r - 如何按个人采样/划分面板数据(最好使用插入符号库)？

Question

我想对面板数据进行分区并保留数据的面板性质:

      library(caret)
      library(mlbench)

      #example panel data where id is the persons identifier over years
      data <- read.table("http://people.stern.nyu.edu/wgreene/Econometrics/healthcare.csv",
                    header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)

      ## Here for instance the dependent variable is working
      inTrain <- createDataPartition(y = data$WORKING, p = .75,list = FALSE)

      # subset into training
      training <- data[ inTrain,]
      # subset into testing
      testing <- data[-inTrain,]
      # Here we see some intersections of identifiers 
      str(training$id[10:20])
      str(testing$id)

但是我想，在对数据进行分区或采样时，避免将同一个人(id)分成两个数据集。他们是一种从数据中随机采样/分区并将个体分配给相应分区而不是观察的方法?

我试图采样:

    mysample <- data[sample(unique(data$id), 1000,replace=FALSE),] 

然而，这破坏了数据的面板性质......

Answer 1

我认为使用 sample() 的采样方法存在一个小错误。 : 它正在使用 id像行号一样的变量。相反，该函数需要获取属于一个 ID 的所有行:

nID <- length(unique(data$id))
p = 0.75
set.seed(123)
inTrainID <- sample(unique(data$id), round(nID * p), replace=FALSE)
training <- data[data$id %in% inTrainID, ] 
testing <- data[!data$id %in% inTrainID, ] 

head(training[, 1:5], 10)
#    id FEMALE YEAR AGE   HANDDUM
# 1   1      0 1984  54 0.0000000
# 2   1      0 1985  55 0.0000000
# 3   1      0 1986  56 0.0000000
# 8   3      1 1984  58 0.1687193
# 9   3      1 1986  60 1.0000000
# 10  3      1 1987  61 0.0000000
# 11  3      1 1988  62 1.0000000
# 12  4      1 1985  29 0.0000000
# 13  5      0 1987  27 1.0000000
# 14  5      0 1988  28 0.0000000


dim(data)
# [1] 27326    41
dim(training)
# [1] 20566    41
dim(testing)
# [1] 6760   41
20566/27326
### 75.26% were selected for training

让我们检查类(class)余额，因为 createDataPartition将在所有集合中保持 WORKING 的类平衡。

table(data$WORKING) / nrow(data)
#         0         1 
# 0.3229525 0.6770475 
#
table(training$WORKING) / nrow(training)
#         0         1 
# 0.3226685 0.6773315 
#
table(testing$WORKING) / nrow(testing)
#         0         1 
# 0.3238166 0.6761834 
### virtually equal