R语言实现漂亮的ROC图效果

上图中如果选cutoff值为0.5时
TPR = 5 /（5 + 0）= 1，
FPR = 2 / (2 + 3) = 0.4, 
预测的准确性 = (TP + TN )/ 总的样本数 = (5 + 3)/10 = 0.8

library(openxlsx)
ROC <- read.xlsx("ROC曲线.xlsx")

library(pROC)
## roc的计算，可以一次性批量计算a、b、c三组数据
res<-roc(outcome~a+b+c,data=ROC,aur=TRUE,
         ci=TRUE, # 显示95%CI
         # percent=TRUE, ##是否需要以百分比显示
         levels=c('group1','group2'),direction=">" #设置分组方向
         )
## 平滑曲线的ROC结果
smooth<-roc(outcome~a+b+c,data=ROC,aur=TRUE,
         ci=TRUE, # 显示95%CI
         # percent=TRUE, ##是否需要以百分比显示
         smooth=TRUE,
         levels=c('group1','group2'),direction=">" #设置分组方向
         )

res
Call:
roc.fORMula(formula = outcome ~ a, data = ROC, aur = TRUE, ci = TRUE,     levels = c("group1", "group2"), direction = ">")
Data: a in 40 controls (outcome group1) > 32 cases (outcome group2).
Area under the curve: 0.7328
95% CI: 0.6171-0.8485 (DeLong)
$b
Call:
roc.formula(formula = outcome ~ b, data = ROC, aur = TRUE, ci = TRUE,     levels = c("group1", "group2"), direction = ">")

Data: b in 40 controls (outcome group1) > 32 cases (outcome group2).
Area under the curve: 0.8234
95% CI: 0.7303-0.9165 (DeLong)
$c
Call:
roc.formula(formula = outcome ~ c, data = ROC, aur = TRUE, ci = TRUE,     levels = c("group1", "group2"), direction = ">")

Data: c in 40 controls (outcome group1) > 32 cases (outcome group2).
Area under the curve: 0.9242
95% CI: 0.8679-0.9805 (DeLong)

library(ggplot2)
pa<- ggroc(smooth$a, 
       legacy.axes = TRUE # 将X轴改为0-1，（默认是1-0）
       )+
   geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), 
                color="darkgrey", linetype=4)+
 theme_bw() +# 设置背景
 ggtitle('a-ROC')
pb<- ggroc(smooth$b, legacy.axes = TRUE)+geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color="darkgrey", linetype=4)+theme_bw() +ggtitle('b-ROC')
pc<- ggroc(smooth$c, legacy.axes = TRUE)+geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color="darkgrey", linetype=4)+theme_bw() +ggtitle('c-ROC')
cowplot::plot_grid(pa,pb,pc,labels = "AUTO",nrow = 1)

ggroc(smooth, legacy.axes = TRUE)+
    geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1), color="darkgrey", linetype=4)+
    theme_bw()+ggtitle('ROC')+ggsci::scale_color_lancet()+
    annotate("text",x=0.75,y=0.125,label=paste("a-AUC = ", round(res$a$auc,3)))+
    annotate("text",x=0.75,y=0.25,label=paste("b-AUC = ", round(res$b$auc,3)))+
    annotate("text",x=0.75,y=0.375,label=paste("c-AUC = ", round(res$c$auc,3)))