quinterpol: Interpolated Quantiles

In plgraphics: User Oriented Plotting Functions

Interpolated Quantiles

Description

This function implements a version of empirical quantiles based on interpolation

Usage

quinterpol(x, probs = c(0.25, 0.5, 0.75), extend = FALSE)

Arguments

x	vector of data determining the quantiles
probs	vector of probabilities defining which quantiles should be produced
extend	logical: Should quantiled be calculated outside the range of the data by linear extrapolation? This may make sense if the sample is small or the data is rounded or grouped or a score.

Details

The empirical quantile function jumps at the data values according to the usual definition. The version of quantiles calculated by 'quinterpol' avoids jumps. It is based on linear interpolation of the step version of the empirical cumulative distribution function, using as the given points the midpoints of both vertical and horizontal pieces of the latter. See 'examples' for a visualization.

Value

vector of quantiles

Author(s)

Werner A. Stahel

See Also

quantile

Examples

## This example illustrates the definition of the "interpolated quantiles"

set.seed(2)
t.x <- sort(round(2*rchisq(20,2)))
table(t.x)
t.p <- ppoints(100)
plot(quinterpol(t.x,t.p),t.p, type="l")

plgraphics documentation built on Oct. 19, 2023, 3 p.m.