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.
## 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, ...)
- a matrix or data frame
- a character vector specifying the type of processing. Possible values are "center", "scale", "pca" and "spartialSign"
- a cutoff for the cumulative percent of variance to be retained by PCA
- a logical; should missing values be removed from the calculations?
- an object of class
- a matrix or data frame of new data to be pre-processed
- Additional arguments (currently this argument is not used)
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.
preProcessresults in a list with elements
call the function call dim the dimensions of
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
thresh the value of
numComp the number of principal components required of capture the specified amount of variance
Kuhn (2008), ``Building Predictive Models in R Using the caret'' (
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])