colSdDiffs,dgCMatrix-method {sparseMatrixStats} | R Documentation

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

### Description

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

### Usage

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

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

### 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.

### Details

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

### Value

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

### See Also

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

- for the direct standard deviation see `rowSds()`.

### Examples

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

      print(mat)

      rowSdDiffs(mat)
      colSdDiffs(mat)

[Package sparseMatrixStats version 1.2.1 Index]