Covariance and Correlation
sd() will compute the standard deviations of the columns, equivalent
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
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
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numeric distributed matrices.
Logical; if TRUE, then
logical or string. See details
Destination process (or 'all') if a reduction occurs
Returns a distributed matrix.
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