# sd: Covariance and Correlation In pbdDMAT: 'pbdR' Distributed Matrix Methods

## Description

`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`.

## Usage

 ```1 2 3 4 5 6``` ```## 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) ```

## Arguments

 `x` numeric distributed matrices. `na.rm` Logical; if TRUE, then `na.exclude()` is called first. `reduce` logical or string. See details `proc.dest` Destination process (or 'all') if a reduction occurs

## Value

Returns a distributed matrix.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```## 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) ```

pbdDMAT documentation built on May 29, 2017, 1:23 p.m.