library(survival)
library(randomForestSRC)# 生成模拟数据
set.seed(123)
n <- 200
time <- rexp(n, rate = 0.1)
status <- rbinom(n, size = 1, prob = 0.7)
var1 <- rnorm(n)
var2 <- rnorm(n)
var3 <- rnorm(n)
data1 <- data.frame(time = time, status = status, var1 = var1, var2 = var2, var3 = var3)# 定义模型列表
models <- list(cox = function(data) {fit <- survival::coxph(Surv(time,status) ~ .,data=data)sum<-summary(fit)[["coefficients"]][,5] canshu<-names(sum)result<-list(fit=fit,canshu=canshu)return(result)},rsf=function(data){fit<-rfsrc(Surv(time,status) ~ .,data=data1)canshu<-var.select(object=fit,method="md",conservative="low")$md.obj$topvars.1seresult<-list(fit=fit,canshu=canshu)return(result)}
)# 列举所有模型组合（考虑顺序）
model<-c("cox","rsf")
all_combinations <- list()
library(gtools)
for (n in 1:length(model)) {permutations <- permutations(2,n,v=model)mat_list <- apply(permutations, 1, function(row) paste(row, collapse = ","))mat_vector_list <- lapply(mat_list, function(str) unlist(strsplit(str, ",")))all_combinations <- c(all_combinations, mat_vector_list)
}
model_combinations<-all_combinations# 循环遍历不同模型组合
selected_vars_final <- list()
for (i in 1:length(model_combinations)) {comb <- model_combinations[[i]]selected_vars <- NULL# 循环遍历每个模型类型data1<-lungfor (model_name in comb) {i=1if (grep(model_name,comb)==1) {# 根据前一步的选择变量建立模型并筛选变量result <- models[[model_name]](data1)cat("第一步:",model_name,"---",result$canshu,"\n")} else {vc=paste("c(", paste(sprintf('"%s"', selected_vars), collapse = ","), ")", sep = "")cat("纳入第二步的因素:",model_name,"---",vc,"\n")selected_data <- data.frame(data1[,eval(parse(text = vc))],data1[,c("time","status")])result <- models[[model_name]](selected_data)}# 更新选定变量selected_vars <- result$canshu}selected_vars_final[[paste(comb, collapse = "_")]] <- selected_vars
}print(selected_vars_final)
