weighted_ecdf: Weighted empirical cumulative distribution function

In ggdist: Visualizations of Distributions and Uncertainty

Weighted empirical cumulative distribution function

Description

A variation of ecdf() that can be applied to weighted samples.

Usage

weighted_ecdf(x, weights = NULL, na.rm = FALSE)

Arguments

x	<numeric> Sample values.
weights	<numeric | NULL> Weights for the sample. One of: numeric vector of same length as x: weights for corresponding values in x, which will be normalized to sum to 1. NULL: indicates no weights are provided, so the unweighted empirical cumulative distribution function (equivalent to ecdf()) is returned.
na.rm	<scalar logical> If TRUE, corresponding entries in x and weights are removed if either is NA.

Details

Generates a weighted empirical cumulative distribution function, F(x). Given x, a sorted vector (derived from x), and w_i, the corresponding weight for x_i, F(x) is a step function with steps at each x_i with F(x_i) equal to the sum of all weights up to and including w_i.

Value

weighted_ecdf() returns a function of class "weighted_ecdf", which also inherits from the stepfun() class. Thus, it also has plot() and print() methods. Like ecdf(), weighted_ecdf() also provides a quantile() method, which dispatches to weighted_quantile().

See Also

weighted_quantile()

Examples

weighted_ecdf(1:3, weights = 1:3)
plot(weighted_ecdf(1:3, weights = 1:3))
quantile(weighted_ecdf(1:3, weights = 1:3), 0.4)

ggdist documentation built on June 8, 2025, 10:59 a.m.