matrixStats: weightedVar – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

matrixStats

weightedVar

Weighted variance and weighted standard deviation

Description

Computes a weighted variance / standard deviation of a numeric vector or across rows or columns of a matrix.

Usage

weightedVar(x, w = NULL, idxs = NULL, na.rm = FALSE, center = NULL, ...)

weightedSd(...)

rowWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ...)

colWeightedVars(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ...)

rowWeightedSds(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ...)

colWeightedSds(x, w = NULL, rows = NULL, cols = NULL, na.rm = FALSE, ...)

Arguments

`x`	a `numeric` `vector` containing the values whose weighted variance is to be computed.
`w`	a vector of weights the same length as `x` giving the weights to use for each element of `x`. Negative weights are treated as zero weights. Default value is equal weight to all values.
`idxs, rows, cols`	A `vector` indicating subset of elements (or rows and/or columns) to operate over. If `NULL`, no subsetting is done.
`na.rm`	a logical value indicating whether `NA` values in `x` should be stripped before the computation proceeds, or not. If `NA`, no check at all for `NA`s is done. Default value is `NA` (for efficiency).
`center`	Optional `numeric` scalar specifying the center location of the data. If `NULL`, it is estimated from data.
`...`	Not used.

Details

The estimator used here is the same as the one used by the "unbiased" estimator of the Hmisc package. More specifically, weightedVar(x, w = w) == Hmisc::wtd.var(x, weights = w),

Value

Returns a numeric scalar.

Missing values

This function handles missing values consistently with weightedMean(). More precisely, if na.rm = FALSE, then any missing values in either x or w will give result NA_real_. If na.rm = TRUE, then all (x, w) data points for which x is missing are skipped. Note that if both x and w are missing for a data points, then it is also skipped (by the same rule). However, if only w is missing, then the final results will always be NA_real_ regardless of na.rm.

Author(s)

Henrik Bengtsson