wt_dist {configural} | R Documentation |
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.
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")
)
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"). |
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}
.
A weighted mean and variance if weights are supplied or an unweighted mean and variance if weights are not supplied.
Jeffrey A. Dahlke
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))