ROCR_AUC.R

model_lasso_min <- glmnet(x=x, y=y, alpha = 1, lambda=cv_fit$lambda.min)
model_lasso_1se <- glmnet(x=x, y=y, alpha = 1, lambda=cv_fit$lambda.1se)
lasso.prob <- predict(cv_fit, newx=x , s=c(cv_fit$lambda.min,cv_fit$lambda.1se) )
re=cbind(y ,lasso.prob)
re=as.data.frame(re)
colnames(re)=c('event','prob_min','prob_1se')
re$event=as.factor(re$event)

pred_min <- prediction(re[,2], re[,1])
perf_min <- performance(pred_min,"tpr","fpr")
auc_min = performance(pred_min,"auc")@y.values[[1]]

pred_1se <- prediction(re[,3], re[,1])
perf_1se <- performance(pred_1se,"tpr","fpr")
auc_1se = performance(pred_1se,"auc")@y.values[[1]]

plot(perf_min,colorize=FALSE, col="blue") 
plot(perf_1se,colorize=FALSE, col="red",add = T) 
lines(c(0,1),c(0,1),col = "gray", lty = 4 )
text(0.8,0.3, labels = paste0("AUC.min = ",round(auc_min,3)),col = "blue")
text(0.8,0.2, labels = paste0("AUC.lse= ",round(auc_1se,3)),col = "red")