colMadDiffs,dgCMatrix-method {sparseMatrixStats} | R Documentation

## Calculates the mean absolute deviation of the difference between each element of a row (column) of a matrix-like object

### Description

Calculates the mean absolute deviation of the difference between each element of a row (column) of a matrix-like object.

### Usage

    ## S4 method for signature 'dgCMatrix'
    colMadDiffs(
      x,
      rows = NULL,
      cols = NULL,
      na.rm = FALSE,
      diff = 1L,
      trim = 0,
      constant = 1.4826
    )

    ## S4 method for signature 'dgCMatrix'
    rowMadDiffs(
      x,
      rows = NULL,
      cols = NULL,
      na.rm = FALSE,
      diff = 1L,
      trim = 0,
      constant = 1.4826
    )

### Arguments

`x` | An NxK matrix-like object.
`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.
`diff` | An integer specifying the order of difference.
`trim` | A double in [0,1/2] specifying the fraction of observations to be trimmed from each end of (sorted) x before estimation.
`constant` | A scale factor. See `mad` for details.

### Details

The S4 methods for `x` of type `matrix`, `array`, or `numeric` call `matrixStats::rowMadDiffs` / `matrixStats::colMadDiffs`.

### Value

Returns a `numeric` `vector` of length N (K).

### See Also

- `matrixStats::rowMadDiffs()` and `matrixStats::colMadDiffs()` which are used when the input is a `matrix` or `numeric` vector.

### Examples

    mat <- matrix(rnorm(15), nrow = 5, ncol = 3)
      mat[2, 1] <- NA
      mat[3, 3] <- Inf
      mat[4, 1] <- 0

      print(mat)

      rowMadDiffs(mat)
      colMadDiffs(mat)

[Package sparseMatrixStats version 1.2.1 Index]