weightedVar {matrixStats}

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,
  ..., useNames = TRUE)

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

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

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

Arguments

x

vector of type integer, numeric, or logical.

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

A vector indicating subset of elements to operate over. If NULL, no subsetting is done.

na.rm

If TRUE, missing values are excluded.

center

Optional numeric scalar specifying the center location of the data. If NULL, it is estimated from data.

...

Not used.

rows

A vector indicating subset of rows to operate over. If NULL, no subsetting is done.

cols

A vector indicating subset of columns to operate over. If NULL, no subsetting is done.

useNames

If TRUE (default), names attributes of the result are set, otherwise not.

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

See Also

For the non-weighted variance, see var.