nomogram图，又称列线图，诺模图，可直接用于计算预测的分数 #first need to set path setwd('E:\Course\cluster') #设置当前工作目录 getwd() #查看当前工作目录 thyroid<- read.table("thyroid.csv",header=FALSE,sep=",") #load data=thyroid data=thyroid[,1:10] #太多不拟合 fix(data) rownames(data)[1]<-"thyroidlabel" #把1,2的标签换成0 1 for (i in 1:455) {if(data[i,1]==2) data[i,1]=0 } data=data[,-13] ###标签换成yes no的离散的 但是不好用 if(0){ for (i in 1:455) {if(data[i,13]==0) data[i,13]='no' else data[i,13]='yes' }} ### library(rms) ## 第三步 按照nomogram要求“打包”数据，绘制nomogram的关键步骤,??datadist查看详细说明 ddist <- datadist(data) options(datadist="ddist") ## 第四步 构建模型 ## 构建logisitc回归模型 mod <- lrm(data$thyroidlabel~.,data = data) mod <- glm(data$thyroidlabel ~.,family=binomial(link = "logit"),data = data) ## 绘制logisitc回归的风险预测值的nomogram图 nom <- nomogram(f1, fun= function(x)1/(1+exp(-x)), # or fun=plogis lp=F, funlabel="LN Metastasis Risk") plot(nom) nom <- nomogram(mod, lp=T, lp.at = seq(-5,5,by=0.5), fun=function(x) 1/(1+exp(-x)), funlabel = 'Risk of metastasis', fun.at = c(0.05,seq(0.1,0.9,by=0.1),0.95), conf.int = c(0.1,0.7)) plot(nom, lplabel = 'Linear Predictor', fun.side = c(1,1,1,1,3,1,3,1,1,1,1), label.every = 3, col.conf = c('blue','green'), conf.space = c(0.1,0.5), col.grid = gray(c(0.8,0.95)), which='shock') 结果如上图所示   参考资料可以去丁香园上找 参考文献： doi:10.1093/jnci/djv291  Establishment and Validation of Prognostic Nomograms for Endemic Nasopharyngeal Carcinoma