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 1.0.3 Index]