reportROC包绘制ROC曲线、计算约登指数及各种度

reportROC包可以通过一行代码绘制ROC曲线及计算敏感性、特异性、准确率、阳性预测值，阴性预测值，AUC值及约登指数

#官网地址：https://rdrr.io/cran/reportROC/


rm(list = ls()) 
options(stringsAsFactors = F)

library(reportROC)



# 1.官网示例 ------------------------------------------------------------------

data(aSAH)#示例数据
head(aSAH)

#for continuous variables
res.num<- reportROC(gold=aSAH$outcome,#金标准
          predictor=aSAH$s100b,#预测变量
          important="sp",#  选择截断值时候哪个更重要
          plot=TRUE,#绘制ROC曲线
          xlab = "1-specificity",
          ylab = "sensitivity")
res.num

#for binary variables
binary=rep(0,nrow(aSAH))
binary[aSAH$s100b>=0.205]=1
aSAH$binary <- binary
head(aSAH)

reportROC(gold=aSAH$outcome,
          predictor.binary=aSAH$binary,
          important="se",#  选择截断值时候哪个更重要
          plot=TRUE,#绘制ROC曲线
          xlab = "1-specificity",
          ylab = "sensitivity")


# 2.逻辑回归绘制ROC曲线及计算各种指标 ----------------------------------------------------

library(rms)
library(survival)
library(tidyverse)
library(broom)
data("cancer")
head(colon)

model <- glm(status ~ sex +  rx +age, 
                  binomial(link="logit"), 
                  data = colon)

colon$predict <- predict(model,
                         newdata = colon,
                         type = "response")

head(colon$predict)


reportROC(gold=colon$status,
          predictor=colon$predict,
          important="se",#  选择截断值时候哪个更重要
          plot=TRUE,#绘制ROC曲线
          xlab = "1-specificity",
          ylab = "sensitivity")

自测数据结果如下图

ROC曲线 数据化计算结果