`scale`

is generic function whose default method centers and/or
scales the columns of a numeric matrix.

`scale(x, center = TRUE, scale = TRUE)`

x

a numeric matrix(like object).

center

either a logical value or a numeric vector of length
equal to the number of columns of `x`

.

scale

either a logical value or a numeric vector of length
equal to the number of columns of `x`

.

For `scale.default`

, the centered, scaled matrix. The numeric
centering and scalings used (if any) are returned as attributes
`"scaled:center"`

and `"scaled:scale"`

The value of `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 `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 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))`

.)

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*.
Wadsworth & Brooks/Cole.

`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