尝试在 map2() 函数中指定 predict.coxph 类型

Question

最近几天我一直在网上搜索 map2 的文档。我采用了训练集，嵌套数据并为其创建了 coxph 模型，将这些模型保存在嵌套 table 中。现在我想根据该模型进行预测，但我想根据文档 (R documentation: predict.coxph)

使用 type=“expected”

The survival probability for a subject is equal to exp(-expected)

我已经调整了相关代码以使用 mpg 数据集重现我的问题。

下面有 4 个示例在预测功能有效后无效。请注意，我已经从这个集合中删除了 coxph.null 个模型，因此只有 class(coxph) 的模型。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## 以删除 newdata 参数并将 map2() 函数更改为有效的 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))

尝试在 map2() 函数中指定 predict.coxph 类型

Trying to specify predict.coxph type within map2() function

r

prediction

cox-regression

survival-analysis

purrr