model.selection.R {ClustMMDD} | R Documentation |
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.
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 |
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, 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.
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