preProcess

From caret v4.65 by Max Kuhn

Pre-Processing of Predictors

Pre-processing transformation (centering, scaling etc) can be estimated from the training data and applied to any data set with the same variables.

Keywords: utilities

Usage

preProcess(x, ...)
## S3 method for class 'default':
preProcess(x, 
           method = c("center", "scale"), 
           thresh = 0.95, 
           na.remove = TRUE, 
           k = 5,
           knnSummary = mean,
           ...)
## S3 method for class 'preProcess':
predict(object, newdata, ...)

Arguments

x: a matrix or data frame. All variables must be numeric.
method: a character vector specifying the type of processing. Possible values are "center", "scale", "knnImpute", "pca" "ica" and "spatialSign" (see Details below)
thresh: a cutoff for the cumulative percent of variance to be retained by PCA
na.remove: a logical; should missing values be removed from the calculations?
object: an object of class preProcess
newdata: a matrix or data frame of new data to be pre-processed
k: the number of nearest neighbors from the training set to use for imputation
knnSummary: function to average the neighbor values per column during imputation
...: additional arguments to pass to fastICA, such as n.comp

Details

The operations are applied in this order: imputation, centering, scaling, PCA, ICA then spatial sign.

If PCA is requested but scaling is not, the values will still be scaled. Similarly, when ICA is requested, the data are automatically centered.

$k$-nearest neighbor imputation is carried out by finding the k closest samples (Euclidian distance) in the training set.

A warning is thrown if both PCA and ICA are requested. ICA, as implemented bt the fastICA package automatically does a PCA decomposition prior to finding the ICA scores.

The function will throw an error of any variables in x has less than two unique values.

Value

preProcess results in a list with elements
callthe function call
dimthe dimensions of x
meana vector of means (if centering was requested)
stda vector of standard deviations (if scaling or PCA was requested)
rotationa matrix of eigenvectors if PCA was requested
methodthe value ofmethod
threshthe value ofthresh
numCompthe number of principal components required of capture the specified amount of variance
icacontains values for the W and K matrix of the decomposition

References

Kuhn (2008), ``Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/v28/i05/)

Examples

data(BloodBrain)
# one variable has one unique value
preProc <- preProcess(bbbDescr[1:100,])

preProc <- preProcess(bbbDescr[1:100,-3])
training <- predict(preProc, bbbDescr[1:100,-3])
test <- predict(preProc, bbbDescr[101:208,-3])

Documentation reproduced from package caret, version 4.65, License: GPL-2

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