stratified splitting the data

问题

I have a large data set and like to fit different logistic regression for each City, one of the column in my data. The following 70/30 split works without considering City group.

indexes <- sample(1:nrow(data), size = 0.7*nrow(data))

train <- data[indexes,]
test <- data[-indexes,]

But this does not guarantee the 70/30 split for each city.

lets say that I have City A and City B, where City A has 100 rows, and City B has 900 rows, totaling 1000 rows. Splitting the data with above code will give me 700 rows for train and 300 for test data, but it does not guarantee that i will have 70 rows for City A, and 630 rows for City B in the train data. How do i do that?

Once i have the training data split-ed to 70/30 fashion for each city,i will run logistic regression for each city ( I know how to do this once i have the train data)

回答1:

Try createDataPartition from caret package. Its document states: By default, createDataPartition does a stratified random split of the data.

library(caret)
train.index <- createDataPartition(Data$Class, p = .7, list = FALSE)
train <- Data[ train.index,]
test  <- Data[-train.index,]

it can also be used for stratified K-fold like:

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 3,
                     ...)
# when calling train, pass this train control
train(...,
      trControl = ctrl,
      ...)

check out caret document for more details

回答2:

The package splitstackshape has a nice function stratified which can do this as well, but this is a bit better than createDataPartition because it can use multiple columns to stratify at once. It can be used with one column like:

library(splitstackshape)
set.seed(42)  # good idea to set the random seed for reproducibility
stratified(data, c('City'), 0.7)

Or with multiple columns:

stratified(data, c('City', 'column2'), 0.7)

回答3:

The typical way is with split

lapply( split(dfrm, dfrm$City), function(dd){
            indexes= sample(1:nrow(dd), size = 0.7*nrow(dd))
            train= dd[indexes, ]    # Notice that you may want all columns
            test= dd[-indexes, ]
            # analysis goes here
            }

If you were to do it in steps as you attempted above it would be like this:

cities <- split(data,data$city)

idxs <- lapply(cities, function (d) {
    indexes <- sample(1:nrow(d), size=0.7*nrow(d))
})

train <- data[ idxs[[1]], ]  # for the first city
test <-  data[ -idxs[[1]], ]

I happen to think the is the clumsy way to do it, but perhaps breaking it down into small steps will let you examine the intermediate values.

回答4:

Your code works just fine as is, if City is a column, simply run training data as train[,2]. You can do this easily for each one with a lambda function

logReg<-function(ind) {
    reg<-glm(train[,ind]~WHATEVER)
    ....
    return(val) }

Then run sapply over the vector of city indexes.

来源：https://stackoverflow.com/questions/20776887/stratified-splitting-the-data

标签

split

logistic-regression