colWeightedMeans: Calculates the weighted mean for each row (column) of a...

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Calculates the weighted mean for each row (column) of a matrix-like object.

Usage

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## S4 method for signature 'DelayedMatrix'
colWeightedMeans(
  x,
  w = NULL,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  force_block_processing = FALSE,
  ...
)

## S4 method for signature 'DelayedMatrix'
rowWeightedMeans(
  x,
  w = NULL,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  force_block_processing = FALSE,
  ...
)

Arguments

x

A NxK DelayedMatrix.

w

A numeric vector of length K (N) that specifies by how much each element is weighted.

rows

A vector indicating the subset of rows (and/or columns) to operate over. If NULL, no subsetting is done.

cols

A vector indicating the subset of rows (and/or columns) to operate over. If NULL, no subsetting is done.

na.rm

If TRUE, NAs are excluded first, otherwise not.

force_block_processing

FALSE (the default) means that a seed-aware, optimised method is used (if available). This can be overridden to use the general block-processing strategy by setting this to TRUE (typically not advised). The block-processing strategy loads one or more (depending on \link[DelayedArray]{getAutoBlockSize}()) columns (colFoo()) or rows (rowFoo()) into memory as an ordinary base::array.

...

Additional arguments passed to specific methods.

Details

The S4 methods for x of type matrix, array, or numeric call matrixStats::rowWeightedMeans / matrixStats::colWeightedMeans.

Value

Returns a numeric vector of length N (K).

Author(s)

Peter Hickey

See Also

Examples

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# A DelayedMatrix with a 'Matrix' seed
dm_Matrix <- DelayedArray(Matrix::Matrix(c(rep(1L, 5),
                                           as.integer((0:4) ^ 2),
                                           seq(-5L, -1L, 1L)),
                                         ncol = 3))

colWeightedMeans(dm_Matrix)
# Specifying weights inversely proportional to rowwise variances
colWeightedMeans(dm_Matrix, w = 1 / rowVars(dm_Matrix))
rowWeightedMeans(dm_Matrix, w = 1:3)

DelayedMatrixStats documentation built on Feb. 5, 2021, 2:04 a.m.