weighted.quantile: Weighted Sample Quantile

In midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

Weighted Sample Quantile

Description

weighted.quantile() produces weighted sample quantiles corresponding to the given probabilities.

Usage

weighted.quantile(
  x,
  w = NULL,
  probs = seq(0, 1, 0.25),
  na.rm = FALSE,
  names = TRUE,
  digits = 7L,
  type = 1L,
  ...
)

Arguments

x	a numeric vector whose weighted sample quantiles are wanted.
w	a numeric vector of the sample weights for each value in x.
probs	a numeric vector of probabilities with values in [0, 1].
na.rm	logical. If TRUE, any NA and NaNs are removed from x before the quantiles are computed.
names	logical. If TRUE, the result has a "names" attribute.
digits	used only when names is TRUE. The precision to use when formatting the percentages.
type	an integer between 1 and 9 selecting the quantile algorithms. Only 1 is available for the weighted quantile.
...	further arguments passed to stats::quantile() when weights are not passed.

Details

weighted.quantile() is a wrapper function of stats::quantile() for weighted quantiles. For the weighted quantile, only the "type 1" quantile, the inverse of the empirical distribution function, is available. This function is used in numeric.encoder() to enable weights-based encoding.

Value

weighted.quantile() returns weighted sample quantiles corresponding to the given probabilities.

See Also

weighted.tabulate

midr documentation built on Sept. 11, 2025, 1:07 a.m.