preProcess: Pre-Processing of Predictors

Description

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

Usage

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

Arguments

a matrix or data frame

method

a character vector specifying the type of processing. Possible values are "center", "scale", "pca" and "spartialSign"

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

...

Additional arguments (currently this argument is not used)

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

Details

The operations are applied in this order: centering, scaling, PCA and spatial sign. If PCA is requested but scaling is not, the values will still be scaled.

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

Examples

Run this code

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])