genotype2_ExploredModels

A data frame of competing models gathered by <code><a href="/link/backward.explorer?package=ClustMMDD&version=1.0.3" rd-options="" data-mini-rdoc="ClustMMDD::backward.explorer">backward.explorer</a></code> for $Kmax = 10$. Such data file can be used for a final model selection process.

Backward-Stepwise selection

Model selection in clustering

Population structure

An implementation of a variable selection procedure in clustering by mixture of multinomial models for discrete data. 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. 

Wilson Toussile

ClustMMDD

Variable Selection in Clustering by Mixture Models for Discrete
        Data

genotype2_ExploredModels function

source

A data frame of competing models gathered by <code><a href="/link/backward.explorer?package=ClustMMDD&version=1.0.3" rd-options="" data-mini-rdoc="ClustMMDD::backward.explorer">backward.explorer</a></code>.

genotype2_ExploredModels: A data frame of competing models gathered by `backward.explorer`.

Description

Usage

Arguments

source

Details

See Also

Examples