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 "slopeheuristics" (see backward.explorer
, dimJump.R
),
and α is a coefficient in [1.5, 2] to be given by the user.
1 2 
fileOrData 
A character string or a data frame (see 
cte 
A penalty function parameter. The associated criterion is log(likelihood)+cte*dim. 
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 
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, 23442371, ISSN.
Wilson Toussile and Elisabeth Gassiat (2009) : Variable selection in modelbased clustering using multilocus genotype data. Adv Data Anal Classif, Vol 3, number 2, 109134.
1 2 3 4 5  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

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