dist.meas: Compute the Distance in Measure between the clustering...

In AlessandroCasa/BsMc: Bandwidth selection for modal clustering

Description

Compute the Distance in Measure between the clustering induced by a kernel density estimator (based on a sample x and a bandwidth h) and the population clustering defined by a K-component normal mixture density.

Usage

dist.meas(x, h, mus = 0, sigmas = 1, props = 1, plot = FALSE)

Arguments

x	(vector) the data to be partitioned.
h	the bandwidth to be used to estimate the density via KDE.
mus	vector of means of the mixture components.
sigmas	vector of standard deviations of the mixture components.
props	vector of mixing proportions of the mixture components.
plot	if true, the true density and the estimated one are displayed.

Value

the value of the Distance in Measure.

References

Chacón, J.E. (2015). A population background for nonparametric density-based clustering. Statistical Science 30(4): 518-532.

AlessandroCasa/BsMc documentation built on Oct. 30, 2019, 4:49 a.m.