Standardization of Data Matrices

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.

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").

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.

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

Bjørn-Helge Mevik and Ron Wehrens

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