Caret - 基于多个变量创建分层数据集

Question

在 R 包 caret 中，我们是否可以使用函数 createDataPartition()（或 createFolds() 进行交叉验证）基于多个变量创建分层训练和测试集？

以下是一个变量的示例：

#2/3rds for training
library(caret)
inTrain = createDataPartition(df$yourFactor, p = 2/3, list = FALSE)
dfTrain=df[inTrain,]
dfTest=df[-inTrain,]

在上面的代码中，训练集和测试集按“df$yourFactor”分层。但是是否可以使用多个变量（例如“df$yourFactor”和“df$yourFactor2”）进行分层？以下代码似乎有效，但我不知道它是否正确：

inTrain = createDataPartition(df$yourFactor, df$yourFactor2, p = 2/3, list = FALSE)

Answer 1

如果您使用tidyverse.

例如：

df <- df %>%
  mutate(n = row_number()) %>% #create row number if you dont have one
  select(n, everything()) # put 'n' at the front of the dataset
train <- df %>%
  group_by(var1, var2) %>% #any number of variables you wish to partition by proportionally
  sample_frac(.7) # '.7' is the proportion of the original df you wish to sample
test <- anti_join(df, train) # creates test dataframe with those observations not in 'train.'