The value of `center`

determines how column centering is
performed. If `center`

is a numeric-alike 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 `NA`

s) 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-alike 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))`

.)