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.
|Date of publication||2016-05-30 20:26:19|
|Maintainer||Wilson Toussile <firstname.lastname@example.org>|
|License||GPL (>= 2)|
backward.explorer: Gather a set of the most competitive models.
ClustMMDD-package: 'ClustMMDD' : Clustering by Mixture Models for Discrete Data.
cutEachCol: Retrieve data from strings in the dataset.
dataR2C: Transform a (normal) data frame to be compatible with...
dimJump.R: Data driven calibration of the penalty function
em.cluster.R: Compute estimates of the parameters by Expectation and...
EmOptions: Display the current Expectation and Maximization options.
exModelKS: An example of 'modelKS'.
genotype1: 'genotype1' is a data frame of genotype data with 'ploidy =...
genotype2: A genotype data frame compatible with 'ClustMMDD' main...
genotype2_ExploredModels: A data frame of competing models gathered by...
is.element-methods: Check if a 'modelKS' object is in a set of such objects.
isInFile.R: Find a model in a file.
is.modelKS-methods: Is an object from class 'modelKS'?
modelKS-class: 'modelKS' is a class of parameters of (K, S) model.
model-methods: Retrieve a list of model <=ft(K,S\right) from a 'modelKS'...
model.selection.R: Selection of both the number K of clusters and the subset S...
Rcpp_modules_examples: Functions and Objects created by Rcpp Modules Example
read.modelKS-methods: Read the parameters of a model <=ft(K,S\right) from a file.
read.or.compute: Read a given model from a file or compute the estimates of...
selectK.R: Selection of the number K of clusters.
setEmOptions: Set Expectation and Maximization options.
setModelKS-methods: Set an instance of class 'modelKS' from a list.
show-methods: 'show' method for an object of class 'modelKS'
simulData-methods: Simulate a dataset from a given set of parameters in an...
z==-methods: Methods for Function '=='
z[_--methods: Get or set a slot from 'modelKS'.
z[-methods: Get a slot from 'modelKS'.