kerndenscluster: Aggregated distance to elliptical unimodal density over...
In otrimle: Robust Model-Based Clustering

Description Usage Arguments Details Value Author(s) References See Also Examples

This calls kerndensp for computing and aggregating density- and principal components-based distances between multivariate data and a unimodal elliptical distribution about the data mean for all clusters in a mixture-based clustering as generated by otrimle or rimle. For use in otrimleg.

1	kerndenscluster(x,fit,maxq=qnorm(0.9995),kernn=100)

`x`	something that can be coerced into a matrix. Dataset.
`fit`	output object of `otrimle` or `rimle`.
`maxq`	positive numeric. One-dimensional densities are evaluated between `mean(x)-maxq` and `mean(x)+maxq`.
`kernn`	integer. Number of points at which the one-dimensional density is evaluated, input parameter `n` of `density`. This should be even.

See Hennig and Coretto (2021), Sec. 4.2. kerndenscluster calls kerndensp for all clusters and aggregates the resulting measures as root sum of squares.

A list with components ddpi, ddpm, measure.

`ddpi`	list of outputs of `kerndensp` for all clusters.
`ddpm`	vector of `measure`-components of `ddpi`.
`measure`	Final aggregation result.

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

Hennig, C. and P.Coretto (2021). An adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture based clustering. To appear in Australian and New Zealand Journal of Statistics, https://arxiv.org/abs/2009.00921.

kerndensp, kerndensmeasure, otrimle, rimle

   data(banknote)
   selectdata <- c(1:30,101:110,117:136,160:161)
   set.seed(555566)
   x <- banknote[selectdata,5:7]
   ox <- otrimle(x, G=2, ncores=1)
   kerndenscluster(x,ox)$measure