pbdDMAT (version 0.5-1)

reductions: Arithmetic Reductions: Sums, Means, and Prods

Description

Arithmetic reductions for distributed matrices.

Usage

rowMin(x, ...)
rowMax(x, ...)
colMin(x, ...)
colMax(x, ...)
# S4 method for ddmatrix
rowSums(x, na.rm = FALSE)
# S4 method for ddmatrix
colSums(x, na.rm = FALSE)
# S4 method for ddmatrix
rowMeans(x, na.rm = FALSE)
# S4 method for ddmatrix
colMeans(x, na.rm = FALSE)
# S4 method for ddmatrix
rowMin(x, na.rm = FALSE)
# S4 method for matrix
rowMin(x, na.rm = FALSE)
# S4 method for ddmatrix
colMin(x, na.rm = FALSE)
# S4 method for matrix
colMin(x, na.rm = FALSE)
# S4 method for ddmatrix
rowMax(x, na.rm = FALSE)
# S4 method for matrix
rowMax(x, na.rm = FALSE)
# S4 method for ddmatrix
colMax(x, na.rm = FALSE)
# S4 method for matrix
colMin(x, na.rm = FALSE)

Arguments

x

numeric distributed matrix

...

additional arguments

na.rm

logical. Should missing (including NaN) be removed?

Value

Returns a global numeric vector.

Details

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.