colAvgsPerRowSet,dgCMatrix-method {sparseMatrixStats} | R Documentation

## Calculates for each row (column) a summary statistic for equally sized subsets of columns (rows)

### Description

Calculates for each row (column) a summary statistic for equally sized subsets of columns (rows)

### Usage

    ## S4 method for signature 'dgCMatrix'
    colAvgsPerRowSet(
      X,
      W = NULL,
      cols = NULL,
      S,
      FUN = colMeans2,
      ...,
      tFUN = FALSE
    )

    ## S4 method for signature 'dgCMatrix'
    rowAvgsPerColSet(
      X,
      W = NULL,
      rows = NULL,
      S,
      FUN = rowMeans2,
      ...,
      tFUN = FALSE
    )

### Arguments

`X` | An `NxM` matrix-like object.
`W` | An optional numeric `NxM` matrix of weights.
`cols` | A `vector` indicating the subset (and/or columns) to operate over. If `NULL`, no subsetting is done.
`S` | An integer `KxJ` matrix that specifying the `J` subsets. Each column hold `K` column (row) indices for the corresponding subset. The range of values is [1, M] ([1,N]).
`FUN` | A row-by-row (column-by-column) summary statistic function. It is applied to to each column (row) subset of `X` that is specified by `S`.
`...` | Additional arguments passed to `FUN`.
`tFUN` | If `TRUE`, `X` is transposed before it is passed to `FUN`.
`rows` | A `vector` indicating the subset (and/or columns) to operate over. If `NULL`, no subsetting is done.

### Details

**Note**: the handling of missing parameters differs from [matrixStats::colAvgsPerRowSet()]. The 'matrixStats' version always removes ‘NA'’s if there are any in the data. This method however does whatever is passed in the '...' parameter.

### Value

Returns a numeric `JxN` (`MxJ`) matrix.

### See Also

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

### Examples

    mat <- matrix(rnorm(20), nrow = 5, ncol = 4)
      mat[2, 1] <- NA
      mat[3, 3] <- Inf
      mat[4, 1] <- 0

      print(mat)

      S <- matrix(1:ncol(mat), ncol = 2)
      print(S)

      rowAvgsPerColSet(mat, S = S, FUN = rowMeans)
      rowAvgsPerColSet(mat, S = S, FUN = rowVars)

[Package sparseMatrixStats version 1.0.5 Index]