
title(ylab="Coverage",cex.lab=1.6)
## DIF 03 50%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="Weak DIF - 50% of items",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
## DIF 05 25%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="Medium DIF - 25% of items",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
## DIF 05 50%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="Medium DIF - 50% of items",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
# N100
## DIF 03 25%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab="",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
title(ylab="Coverage",cex.lab=1.6)
## DIF 03 50%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
## DIF 05 25%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
## DIF 05 50%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
# N300
## DIF 03 25%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
title(ylab="Coverage",cex.lab=1.6)
## DIF 03 50%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
## DIF 05 25%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
## DIF 05 50%
bp.dat.coverage.ignore <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif>0.3 & res.dat.article.2$nb.dif!=0,c("coverage")])
bp.dat.coverage.nodif <- as.numeric(res.dat.article.nodif.2[,"coverage"])
bp.dat.coverage.rosali <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif>0.3 & res.dat.article.rosali.2$nb.dif!=0,"coverage"])
bp.dat.coverage.residif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif>0.3 & res.dat.article.residif.2$nb.dif!=0,"coverage"])
bp.dat.coverage.dif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif>0.3 & res.dat.article.dif.2$nb.dif!=0,"coverage"])
bp.dat.coverage <- data.frame(coverage=c(bp.dat.coverage.nodif,bp.dat.coverage.ignore,bp.dat.coverage.rosali,bp.dat.coverage.residif,
bp.dat.coverage.dif),method=c(rep("NO DIF",18),
rep("IGNORE-DIF",10),
rep("ROSALI",10),
rep("RESIDIF",10),
rep("PCM-DIF",10) ) )
bp.dat.coverage$method <- factor(bp.dat.coverage$method,levels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"))
boxplot(bp.dat.coverage$coverage~bp.dat.coverage$method,xlab=" ",pch=3,main="",
ylab=" ",ylim=c(0,1),yaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.45,
col=c("#e69875","#a7c080","#a7c080","#a7c080","#a7c080")
,border=c("#CD5E35","#697850","#697850","#697850","#697850"))
axis(2,seq(0,1,0.1),cex.axis=1.45)
abline(h=0.95,lty=2,col='#777777',lwd=2)
##########################
# BOXPLOT PERF VS NODIF FACET POWER
##########################
par(mfrow=c(3,4))
par(bg = "white")
res.dat.article.2$abs.gamma <- abs(as.numeric(res.dat.article.2$true.gamma))
res.dat.article.rosali.2$abs.gamma <- abs(as.numeric(res.dat.article.rosali.2$true.gamma))
res.dat.article.residif.2$abs.gamma <- abs(as.numeric(res.dat.article.residif.2$true.gamma))
res.dat.article.dif.2$abs.gamma <- abs(as.numeric(res.dat.article.dif.2$true.gamma))
res.dat.article.2$prop.dif <- res.dat.article.2$nb.dif/res.dat.article.2$J
res.dat.article.rosali.2$prop.dif <- res.dat.article.rosali.2$nb.dif/res.dat.article.rosali.2$J
res.dat.article.residif.2$prop.dif <- res.dat.article.residif.2$nb.dif/res.dat.article.residif.2$J
res.dat.article.dif.2$prop.dif <- res.dat.article.dif.2$nb.dif/res.dat.article.dif.2$J
# N50
## DIF 03 25%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="Weak DIF - 25% of items",
ylab="RCT power - expected power",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 03 50%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="Weak DIF - 50% of items",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 05 25%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="Medium DIF - 25% of items",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 05 50%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==50 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==50 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==50 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==50 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="Medium DIF - 50% of items",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
# N100
## DIF 03 25%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="RCT power - expected power",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 03 50%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 05 25%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 05 50%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==100 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==100 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==100 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==100 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
# N300
## DIF 03 25%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="RCT power - expected power",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.6,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 03 50%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.3 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.3 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.3 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.3 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 05 25%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
## DIF 05 50%
bp.dat.power.ignore.mask <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma>0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.ignore.magnif <- as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.2[res.dat.article.2$N==300 & res.dat.article.2$abs.gamma==0.5 & res.dat.article.2$prop.dif<0.3 & res.dat.article.2$true.gamma<0 & res.dat.article.2$true.beta>0 & res.dat.article.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.nodif <- as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"power"])-as.numeric(res.dat.article.nodif.2[res.dat.article.nodif.2$true.beta>0,"theoretical.power"])
bp.dat.power.rosali.mask <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma>0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.rosali.magnif <- as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.rosali.2[res.dat.article.rosali.2$N==300 & res.dat.article.rosali.2$abs.gamma==0.5 & res.dat.article.rosali.2$prop.dif<0.3 & res.dat.article.rosali.2$true.beta>0 & res.dat.article.rosali.2$true.gamma<0 & res.dat.article.rosali.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.mask <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma>0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.residif.magnif <- as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.residif.2[res.dat.article.residif.2$N==300 & res.dat.article.residif.2$abs.gamma==0.5 & res.dat.article.residif.2$prop.dif<0.3 & res.dat.article.residif.2$true.beta>0 & res.dat.article.residif.2$true.gamma<0 & res.dat.article.residif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.mask <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma>0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power.dif.magnif <- as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"power"])-as.numeric(res.dat.article.dif.2[res.dat.article.dif.2$N==300 & res.dat.article.dif.2$abs.gamma==0.5 & res.dat.article.dif.2$prop.dif<0.3 & res.dat.article.dif.2$true.beta>0 & res.dat.article.dif.2$true.gamma<0 & res.dat.article.dif.2$nb.dif!=0,"theoretical.power"])
bp.dat.power <- data.frame(power=c(bp.dat.power.nodif,bp.dat.power.ignore.mask,bp.dat.power.ignore.magnif,bp.dat.power.rosali.mask,bp.dat.power.rosali.magnif,bp.dat.power.residif.mask,bp.dat.power.residif.magnif,
bp.dat.power.dif.mask,bp.dat.power.dif.magnif),
method=c(rep("NO DIF",12),
rep("MASK1",4),rep("AMPLIFY1",4),
rep("MASK2",4),rep("AMPLIFY2",4),
rep("MASK3",4),rep("AMPLIFY3",4),
rep("MASK4",4),rep("AMPLIFY4",4) ))
bp.dat.power$method <- factor(bp.dat.power$method,levels=c("NO DIF","MASK1","AMPLIFY1","MASK2","AMPLIFY2","MASK3","AMPLIFY3","MASK4","AMPLIFY4"))
boxplot(bp.dat.power$power~bp.dat.power$method,xlab="",pch=3,main="",
ylab="",ylim=c(-1,1),yaxt="n",xaxt="n",
cex.lab=1.45,cex.main=1.5,cex.axis=1.15,
col=c("#e69875","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5","#798A5D","#D4E8B5")
,border=c("#CD5E35","#697850","#697850","#697850","#697850","#697850","#697850","#697850","#697850"),
width=c(0.8,0.4,0.4,0.4,0.4,0.4,0.4,0.4,0.4),
at=c(1,2,2.5,3.25,3.75,4.5,5,5.75,6.25))
axis(2,seq(-1,1,0.25),cex.axis=1.45)
axis(1,c(1,2.25,3.5,4.75,6),labels=c("NO DIF","IGNORE-DIF","ROSALI","RESIDIF","PCM-DIF"),cex.axis=1.45)
abline(h=0,lty=2,col='#595959',lwd=2)
par(mfrow=c(1,1))