Standardization of Data Matrices
Performs standardization (centering and scaling) of a data matrix.
stdize(x, center = TRUE, scale = TRUE) ## S3 method for class 'stdized' predict(object, newdata, ...) ## S3 method for class 'stdized' makepredictcall(var, call)
x, newdata |
numeric matrices. The data to standardize. |
center |
logical value or numeric vector of length equal to the
number of coloumns of |
scale |
logical value or numeric vector of length equal to the
number of coloumns of |
object |
an object inheriting from class |
var |
A variable. |
call |
The term in the formula, as a call. |
... |
other arguments. Currently ignored. |
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.
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").
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.
Bjørn-Helge Mevik and Ron Wehrens
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
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