Arithmetic reductions for distributed matrices.
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rowMax(x, ...)
colMin(x, ...)
colMax(x, ...)
## S4 method for signature 'ddmatrix'
rowSums(x, na.rm = FALSE)
## S4 method for signature 'ddmatrix'
colSums(x, na.rm = FALSE)
## S4 method for signature 'ddmatrix'
rowMeans(x, na.rm = FALSE)
## S4 method for signature 'ddmatrix'
colMeans(x, na.rm = FALSE)
## S4 method for signature 'ddmatrix'
rowMin(x, na.rm = FALSE)
## S4 method for signature 'matrix'
rowMin(x, na.rm = FALSE)
## S4 method for signature 'ddmatrix'
colMin(x, na.rm = FALSE)
## S4 method for signature 'matrix'
colMin(x, na.rm = FALSE)
## S4 method for signature 'ddmatrix'
rowMax(x, na.rm = FALSE)
## S4 method for signature 'matrix'
rowMax(x, na.rm = FALSE)
## S4 method for signature 'ddmatrix'
colMax(x, na.rm = FALSE)
## S4 method for signature 'matrix'
colMin(x, na.rm = FALSE)

x 
numeric distributed matrix 
... 
additional arguments 
na.rm 
logical. Should missing (including 
Performs the reduction operation on a distributed matrix.
There are several legitimately new operations, including rowMin()
,
rowMax()
, colMin()
, and colMax()
. These
implementations are not really necessary in R because one can easily (and
reasonably efficiently) do something like
apply(X=x, MARGIN=1L, FUN=min, na.rm=TRUE)
But apply()
on a ddmatrix
is very costly, and should be
used sparingly.
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
.
Returns a global numeric vector.
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