pls (version 1.2-1)

stdize: 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, \dots)
## 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.

var

A variable.

call

The term in the formula, as a call.

...

other arguments. Currently ignored.

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").

encoding

latin1

Details

makepredictcall.stdized is an internal utility function; it is noe 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.

See Also

mvr, pcr, plsr, msc, scale

Examples

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

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