pls (version 2.1-0)

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)

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 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.

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