Question

过去几天我一直在网上搜索map2的文档。我已经采用了训练集，嵌套了数据并为其创建了coxph模型，将这些模型保存在嵌套表中。现在我想从该模型预测，但我想使用type =“expected”作为，根据文档（R documentation: predict.coxph）

受试者的生存概率等于exp（-expected）

我已经调整了相关代码，使用mpg数据集重现我的问题。

我有以下4个例子，它们在预测功能起作用后无效。请注意，我已从此集合中删除了coxph.null模型，因此唯一的模型是类（coxph）。此代码可用于复制错误。

#Needed libraries
library(ggplot2)
library(tidyverse)
library(purrr)
library(broom)
library(survival)
#Create data set
mpg_data <- mpg
mpg_data <- mpg_data %>% 
  mutate(mpg_diff = cty - hwy)
mpg_data <- mpg_data %>% 
  mutate(EVENT = (mpg_diff >= -8))
set.seed(1)
mpg_data <- mpg_data %>% 
  mutate(TIME_TO_EVENT = as.integer(runif(234, 1, 100)))
mpg_nested <- mpg_data %>% 
  group_by(manufacturer) %>% 
  mutate(n_prot = length(model)) %>% 
  nest()
# Stepwise regression 
stepwise <- function(data) {
  response <- Surv(time = data$TIME_TO_EVENT, event = data$EVENT, type = "right") 
full <- "Surv(time = data$TIME_TO_EVENT, event = data$EVENT, type = 'right') ~ data$cyl+data$cty+data$hwy+data$displ"
x <- factor(as.factor(data$model))
full <- ifelse(nlevels(x) >= 2, paste(full, "as.character(data$model)", sep = "+"), full)
x <- factor(as.factor(data$trans))
full <- ifelse(nlevels(x) >= 2, paste(full, "as.character(data$trans)", sep = "+"), full)
x <- factor(as.factor(data$drv))
full <- ifelse(nlevels(x) >= 2, paste(full, "as.character(data$drv)", sep = "+"), full)
null_model_ONE <- coxph(response ~ 1, data=data)
full_model_ONE <- coxph(as.formula(full), data=data)
model_ONE <- step(null_model_ONE, scope=list(lower=null_model_ONE, upper=full_model_ONE))
}
survival_mpg <- mpg_nested %>%  
  mutate(model_fit = map(data, stepwise))
#Predicting values
#This works but is not type="expected"
survival_mpg_predict <- survival_mpg %>% 
  mutate(mpg_predict = map2(model_fit, data, predict))
##TRY 1##
predict.F <- function(model_fit, data){
  predict(model_fit, newdata=data, type="expected")
}
survival_mpg_predict <- survival_mpg %>% 
  mutate(mpg_predict = map2(model_fit, data, predict.F))
#Error in mutate_impl(.data, dots) : Evaluation error: requires numeric/complex matrix/vector arguments.
##Try 2##
survival_mpg_predict <- survival_mpg %>% 
  mutate(mpg_predict = map2(model_fit, data, predict(model_fit, newdata = data, type="expected")))
#Error in mutate_impl(.data, dots) : Evaluation error: no applicable method for 'predict' applied to an object of class "list".
##Try 3##
survival_mpg_predict <- survival_mpg %>% 
  mutate(mpg_predict = map2(model_fit, data, ~ predict(.x, newdata = .y, type="expected")))
#Error in mutate_impl(.data, dots) : Evaluation error: requires numeric/complex matrix/vector arguments.
##Try 4##
survival_mpg_predict <- survival_mpg %>% 
  mutate(mpg_predict = map2(model_fit, data, function(model_fit, data) predict(model_fit, newdata=data, type="expected")))
#Error in mutate_impl(.data, dots) : Evaluation error: requires numeric/complex matrix/vector arguments.

Answer 1

修改## TRY 1 ##删除newdata参数并将map2（）函数更改为map（）函数

predict.F <- function(model_fit, data){
predict(model_fit, type="expected")
}
survival_mpg_predict <- survival_mpg %>% 
mutate(mpg_predict = map(model_fit, predict.F))

试图在map2（）函数中指定predict.coxph类型

1 个答案: