scale is generic function whose default method centers and/or
scales the columns of a numeric matrix.
scale(x, center = TRUE, scale = TRUE)x.x.center determines how column centering is
performed. If center is a numeric vector with length equal to
the number of columns of x, then each column of x has
the corresponding value from center subtracted from it. If
center is TRUE then centering is done by subtracting the
column means (omitting NAs) of x from their
corresponding columns, and if center is FALSE, no
centering is done. The value of scale determines how column scaling is performed
(after centering). If scale is a numeric vector with length
equal to the number of columns of x, then each column of
x is divided by the corresponding value from scale.
If scale is TRUE then scaling is done by dividing the
(centered) columns of x by their standard deviations if
center is TRUE, and the root mean square otherwise.
If scale is FALSE, no scaling is done.
The root-mean-square for a (possibly centered) column is defined as
$sqrt(sum(x^2)/(n-1))$, where $x$ is
a vector of the non-missing values and $n$ is the number of
non-missing values. In the case center = TRUE, this is the
same as the standard deviation, but in general it is not. (To scale
by the standard deviations without centering, use
scale(x, center = FALSE, scale = apply(x, 2, sd, na.rm = TRUE)).)
sweep which allows centering (and scaling) with
arbitrary statistics. For working with the scale of a plot, see par.
require(stats)
x <- matrix(1:10, ncol = 2)
(centered.x <- scale(x, scale = FALSE))
cov(centered.scaled.x <- scale(x)) # all 1
Run the code above in your browser using DataLab