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caret (version 6.0-94)

# 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 while `createTimeSlices` creates cross-validation split for series data. `groupKFold` splits the data based on a grouping factor.

## Usage

```createDataPartition(
y,
times = 1,
p = 0.5,
list = TRUE,
groups = min(5, length(y))
)createFolds(y, k = 10, list = TRUE, returnTrain = FALSE)createMultiFolds(y, k = 10, times = 5)createTimeSlices(y, initialWindow, horizon = 1, fixedWindow = TRUE, skip = 0)groupKFold(group, k = length(unique(group)))createResample(y, times = 10, list = TRUE)```

## Value

A list or matrix of row position integers corresponding to the training data. For `createTimeSlices` subsamples are named by the end index of each training subsample.

## Arguments

y

a vector of outcomes. For `createTimeSlices`, these should be in chronological order.

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`

initialWindow

The initial number of consecutive values in each training set sample

horizon

the number of consecutive values in test set sample

fixedWindow

logical, if `FALSE`, all training samples start at 1

skip

integer, how many (if any) resamples to skip to thin the total amount

group

a vector of groups whose length matches the number of rows in the overall data set.

## Author

Max Kuhn, `createTimeSlices` by Tony Cooper

## 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 percentiles and sampling is done within these subgroups. For `createDataPartition`, the number of percentiles is set via the `groups` argument. For `createFolds` and `createMultiFolds`, the number of groups is set dynamically based on the sample size and `k`. For smaller samples sizes, these two functions may not do stratified splitting and, at most, will split the data into quartiles.

Also, for `createDataPartition`, very small class sizes (<= 3) the classes may not show up in both the training and test data

For multiple k-fold cross-validation, completely independent folds are created. The names of the list objects will denote the fold membership using the pattern "Foldi.Repj" meaning the ith section (of k) of the jth cross-validation set (of `times`). Note that this function calls `createFolds` with `list = TRUE` and `returnTrain = TRUE`.

Hyndman and Athanasopoulos (2013)) discuss rolling forecasting origin techniques that move the training and test sets in time. `createTimeSlices` can create the indices for this type of splitting.

For Group k-fold cross-validation, the data are split such that no group is contained in both the modeling and holdout sets. One or more group could be left out, depending on the value of `k`.

## References

http://topepo.github.io/caret/data-splitting.html

Hyndman and Athanasopoulos (2013), Forecasting: principles and practice. https://otexts.com/fpp2/

## Examples

Run this code
``````
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))

createTimeSlices(1:9, 5, 1, fixedWindow = FALSE)
createTimeSlices(1:9, 5, 1, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = TRUE)
createTimeSlices(1:9, 5, 3, fixedWindow = FALSE)

createTimeSlices(1:15, 5, 3)
createTimeSlices(1:15, 5, 3, skip = 2)
createTimeSlices(1:15, 5, 3, skip = 3)

set.seed(131)
groups <- sort(sample(letters[1:4], size = 20, replace = TRUE))
table(groups)
folds <- groupKFold(groups)
lapply(folds, function(x, y) table(y[x]), y = groups)
``````

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