model.selection.R: Selection of both the number K of clusters and the subset S...
In ClustMMDD: Variable Selection in Clustering by Mixture Models for Discrete Data

Description Usage Arguments Value Author(s) References See Also Examples

The inference on both the number K of clusters and the subset S of clustering variables is seen as a model selection problem. Each competing model is characterized by one value of ≤ft(K,S\right). The competing models are compared using penalized criteria AIC, BIC, ICL and a more general penalized criterion with a penalty function on the form

pen≤ft(K,S\right)=α*λ*dim≤ft(K,S\right),

where

λ is a parameter that can be calibrated using "slope-heuristics" (see backward.explorer, dimJump.R),
and α is a coefficient in [1.5, 2] to be given by the user.

1 2	model.selection.R(fileOrData, cte = as.double(1), alpha = as.double(2.0), header = TRUE, lines = integer())

`fileOrData`	A character string or a data frame (see `backward.explorer`). If `fileOrData` is a data frame, it must contains a column named `logLik` and another named dim (see details).
`cte`	A penalty function parameter. The associated criterion is -log(likelihood)+ctedim*.
`alpha`	A coefficient in [1.5,2]. The default value is 2.
`header`	Indication of the presence of header in the file.
`lines`	A vector of integer. If not empty and `fileOrData` is the name of a file, only models defined in `lines` are compared.

A data frame of the selected models for the proposed penalized criteria.

Wilson Toussile

Dominique Bontemps and Wilson Toussile (2013) : Clustering and variable selection for categorical multivariate data. Electronic Journal of Statistics, Volume 7, 2344-2371, ISSN.
Wilson Toussile and Elisabeth Gassiat (2009) : Variable selection in model-based clustering using multilocus genotype data. Adv Data Anal Classif, Vol 3, number 2, 109-134.

backward.explorer, dimJump.R.

data(genotype2_ExploredModels)
outDimJump = dimJump.R(genotype2_ExploredModels, N = 1000, h = 5, header = TRUE)
cte1 = outDimJump[[1]][1]
outSlection = model.selection.R(genotype2_ExploredModels, cte = cte1, header = TRUE)
outSlection