Nothing
# plot CV pred MBlocs
#########################
plot_cvpred_mbplsda <- function (obj, filename="PlotCVpredMbplsda"){
pdf(paste0(filename,".pdf"), paper="a4r", width=12, height=12)
par(mai=c(1,1,1,1))
# parameters and arguments
appel <- as.list(obj$call)
initialModel <- eval.parent(appel$object)
bloY <- eval.parent(appel$bloY)
nNoBin <- sum(bloY!=2) # nombre de variables non binaires
optdim <- eval.parent(appel$optdim)
if(is.null(eval.parent(appel$algo))==TRUE) (algo <- c("max","gravity","threshold"))
if(is.null(eval.parent(appel$algo))==FALSE) (algo <- eval.parent(appel$algo))
initialScoresInd <- eval.parent(initialModel)$lX[,1:optdim]
# IF MORE THAN ONE DIMENSION
if(optdim >1){
if(optdim%%2!=0 & optdim>2) {initialScoresInd <- cbind(initialScoresInd[,1:(optdim-1)],initialScoresInd[,(optdim-1):(optdim)])}
miniScoresInd <- min(initialScoresInd)
maxiScoresInd <- max(initialScoresInd)
# scatter plot with coloration according to the true statut
par(mfrow=c(2,2))
for(j in seq(from=1, to=dim(initialScoresInd)[2], by=2)){
for(i in 1:(sum(bloY))){
plot(initialScoresInd[,j],initialScoresInd[,j+1],
pch=c(16,16,1)[as.numeric(factor(eval.parent(appel$object)$tabY[,i]))],
col=c("grey","black","black")[as.numeric(factor(eval.parent(appel$object)$tabY[,i]))],
main=paste("Observed scatterplot \n obs colored by",colnames(eval.parent(appel$object)$tabY)[i]),
xlim=c(miniScoresInd,maxiScoresInd)*1.05, ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab=colnames(initialScoresInd)[j], ylab=colnames(initialScoresInd)[j+1],
las=1)
abline(h=0,v=0)
legend("bottomright", legend = c("0","1"), pch=c(16,16), col=c("grey","black"))
}
}
# scatter plot with coloration according to matPredYv.max
if("max" %in% algo){
for(j in seq(from=1, to=dim(initialScoresInd)[2], by=2)){
for(i in (sum(bloY)+2):(dim(obj$matPredYv.max)[2])){
plot(initialScoresInd[,j],initialScoresInd[,j+1], pch=c(16,16,1)[factor(obj$matPredYv.max[,i])], col=c("grey","black","black")[factor(obj$matPredYv.max[,i])],
main=paste("Observed scatterplot obs colored \n by cross validated",colnames(obj$matPredYv.max)[i]),
xlim=c(miniScoresInd,maxiScoresInd)*1.05, ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab=colnames(initialScoresInd)[j], ylab=colnames(initialScoresInd)[j+1],
las=1)
abline(h=0,v=0)
legend("bottomright", legend = c("0","1","NA"), pch=c(16,16,1), col=c("grey","black","black"))
mtext("subset=validation, method=maximal value", cex=0.75)
}
}
}
# scatter plot with coloration according to matPredYv.gravity
if("gravity" %in% algo){
for(j in seq(from=1, to=dim(initialScoresInd)[2], by=2)){
for(i in (sum(bloY)+2):(dim(obj$matPredYv.gravity)[2])){
plot(initialScoresInd[,j],initialScoresInd[,j+1], pch=c(16,16,1)[factor(obj$matPredYv.gravity[,i])], col=c("grey","black","black")[factor(obj$matPredYv.gravity[,i])],
main=paste("Observed scatterplot obs colored \n by cross validated",colnames(obj$matPredYv.gravity)[i]),
xlim=c(miniScoresInd,maxiScoresInd)*1.05, ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab=colnames(initialScoresInd)[j], ylab=colnames(initialScoresInd)[j+1],
las=1)
abline(h=0,v=0)
legend("bottomright", legend = c("0","1","NA"), pch=c(16,16,1), col=c("grey","black","black"))
mtext("subset=validation, method=center of gravity", cex=0.75)
}
}
}
if("threshold" %in% algo){
# scatter plot with coloration according to matPredYv.threshold
for(j in seq(from=1, to=dim(initialScoresInd)[2], by=2)){
for(i in (sum(bloY)+2):(dim(obj$matPredYv.threshold)[2])){
plot(initialScoresInd[,j],initialScoresInd[,j+1], pch=c(16,16,1)[factor(obj$matPredYv.threshold[,i])], col=c("grey","black","black")[factor(obj$matPredYv.threshold[,i])],
main=paste("Observed scatterplot obs colored \n by cross validated",colnames(obj$matPredYv.threshold)[i]),
xlim=c(miniScoresInd,maxiScoresInd)*1.05, ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab=colnames(initialScoresInd)[j], ylab=colnames(initialScoresInd)[j+1],
las=1)
abline(h=0,v=0)
legend("bottomright", legend = c("0","1","NA"), pch=c(16,16,1), col=c("grey","black","black"))
mtext("subset=validation, method=threshold", cex=0.75)
}
}
}
}
# IF ONE DIMENSION
if(optdim==1){
miniScoresInd <- min(initialScoresInd)
maxiScoresInd <- max(initialScoresInd)
# scatter plot with coloration according to the true statut
par(mfrow=c(2,2))
for(i in 1:(sum(bloY))){
plot(initialScoresInd,
pch=c(16,16,1)[as.numeric(factor(eval.parent(appel$object)$tabY[,i]))],
col=c("grey","black","black")[as.numeric(factor(eval.parent(appel$object)$tabY[,i]))],
main=paste("Observed scatterplot \n obs colored by",colnames(eval.parent(appel$object)$tabY)[i]),
ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab="observations", ylab="Ax1",
las=1)
abline(h=0)
legend("bottomright", legend = c("0","1"), pch=c(16,16), col=c("grey","black"))
}
# scatter plot with coloration according to matPredYv.max
if("max" %in% algo){
for(i in (sum(bloY)+2):(dim(obj$matPredYv.max)[2])){
plot(initialScoresInd, pch=c(16,16,1)[factor(obj$matPredYv.max[,i])], col=c("grey","black","black")[factor(obj$matPredYv.max[,i])],
main=paste("Observed scatterplot obs colored \n by cross validated",colnames(obj$matPredYv.max)[i]),
ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab="observations", ylab="Ax1",
las=1)
abline(h=0)
legend("bottomright", legend = c("0","1","NA"), pch=c(16,16,1), col=c("grey","black","black"))
mtext("subset=validation, method=maximal value", cex=0.75)
}
}
# scatter plot with coloration according to matPredYv.gravity
if("gravity" %in% algo){
for(i in (sum(bloY)+2):(dim(obj$matPredYv.gravity)[2])){
plot(initialScoresInd, pch=c(16,16,1)[factor(obj$matPredYv.gravity[,i])], col=c("grey","black","black")[factor(obj$matPredYv.gravity[,i])],
main=paste("Observed scatterplot obs colored \n by cross validated",colnames(obj$matPredYv.gravity)[i]),
ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab="observations", ylab="Ax1",
las=1)
abline(h=0)
legend("bottomright", legend = c("0","1","NA"), pch=c(16,16,1), col=c("grey","black","black"))
mtext("subset=validation, method=center of gravity", cex=0.75)
}
}
if("threshold" %in% algo){
# scatter plot with coloration according to matPredYv.threshold
for(i in (sum(bloY)+2):(dim(obj$matPredYv.threshold)[2])){
plot(initialScoresInd, pch=c(16,16,1)[factor(obj$matPredYv.threshold[,i])], col=c("grey","black","black")[factor(obj$matPredYv.threshold[,i])],
main=paste("Observed scatterplot obs colored \n by cross validated",colnames(obj$matPredYv.threshold)[i]),
ylim=c(miniScoresInd,maxiScoresInd)*1.05,
xlab="observations", ylab="Ax1",
las=1)
abline(h=0)
legend("bottomright", legend = c("0","1","NA"), pch=c(16,16,1), col=c("grey","black","black"))
mtext("subset=validation, method=threshold", cex=0.75)
}
}
}
dev.off()
}
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