caret (version 4.20)

createDataPartition: Data Splitting functions

Description

A series of test/training partitions are created using createDataPartition while createResample creates one or more bootstrap samples. createFolds splits the data into k groups.

Usage

createDataPartition(y, 
                    times = 1,
                    p = 0.5,
                    list = TRUE,
                    groups = min(5, length(y)))
createResample(y, times = 10, list = TRUE)
createFolds(y, k = 10, list = TRUE, returnTrain = FALSE)

Arguments

y

a vector of outcomes

times

the number of partitions to create

p

the percentage of data that goes to training

list

logical - should the results be in a list (TRUE) or a matrix with the number of rows equal to floor(p * length(y)) and times columns.

groups

for numeric y, the number of breaks in the quantiles (see below)

k

an integer for the number of folds.

returnTrain

a logical. When true, the values returned are the sample positions corresponding to the data used during training. This argument only works in conjunction with list = TRUE

Value

• A list or matrix of row position integers corresponding to the training data

Details

For bootstrap samples, simple random sampling is used.

For other data splitting, the random sampling is done within the levels of y when y is a factor in an attempt to balance the class distributions within the splits. For numeric y, the sample is split into groups sections based on quantiles and sampling is done within these subgroups. Also, for very small class sizes (

Examples

data(oil)
createDataPartition(oilType, 2)

x <- rgamma(50, 3, .5)
inA <- createDataPartition(x, list = FALSE)

plot(density(x[inA]))
rug(x[inA])

points(density(x[-inA]), type = "l", col = 4)
rug(x[-inA], col = 4)

createResample(oilType, 2)

createFolds(oilType, 10)
createFolds(oilType, 5, FALSE)

createFolds(rnorm(21))