官网教程
https://rdrr.io/cran/givitiR/man/logit.html
用的是包自带的测试数据，这个包只能对于二分类变量做出校准曲线了

library(givitiR)
data("icuData")


cb <- givitiCalibrationBelt(o = icuData$outcome, #最终结果，二分类金标准
                            e = icuData$probSaps,#预测概率
                            devel = "external",#是验证集还是模型训练集
                            nPoints = 200)#绘制的点数


plot(cb,#返回对象
     main = "SAPSII calibration",#图名称
     xlab = "SAPSII predicted probability",#x轴名称
     ylab = "Observed mortality",#y轴名称
     pvalueString =T,#是否显示P值
     nString= T,#是否显示样本量
     polynomialString =T,#是否显示分类数量
     table = T,#是否显示右下角图表
     xlim = c(0,1), ylim = c(0,1))#横纵坐标范围


#构建公式
formulaSAPS <- formula(outcome ~ relevel(adm,'schSurg') + relevel(chronic,'noChronDis') +
                         relevel(age,'<40') + relevel(gcs,'14-15') +
                         relevel(BP,'100-199') + relevel(HR,'70-119') +
                         relevel(temp,'<39') + relevel(urine,'>=1') +
                         relevel(urea,'<0.60') + relevel(WBC,'1-19') +
                         relevel(potassium,'3-4.9') + relevel(sodium,'125-144') +
                         relevel(HCO3,'>=20') + relevel(bili,'<4') +
                         relevel(paFiIfVent,'noVent') )

#进行逻辑回归
refittedSaps <- glm(formula = formulaSAPS, 
                    family=binomial(link='logit'),
                    na.action = na.exclude, 
                    data = icuData)

#计算预测值
icuData$probRefittedSaps <- predict(refittedSaps, type = "response")

#进行校准曲线分析
cbInternal <- givitiCalibrationBelt(o = icuData$outcome, icuData$probRefittedSaps,
                                    devel = "internal")
#绘制图表
plot(cbInternal, main = "Refitted SAPSII calibration",
     xlab = "Refitted SAPSII predicted probability",
     ylab = "Observed mortality")