Data Splitting functions
A series of test/training partitions are created using
createResample creates one or
more bootstrap samples.
createFolds splits the data into
k groups while
createTimeSlices creates cross-validation
sample information to be used with time series data.
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) createMultiFolds(y, k = 10, times = 5) createTimeSlices(y, initialWindow, horizon = 1, fixedWindow = TRUE, skip = 0)
- a vector of outcomes. For
createTimeSlices, these should be in chronological order.
- the number of partitions to create
- the percentage of data that goes to training
- logical - should the results be in a list (
TRUE) or a matrix with the number of rows equal to
floor(p * length(y))and
- for numeric
y, the number of breaks in the quantiles (see below)
- an integer for the number of folds.
- 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
- The initial number of consecutive values in each training set sample
- The number of consecutive values in test set sample
- A logical: if
FALSE, the training set always start at the first sample.
- An integer specifying how many (if any) resamples to skip to thin the total amount.
For bootstrap samples, simple random sampling is used.
For other data splitting, the random sampling is done within the
y is a factor in an attempt to balance
the class distributions within the splits.
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
the number of groups is set dynamically based on the sample size and
For smaller samples sizes, these two functions may not do stratified
splitting and, at most, will split the data into quartiles.
createDataPartition, very small class sizes (<= 3)="" the="" classes="" may="" not="" show="" up="" in="" both="" training="" and="" test="" data<="" p="">
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
times). Note that this function calls
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.
A list or matrix of row position integers corresponding to the training
Hyndman and Athanasopoulos (2013), Forecasting: principles and practice. https://www.otexts.org/fpp
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)