caret (version 4.69)

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, 
           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", "bagImpute", "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

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
• ica
  contains values for the W and K matrix of the decomposition

Details

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

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

$k$-nearest neighbor imputation is carried out by finding the k closest samples (Euclidian distance) in the training set. Imputation via bagging fits a bagged tree model for each predictor (as a function of all the others). This method is simple, accurate and accepts missing values, but it has much higher computational cost.

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.

References

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

See Also

prcomp, fastICA, 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])