ICLFunction: Model Selection Via Integrated Completed Likelihood

In anjalisilva/MPLNClust: Mixtures of Multivariate Poisson-Log Normal Model for Clustering Count Data

Model Selection Via Integrated Completed Likelihood

Description

Performs model selection using integrated completed likelihood (ICL) by Biernacki et al., (2000).

Usage

ICLFunction(
  logLikelihood,
  nParameters,
  nObservations,
  clusterRunOutput = NA,
  probaPost = NA,
  gmax,
  gmin,
  parallel = FALSE
)

Arguments

logLikelihood	A vector with value of final log-likelihoods for each cluster size.
nParameters	A vector with number of parameters for each cluster size.
nObservations	A positive integer specifying the number of observations in the dataset analyzed.
clusterRunOutput	Output from MPLNClust::mplnVariational, MPLNClust::mplnMCMCParallel, or MPLNClust::mplnMCMCNonParallel functions. Either clusterRunOutput or probaPost must be provided.
probaPost	A list that is length (gmax - gmin + 1) containing posterior probability at each g, for g = gmin:gmax. This argument is useful if clustering output have been generated non-serially, e.g., g = 1:5 and g = 6:10 rather than g = 1:10. Either clusterRunOutput or probaPost must be provided.
gmax	A positive integer, > gmin, specifying the maximum number of components to be considered in the clustering run.
gmin	A positive integer specifying the minimum number of components to be considered in the clustering run.
parallel	TRUE or FALSE indicating if MPLNClust::mplnMCMCParallel has been used.

Value

Returns an S3 object of class MPLN with results.

• allICLvalues - A vector of ICL values for each cluster size.

• ICLmodelselected - An integer specifying model selected by ICL.

• ICLmodelSelectedLabels - A vector of integers specifying cluster labels for the model selected. Only provided if user input clusterRunOutput.

• ICLMessage - A character vector indicating if spurious clusters are detected. Otherwise, NA.

Author(s)

Anjali Silva, [e-mail]

References

Biernacki, C., G. Celeux, and G. Govaert (2000). Assessing a mixture model for clustering with the integrated classification likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22.

Examples

trueMu1 <- c(6.5, 6, 6, 6, 6, 6)
trueMu2 <- c(2, 2.5, 2, 2, 2, 2)

trueSigma1 <- diag(6) * 2
trueSigma2 <- diag(6)

# Generating simluated data
sampleData <- MPLNClust::mplnDataGenerator(nObservations = 100,
                                           dimensionality = 6,
                                           mixingProportions = c(0.79, 0.21),
                                           mu = rbind(trueMu1, trueMu2),
                                           sigma = rbind(trueSigma1, trueSigma2),
                                           produceImage = "No")

# Clustering
mplnResults <- MPLNClust::mplnVariational(dataset = sampleData$dataset,
                                          membership = sampleData$trueMembership,
                                          gmin = 1,
                                          gmax = 2,
                                          initMethod = "kmeans",
                                          nInitIterations = 2,
                                          normalize = "Yes")

# Model selection
ICLmodel <- MPLNClust::ICLFunction(logLikelihood = mplnResults$logLikelihood,
                                   nParameters = mplnResults$numbParameters,
                                   nObservations = nrow(mplnResults$dataset),
                                   clusterRunOutput = mplnResults$allResults,
                                   gmin = mplnResults$gmin,
                                   gmax = mplnResults$gmax,
                                   parallel = FALSE)

anjalisilva/MPLNClust documentation built on Sept. 19, 2024, 7:34 a.m.