covariance {pbdDMAT} | R Documentation |
cov()
and var()
form the variance-covariance matrix. cor()
forms
the correlation matrix. cov2cor()
scales a covariance matrix into a
correlation matrix.
## S4 method for signature 'ddmatrix' cov(x, y = NULL, use = "everything", method = "pearson") ## S4 method for signature 'ddmatrix' var(x, y = NULL, na.rm = FALSE, use) ## S4 method for signature 'ddmatrix' cor(x, y = NULL, use = "everything", method = "pearson") ## S4 method for signature 'ddmatrix' cov2cor(V)
x, y, V |
numeric distributed matrices. |
use |
character indicating how missing values should be treated.
Acceptable values are the same as |
method |
character argument indicating which method should be used to
calculate covariances. Currently only "spearman" is available for
|
na.rm |
logical, determines whether or not |
cov()
forms the variance-covariance matrix. Only
method="pearson"
is implemented at this time.
var()
is a shallow wrapper for cov()
in the case of a
distributed matrix.
cov2cor()
scales a covariance matrix into a correlation matrix.
Returns a distributed matrix.
spmd.code = " library(pbdDMAT, quiet = TRUE) init.grid() x <- ddmatrix('rnorm', nrow=3, ncol=3), bldim=2 cv <- cov(x) cv finalize() " pbdMPI::execmpi(spmd.code = spmd.code, nranks = 2L)