ModelSelectionVisualization <- function(outdir){
# Loading the model performance tables
betamultiplier <- NULL
AICc <- NULL
AUC.Test <- NULL
variables <- NULL
ModelPerformances <- read.table(paste(outdir,"/ModelPerformance.txt",sep=""), header=TRUE, stringsAsFactors=FALSE)
ModelPerformances$AIC[ModelPerformances$AIC=="x"]=NA
ModelPerformances$AICc[ModelPerformances$AICc=="x"]=NA
ModelPerformances$AICc <- as.numeric(ModelPerformances$AICc)
ModelPerformances$BIC[ModelPerformances$BIC=="x"]=NA
ModelPerformances$AUC.Test <- as.numeric(ModelPerformances$AUC.Test)
# Select the lowest AICc values
ModelPerformances.bestsets <- ModelPerformances[
which(ModelPerformances$AICc==min(ModelPerformances$AICc,na.rm=TRUE))
,]
ModelPerformances <- ModelPerformances[
-which(ModelPerformances$AICc==min(ModelPerformances$AICc,na.rm=TRUE))
,]
ModelPerformances.added <- ModelPerformances.bestsets # Just to add this in the end so that the legends don't appear red
ModelPerformances.added$AUC.Test <- ModelPerformances.added$AUC.Test+5
png(filename = paste(outdir,"/ModelSelectionAICc_MarkedMinAICc.png",sep=""), height=120, width=250, units="mm", pointsize=12, res=600)
print(
ggplot2::ggplot(ModelPerformances,ggplot2::aes(betamultiplier,AICc,size=log(variables),colour=variables))+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red")+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red",shape=1,size=8)+
ggplot2::geom_point()+
ggplot2::ylim(min(ModelPerformances$AICc)-100,max(ModelPerformances$AICc)+100)+
ggplot2::guides(size=FALSE)+ # Exclude legend on size
ggplot2::theme(axis.text.y=ggplot2::element_text(size=14),
axis.text.x = ggplot2::element_text(size=14),
axis.title.x=ggplot2::element_text(size=15),
axis.title.y=ggplot2::element_text(size=15,vjust=1),
legend.text=ggplot2::element_text(size=13))+
ggplot2::theme(legend.title = ggplot2::element_text(size = 13))+
ggplot2::labs(colour="Variables")
)
dev.off()
png(filename = paste(outdir,"/ModelSelectionAUCTest_MarkedMinAICc.png",sep=""), height=120, width=250, units="mm", pointsize=12, res=600)
print(
ggplot2::ggplot(ModelPerformances,ggplot2::aes(betamultiplier,AUC.Test,size=log(variables),colour=variables))+
ggplot2::geom_point()+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red")+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red",shape=1,size=8)+
ggplot2::geom_point(data=ModelPerformances.added)+
ggplot2::ylim(min(ModelPerformances$AUC.Test)-0.1,max(ModelPerformances$AUC.Test)+0.1)+
ggplot2::guides(size=FALSE)+ # Exclude legend on size
ggplot2::theme(axis.text.y=ggplot2::element_text(size=14),
axis.text.x = ggplot2::element_text(size=14),
axis.title.x= ggplot2::element_text(size=15),
axis.title.y= ggplot2::element_text(size=15,vjust=1),
legend.text=ggplot2::element_text(size=13))+
ggplot2::theme(legend.title = ggplot2::element_text(size = 13))+
ggplot2::labs(colour="Variables")
)
dev.off()
ModelPerformances <- read.table(paste(outdir,"/ModelPerformance.txt",sep=""), header=TRUE, stringsAsFactors=FALSE)
ModelPerformances$AIC[ModelPerformances$AIC=="x"]=NA
ModelPerformances$AICc[ModelPerformances$AICc=="x"]=NA
ModelPerformances$AICc <- as.numeric(ModelPerformances$AICc)
ModelPerformances$BIC[ModelPerformances$BIC=="x"]=NA
ModelPerformances$AUC.Test <- as.numeric(ModelPerformances$AUC.Test)
# Select the highest AUC.Test values
ModelPerformances.bestsets <- ModelPerformances[
which(ModelPerformances$AUC.Test==max(ModelPerformances$AUC.Test,na.rm=TRUE))
,]
ModelPerformances <- ModelPerformances[
-which(ModelPerformances$AUC.Test==max(ModelPerformances$AUC.Test,na.rm=TRUE))
,]
ModelPerformances.added <- ModelPerformances.bestsets # Just to add this in the end so that the legends don't appear red
ModelPerformances.added$AUC.Test <- ModelPerformances.added$AUC.Test+5
png(filename = paste(outdir,"/ModelSelectionAICc_MarkedMaxAUCTest.png",sep=""), height=120, width=250, units="mm", pointsize=12, res=600)
print(
ggplot2::ggplot(ModelPerformances,ggplot2::aes(betamultiplier,AICc,size=log(variables),colour=variables))+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red")+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red",shape=1,size=8)+
ggplot2::geom_point()+
ggplot2::ylim(min(ModelPerformances$AICc)-100,max(ModelPerformances$AICc)+100)+
ggplot2::guides(size=FALSE)+ # Exclude legend on size
ggplot2::theme(axis.text.y=ggplot2::element_text(size=14),
axis.text.x = ggplot2::element_text(size=14),
axis.title.x=ggplot2::element_text(size=15),
axis.title.y=ggplot2::element_text(size=15,vjust=1),
legend.text=ggplot2::element_text(size=13))+
ggplot2::theme(legend.title = ggplot2::element_text(size = 13))+
ggplot2::labs(colour="Variables")
)
dev.off()
png(filename = paste(outdir,"/ModelSelectionAUCTest_MarkedMaxAUCTest.png",sep=""), height=120, width=250, units="mm", pointsize=12, res=600)
print(
ggplot2::ggplot(ModelPerformances,ggplot2::aes(betamultiplier,AUC.Test,size=log(variables),colour=variables))+
ggplot2::geom_point()+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red")+
ggplot2::geom_point(data=ModelPerformances.bestsets,colour="red",shape=1,size=8)+
ggplot2::geom_point(data=ModelPerformances.added)+
ggplot2::ylim(min(ModelPerformances$AUC.Test)-0.1,max(ModelPerformances$AUC.Test)+0.1)+
ggplot2::guides(size=FALSE)+ # Exclude legend on size
ggplot2::theme(axis.text.y=ggplot2::element_text(size=14),
axis.text.x = ggplot2::element_text(size=14),
axis.title.x=ggplot2::element_text(size=15),
axis.title.y=ggplot2::element_text(size=15,vjust=1),
legend.text=ggplot2::element_text(size=13))+
ggplot2::theme(legend.title = ggplot2::element_text(size = 13))+
ggplot2::labs(colour="Variables")
)
dev.off()
}
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