scale.datacggm {cglasso} | R Documentation |
The method function scale.datacggm
centers and/or scales the columns of a numeric matrix storaged in a ‘datacggm
’ object.
## S3 method for class 'datacggm' scale(x, center = TRUE, scale = TRUE)
x |
an object of class ‘ |
center |
either a logical value or numeric-alike vector of length equal to the number of columns of |
scale |
either a logical value or a numeric-alike vector of length equal to the number of columns of |
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 censoring values) of x$X
from their corresponding columns, and if center
is FALSE
, no centering is done. The same is done for x$lo
and x$up
.
The value of scale
determines how column scaling is performed (after centering). If scale
is a numeric-alike vector with length equal to the number of columns of x
, then each column of x$X
is divided by the corresponding value from scale
. If scale
is TRUE
then scaling is done by dividing the (centered) columns of x$X
by their standard deviations if center
is TRUE
, and the root mean square otherwise. If scale
is FALSE
, no scaling is done. The same is done for x$lo
and x$up
.
The root-mean-square for a (possibly centered) column is defined as sqrt(sum(x^2)/(n-1)), where x is a vector of observed values and n is the number of observed 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))
.)
The method function ‘scale.datacggm
’ returns an object of class datacggm
. The numeric centering and scalings used (if any) are returned as attributes "scaled:center"
and "scaled:scale"
.
set.seed(123) n <- 100L p <- 3L mu <- rep(1L, p) X <- rdatacggm(n = n, mu = mu, probr = 0.05, probl = 0.05) centered.X <- scale(X) centered.X attr(centered.X, "scaled:center") attr(centered.X, "scaled:scale")