caret (version 4.25)

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

x

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
• call
  the function call
• dim
  the dimensions of x
• mean
  a vector of means (if centering was requested)
• std
  a vector of standard deviations (if scaling or PCA was requested)
• rotation
  a matrix of eigenvectors if PCA was requested
• method
  the value ofmethod
• thresh
  the value ofthresh
• numComp
  the 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.

References

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

See Also

prcomp, spatialSign

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])