### stdize.R: Standardization by centering and scaling######	$Id: stdize.R 89 2006-09-20 15:41:09Z bhm $## This is a somewhat modified version of scale.defaultstdize <- function(x, center = TRUE, scale = TRUE) {    nc <- ncol(x)    if (is.logical(center)) {        if (center) {            center <- colMeans(x, na.rm = TRUE)            x <- sweep(x, 2, center)        }    } else if (is.numeric(center) && length(center) == nc)        x <- sweep(x, 2, center)    else stop("invalid 'center'")    if (is.logical(scale)) {        if (scale) {            ## This is faster than sd(x), but cannot handle missing values:            scale <- sqrt(colSums(sweep(x, 2, colMeans(x))^2) / (nrow(x) - 1))            x <- sweep(x, 2, scale, "/")        }    } else if (is.numeric(scale) && length(scale) == nc)        x <- sweep(x, 2, scale, "/")    else stop("invalid 'scale'")    if (is.numeric(center)) attr(x, "stdized:center") <- center    if (is.numeric(scale)) attr(x, "stdized:scale") <- scale    class(x) <- c("stdized", "matrix")    return(x)}## This is not really needed for `stdize' to work with formulas, but might## be nice to have for manually manipulating data:predict.stdized <- function(object, newdata, ...) {    if (missing(newdata)) return(object)    if (is.null(center <- attr(object, "stdized:center")))        center <- FALSE    if (is.null(scale <- attr(object, "stdized:scale")))        scale <- FALSE    stdize(newdata, center = center, scale = scale)}## This method makes things like## `predict(plsr(y ~ stdize(X), data = foo), newdata = bar)' work.## This is a slightly modified version of makepredictcall.default.makepredictcall.stdized <- function(var, call) {    if (as.character(call)[1] != "stdize")        return(call)    if (!is.null(z <- attr(var, "stdized:center")))        call$center <- z    if (!is.null(z <- attr(var, "stdized:scale")))        call$scale <- z    call}