Edist.meas: Compute the Expected Distance in Measure (EDM) between a...
In AlessandroCasa/BsMc: Bandwidth selection for modal clustering

Description Usage Arguments Value References

Compute the Expected Distance in Measure (EDM) between a kernel estimator-induced partition and the population one defined by a K-component normal mixture density over a grid of bandwidths.

1 2	Edist.meas(n = 100, hmin = 0.05, hmax = 1, byh = hmin, mus = 0, sigmas = 1, props = 1, plot = TRUE, B = 100, verbose = TRUE)

`n`	sample size of the simulated Monte Carlo samples.
`hmin`	lower value for the grid of bandwidths.
`hmax`	upper value for the grid of bandwidths.
`byh`	increment of the grid sequence
`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 curve of the EDM as a function of bandwidth values in the grid is displayed along with the single DM for each Monte Carlo sample.
`B`	the number of Monte Carlo samples.
`verbose`	if TRUE, the computational progression is displayed.