| sd {pbdDMAT} | R Documentation |
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 signature 'ddmatrix' sd(x, na.rm = FALSE, reduce = FALSE, proc.dest = "all") ## S4 method for signature 'ANY' sd(x, na.rm = FALSE)
x |
numeric distributed matrices. |
na.rm |
Logical; if TRUE, then |
reduce |
logical or string. See details |
proc.dest |
Destination process (or 'all') if a reduction occurs |
Returns a distributed matrix.
## Not run:
# Save code in a file "demo.r" and run with 2 processors by
# > mpiexec -np 2 Rscript demo.r
library(pbdDMAT, quiet = TRUE)
init.grid()
x <- ddmatrix("rnorm", nrow=3, ncol=3)
cv <- cov(x)
print(cv)
finalize()
## End(Not run)