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$id)
但是我想在对数据进行分区或采样时，避免将同一个人（id）分成两个数据集，这是他们从数据中随机采样/分区的一种方法，将个人分配给相应的分区而不是观察？
我尝试取样：
mysample <- data[sample(unique(data$id), 1000,replace=FALSE),]
但是，这破坏了数据的面板性质。
我认为使用的采样方法中有一个小错误sample()：它id像行号一样使用变量。相反，该函数需要获取属于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
让我们检查类余额，因为它createDataPartition将使WORKING的类余额在所有集合中均相等。
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
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我来说两句