generator.otrimle: Generates random data from OTRIMLE output model

In otrimle: Robust Model-Based Clustering

Description

This uses data and the output of otrimle or rimle to generate a new artificial dataset of the size of the original data using noise and cluster proportions from the clustering output. The clusters are then generated from multivariate normal distributions with the parameters estimated by otrimle, the noise is generated resampling from what is estimated as moise component with weights given by posterior probabilities of all observations to be noise. See Hennig and Coretto (2021).

Usage

  generator.otrimle(data, fit)

Arguments

data	something that can be coerced into a matrix. Dataset.
fit	output object of otrimle or rimle.

Value

A list with components data, clustering.

data	matrix of generated data.
cs	vector of integers. Clustering indicator.

Author(s)

Christian Hennig [e-mail] https://www.unibo.it/sitoweb/christian.hennig/en/

References

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.

See Also

kerndensp, kerndensmeasure, otrimle, rimle

Examples

   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)
   str(generator.otrimle(x, ox))

otrimle documentation built on May 29, 2021, 9:09 a.m.