R 中 glm 插入符号模型的方差 inflation VIF

Question

我想计算 R 中插入符号 glm 模型的方差 inflation 因子 (VIF)。这是我的代码，数据集来自 UCI：

library(caret)
library(tidyverse)

url <- paste0("https://archive.ics.uci.edu/ml/machine-learning-databases/",
              "00267/data_banknote_authentication.txt")

dataset <- read_csv(url, col_names = c("varWav","skeWav","curtWav","entropy","class"))
dataset$class <- as.factor(ifelse(dataset$class == 0,"Authentic","Forgery"))

idx <- createDataPartition(dataset$class, p = 0.8, list = FALSE)
train_set <- dataset[idx,]
test_set <- dataset[-idx,]

notes_model <- train(class ~.,
                   data = train_set,
                   method = "glm")

但是当我尝试这段代码时 returns 我出错了：

car::vif(notes_model)

Error in UseMethod("vcov") : no applicable method for 'vcov' applied to an object of class "c('train', 'train.formula')"

也许我的代码有误？拜托，我们将不胜感激。

Answer 1

您可以提取最终的训练模型，然后计算 vif：

car::vif(notes_model$finalModel)
    varWav     skeWav    curtWav    entropy 
 63.978111 184.323806 356.526156   1.935005

R 中 glm 插入符号模型的方差 inflation VIF

Variance inflation VIF for glm caret model in R

r

r-caret