colWeightedMeans {DelayedMatrixStats}R Documentation

Calculates the weighted means for each row (column) in a matrix

Description

Calculates the weighted means for each row (column) in a matrix.

Usage

colWeightedMeans(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE,
  ...)

rowWeightedMeans(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE,
  ...)

## 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).

rows

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

cols

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

na.rm

If TRUE, missing values are excluded from the calculation, otherwise not.

...

Additional arguments passed to specific methods.

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 getOption("DelayedArray.block.size")) columns (colFoo()) or rows (rowFoo()) into memory as an ordinary base::array.

Details

The implementations of these methods are optimized for both speed and memory. If no weights are given, the corresponding rowMeans()/colMeans() is used.

Value

Returns a numeric vector of length N (K).

See Also

See rowMeans() and colMeans() in colSums() for non-weighted means. See also weighted.mean.

Examples

# 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)
[Package DelayedMatrixStats version 1.2.0 Index]