InfCriteriaCalculation: Calculates Information Criteria

In anjalisilva/TestingProject1: Calculates Information Criteria Values

Description

A function that calculates information criteria given log-likelihood, number of clusters, dimension of dataset, and number of observations, and the probability

Usage

InfCriteriaCalculation(
  loglikelihood,
  nClusters,
  dimensionality,
  observations,
  probability
)

Arguments

loglikelihood	A negative value of class "numeric" indicating the log-likelihood
nClusters	A positive integer indicating the number of clusters
dimensionality	A positive integer indicating the dimensionality of dataset
observations	A positive integer indicating the number of observations
probability	A vector indicating the probability of each cluster

Value

Returns an S3 object of class InfCriteria with results.

• BICresults - A value of class "numeric" indicating BIC value

• AICresults - A value of class "numeric" indicating AIC value

• ICLresults - A value of class "numeric" indicating ICL value

References

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory, New York, NY, USA, pp. 267–281. Springer Verlag. Link

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. Link

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6, 461–464. Link.

Examples

# Using GeneCounts dataset available with package
dim(GeneCounts)

# Calculate information criteria value
InfCriteriaResults <- InfCriteriaCalculation(loglikelihood = -5080,
                                             nClusters = 2,
                                             dimensionality = ncol(GeneCounts),
                                             observations = nrow(GeneCounts),
                                             probability = c(0.5, 0.5))
InfCriteriaResults$BICresults

## Not run: 
# Obtain an external sample RNAseq dataset
library(MBCluster.Seq)
data("Count")
dim(Count)

# Calculate information criteria value
InfCriteriaResults <- InfCriteriaCalculation(loglikelihood = -5080,
                                             nClusters = 2,
                                             dimensionality = ncol(Count),
                                             observations = nrow(Count),
                                             probability = c(0.5, 0.5))
InfCriteriaResults$BICresults

## End(Not run)

anjalisilva/TestingProject1 documentation built on Oct. 22, 2020, 7:05 a.m.