# colWeightedVars-xgCMatrix-method: Calculates the weighted variance for each row (column) of a... In sparseMatrixStats: Summary Statistics for Rows and Columns of Sparse Matrices

## Description

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

## Usage

 ```1 2 3 4 5``` ```## S4 method for signature 'xgCMatrix' colWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE) ## S4 method for signature 'xgCMatrix' rowWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE) ```

## Arguments

 `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.

## Details

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

## Value

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

• `matrixStats::rowWeightedVars()` and `matrixStats::colWeightedVars()` which are used when the input is a `matrix` or `numeric` vector.
 ```1 2 3 4 5 6 7 8 9``` ```mat <- matrix(rnorm(15), nrow = 5, ncol = 3) mat[2, 1] <- NA mat[3, 3] <- Inf mat[4, 1] <- 0 print(mat) w <- rnorm(n = 5, mean = 3) rowWeightedVars(mat, w = w[1:3]) colWeightedVars(mat, w = w) ```