colWeightedMads-dgCMatrix-method: Calculates the weighted median absolute deviation for each...
In sparseMatrixStats: Summary Statistics for Rows and Columns of Sparse Matrices

Description Usage Arguments Details Value See Also Examples

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

## S4 method for signature 'dgCMatrix'
colWeightedMads(
  x,
  w = NULL,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  constant = 1.4826,
  center = NULL
)

## S4 method for signature 'dgCMatrix'
rowWeightedMads(
  x,
  w = NULL,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  constant = 1.4826
)

`x`	An NxK matrix-like object.
`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`, `NA`s are excluded first, otherwise not.
`constant`	A scale factor. See `stats::mad()` for details.
`center`	Not supported at the moment.

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

Returns a numeric vector of length N (K).

matrixStats::rowWeightedMads() and matrixStats::colWeightedMads() which are used when the input is a matrix or numeric vector.
See also rowMads for the corresponding unweighted function.

  mat <- matrix(0, nrow=10, ncol=5)
  mat[sample(prod(dim(mat)), 25)] <- rpois(n=25, 5)
  sp_mat <- as(mat, "dgCMatrix")
  weights <- rnorm(10, mean=1, sd=0.1)

  # sparse version
  sparseMatrixStats::colWeightedMads(sp_mat, weights)

  # Attention the result differs from matrixStats
  # because it always uses 'interpolate=FALSE'.
  matrixStats::colWeightedMads(mat, weights)