ClustMMDD: Variable Selection in Clustering by Mixture Models for Discrete
Data
An implementation of a variable selection procedure in clustering by mixture models for discrete data (clustMMDD). Genotype data are examples of such data with two unordered observations (alleles) at each locus for diploid individual. The two-fold problem of variable selection and clustering is seen as a model selection problem where competing models are characterized by the number of clusters K, and the subset S of clustering variables. Competing models are compared by penalized maximum likelihood criteria. We considered asymptotic criteria such as Akaike and Bayesian Information criteria, and a family of penalized criteria with penalty function to be data driven calibrated.
| Version: |
1.0.4 |
| Depends: |
Rcpp (≥ 0.11.5), R (≥ 3.0.0) |
| Imports: |
methods |
| LinkingTo: |
Rcpp |
| Published: |
2016-05-30 |
| Author: |
Wilson Toussile |
| Maintainer: |
Wilson Toussile <wilson.toussile at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Citation: |
ClustMMDD citation info |
| CRAN checks: |
ClustMMDD results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=ClustMMDD
to link to this page.