stdize: Standardization of Data Matrices

In pls: Partial Least Squares and Principal Component Regression

Standardization of Data Matrices

Description

Performs standardization (centering and scaling) of a data matrix.

Usage

stdize(x, center = TRUE, scale = TRUE)

## S3 method for class 'stdized'
predict(object, newdata, ...)

## S3 method for class 'stdized'
makepredictcall(var, call)

Arguments

x, newdata	numeric matrices. The data to standardize.
center	logical value or numeric vector of length equal to the number of coloumns of x.
scale	logical value or numeric vector of length equal to the number of coloumns of x.
object	an object inheriting from class "stdized", normally the result of a call to stdize.
...	other arguments. Currently ignored.
var	A variable.
call	The term in the formula, as a call.

Details

makepredictcall.stdized is an internal utility function; it is not meant for interactive use. See makepredictcall for details.

If center is TRUE, x is centered by subtracting the coloumn mean from each coloumn. If center is a numeric vector, it is used in place of the coloumn means.

If scale is TRUE, x is scaled by dividing each coloumn by its sample standard deviation. If scale is a numeric vector, it is used in place of the standard deviations.

Value

Both stdize and predict.stdized return a scaled and/or centered matrix, with attributes "stdized:center" and/or "stdized:scale" the vector used for centering and/or scaling. The matrix is given class c("stdized", "matrix").

Note

stdize is very similar to scale. The difference is that when scale = TRUE, stdize divides the coloumns by their standard deviation, while scale uses the root-mean-square of the coloumns. If center is TRUE, this is equivalent, but in general it is not.

Author(s)

Bjørn-Helge Mevik and Ron Wehrens

See Also

mvr, pcr, plsr, msc, scale

Examples

data(yarn)
## Direct standardization:
Ztrain <- stdize(yarn$NIR[yarn$train,])
Ztest <- predict(Ztrain, yarn$NIR[!yarn$train,])

## Used in formula:
mod <- plsr(density ~ stdize(NIR), ncomp = 6, data = yarn[yarn$train,])
pred <- predict(mod, newdata = yarn[!yarn$train,]) # Automatically standardized

pls documentation built on Sept. 16, 2024, 1:07 a.m.