simulData-methods

simulData

object

The number of columns for each variable in the data. For example, $ploidy = 2$ for genotypic data from diploid individual.

ploidy


Simulate a dataset from a given instance of <code>modelKS</code> containing a set of parameters.


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.

Wilson Toussile

ClustMMDD

Variable Selection in Clustering by Mixture Models for Discrete
Data

simulData-methods function

<dl> <dt><code>signature(object = "modelKS", N = "numeric", ploidy = "numeric")</code></dt><dd>Simulate a dataset for a given set of parameters in a <code>modelKS</code> object.</dd> </dl>

simulData-methods: Simulate a dataset from a given set of parameters in an instance of `modelKS`.

Description

Arguments

Value

Methods

See Also

Examples