sd()
will compute the standard deviations of the columns, equivalent
to calling apply(x, MARGIN=2, FUN=sd)
(which will work for
distributed matrices, by the way). However, this should be much faster and
use less memory than apply()
. If reduce=FALSE
then the return
is a distributed matrix consisting of one (global) row; otherwise, an
R
vector is returned, with ownership of this vector determined by
proc.dest
.
# S4 method for ddmatrix
sd(x, na.rm = FALSE, reduce = FALSE,
proc.dest = "all")# S4 method for ANY
sd(x, na.rm = FALSE)
numeric distributed matrices.
Logical; if TRUE, then na.exclude()
is called first.
logical or string. See details
Destination process (or 'all') if a reduction occurs
Returns a distributed matrix.