A variation of ecdf()
that can be applied to weighted samples.
weighted_ecdf(x, weights = NULL, na.rm = FALSE)
numeric vector: sample values
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.
logical: if TRUE
, corresponding entries in x
and weights
are removed if either is NA
.
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()
.
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 \(w_i\).