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

a numeric vector containing the values whose weighted variance is to be computed.

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 NAs 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.

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.

Details