Create multiROC.R

multiROC.R

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+library("PredictABEL")
+
+model.glm0 <- (bayesglm(phen ~ LINE.1,family=binomial,data=data,na.action=na.omit))
+model.glm1 <- (bayesglm(phen ~ cg12071888,family=binomial,data=data,na.action=na.omit))
+model.glm2 <- (bayesglm(phen ~ AHRR,family=binomial,data=data,na.action=na.omit))
+model.glm3 <- (bayesglm(phen ~ ARRB2,family=binomial,data=data,na.action=na.omit))
+model.glm4 <- (bayesglm(phen ~ FGF8,family=binomial,data=data,na.action=na.omit))
+model.glm5 <- (bayesglm(phen ~ cg12071888+AHRR+ARRB2+FGF8,family=binomial,data=data,na.action=na.omit))
+
+pred0 <- predRisk(model.glm0)
+pred1 <- predRisk(model.glm1)
+pred2 <- predRisk(model.glm2)
+pred3 <- predRisk(model.glm3)
+pred4 <- predRisk(model.glm4)
+pred5 <- predRisk(model.glm5)
+
+par(cex.lab=1.5,cex.axis=1.5,lwd=1.5)
+plotROC(data=data,cOutcome=2,predrisk=cbind(pred0,pred1,pred2,pred3,pred4,pred5))
+legend.text<-c("LINE-1","BARHL2","AHRR","ARRB2","FGF8","BAAF")
+legend("bottomright",cex=1.5,legend=legend.text,lty=1:6,col=1:6,bty="n")
+
+tiff("multi-AUC-splited.tiff")
+pdf("multi-AUC-splited.pdf")
+par(mfrow=c(2,3),cex.lab=1.5,cex.axis=1.5,lwd=1.5)
+plotROC(data=data,cOutcome=2,predrisk=cbind(pred0),plottitle="LINE-1")
+plotROC(data=data,cOutcome=2,predrisk=cbind(pred1),plottitle="BARHL2")
+plotROC(data=data,cOutcome=2,predrisk=cbind(pred2),plottitle="AHRR")
+plotROC(data=data,cOutcome=2,predrisk=cbind(pred3),plottitle="ARRB2")
+plotROC(data=data,cOutcome=2,predrisk=cbind(pred4),plottitle="FGF8")
+plotROC(data=data,cOutcome=2,predrisk=cbind(pred5),plottitle="BAAF")
+dev.off()
+
+predicted.value = predict(model.glm5,type=c("response"))
+predicted.data  = data.frame(Type=data$phen, predicted.value)
+logistic.rocobj  = roc(predicted.data$Type, predicted.data$predicted.value,smooth = FALSE)
+logistic.rocdata = data.frame(Sens = logistic.rocobj$sensitivities, Spec = logistic.rocobj$specificities)
+AUC= logistic.rocobj$auc[[1]]
+logistic.rocdata[,3] = logistic.rocdata[,1] + logistic.rocdata[,2]
+seq.max = which(logistic.rocdata[,3] == max(logistic.rocdata[,3]))
+Sens= logistic.rocdata[seq.max[1],1]
+Spec= logistic.rocdata[seq.max[1],2]
+ACC=c(Sens,Spec, AUC)
+ACC