wt_dist {configural}

Weighted descriptive statistics for a vector of numbers

Description

Compute the weighted mean and variance of a vector of numeric values. If no weights are supplied, defaults to computing the unweighted mean and the unweighted maximum-likelihood variance.

Usage

wt_dist(
  x,
  wt = rep(1, length(x)),
  unbiased = TRUE,
  df_type = c("count", "sum_wts")
)

wt_mean(x, wt = rep(1, length(x)))

wt_var(
  x,
  wt = rep(1, length(x)),
  unbiased = TRUE,
  df_type = c("count", "sum_wts")
)

Arguments

x

Vector of values to be analyzed.

wt

Weights associated with the values in x.

unbiased

Logical scalar determining whether variance should be unbiased (TRUE) or maximum-likelihood (FALSE).

df_type

Character scalar determining whether the degrees of freedom for unbiased estimates should be based on numbers of cases ("count"; default) or sums of weights ("sum_wts").

Details

The weighted mean is computed as

\bar{x}_{w}=\frac{\Sigma_{i=1}^{k}x_{i}w_{i}}{\Sigma_{i=1}^{k}w_{i}}

where x is a numeric vector and w is a vector of weights.

The weighted variance is computed as

var_{w}(x)=\frac{\Sigma_{i=1}^{k}\left(x_{i}-\bar{x}_{w}\right)^{2}w_{i}}{\Sigma_{i=1}^{k}w_{i}}

and the unbiased weighted variance is estimated by multiplying var_{w}(x) by \frac{k}{k-1}.

Value

A weighted mean and variance if weights are supplied or an unweighted mean and variance if weights are not supplied.

Author(s)

Jeffrey A. Dahlke

Examples

wt_dist(x = c(.1, .3, .5), wt = c(100, 200, 300))
wt_mean(x = c(.1, .3, .5), wt = c(100, 200, 300))
wt_var(x = c(.1, .3, .5), wt = c(100, 200, 300))