preProcess
From caret v4.25
by Max Kuhn
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, ...)
## 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)
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.
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
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])
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