preProcess
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 asn.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 elementscall 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 of method
thresh the value of thresh
numComp the number of principal components required of capture the specified amount of variance ica contains values for the W
andK
matrix of the decomposition
References
Kuhn (2008), ``Building Predictive Models in R Using the caret'' (
See Also
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])