A vector of length n where the kth entry is the kth weighted
moment of x with weights w. If center is TRUE the moments are
centralized, i.e. E((X - E(X))^k). The first moment is never centralized.
The moments are scaled with 1 / sum(w), so they are not de-biased.
e.g. the second central weighted moment
weighted_moment(x, w)[2L]
is equal to
var(rep(x, w)) * (sum(w) - 1) / sum(w)
for integer w
Arguments
x
Observations
w
Case weights (optional)
n
Number of moments to calculate
center
Calculate centralized moments (default) or noncentralized
moments, i.e. E((X - E(X))^k) or E(X^k).
See Also
Other weighted statistics:
weighted_quantile(),
weighted_tabulate()