isInFile.R: Find a model in a file.

In ClustMMDD: Variable Selection in Clustering by Mixture Models for Discrete Data

Description

Find a given model defined by (K, S) in a file.

Usage

isInFile.R(K, S, file, header)

Arguments

K	The number of clusters.
S	A vector of logicals of length equal to the number of variables, that indicates the clustering variables.
file	A file where to find the model.
header	A logical indicating if the file contains a header or not.

Value

A list :

• TrueFalse : A logical indicating if the given model was found and the following if TRUE.

• line : The line where the given is in the file.

• N : The size of the dataset from which the model was estimated.

• logLik : The log-likelihood.

• dim : The dimension of the model = number of free parameters.

• entropy : The entropy associated to estimated parameters of the models.

Author(s)

Wilson Toussile

References

• 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.

Examples

data(genotype1)
head(genotype1) 
genotype2 = cutEachCol(genotype1[, -11], ploidy = 2)
head(genotype2)

S = c(rep(TRUE, 8), rep(FALSE, 2))
## Not run: 
outPut = selectK.R(genotype2, S, Kmax = 6, ploidy = 2, Kmin=1)
isInFile.R(K = 5, S, "genotype2_ExploredModels.txt", header = TRUE)
isInFile.R(K = 5, rep(TRUE, 10), "genotype2_ExploredModels.txt", header = TRUE)

file.remove("genotype2_ExploredModels.txt")

## End(Not run)

ClustMMDD documentation built on May 2, 2019, 2:44 p.m.