mclust (version 4.1)

mclustBIC: BIC for Model-Based Clustering

Description

BIC for parameterized Gaussian mixture models fitted by EM algorithm initialized by model-based hierarchical clustering.

Usage

mclustBIC(data, G = NULL, modelNames = NULL, 
          prior = NULL, control = emControl(), 
          initialization = list(hcPairs = NULL, subset = NULL, noise = NULL), 
          Vinv = NULL, warn = FALSE, x = NULL, ...)

Arguments

data

A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.

G

An integer vector specifying the numbers of mixture components (clusters) for which the BIC is to be calculated. The default is G=1:9, unless the argument x is specified, in which case the default is taken from the

modelNames

A vector of character strings indicating the models to be fitted in the EM phase of clustering. The help file for mclustModelNames describes the available models. The default is: [

prior

The default assumes no prior, but this argument allows specification of a conjugate prior on the means and variances through the function priorControl.

control

A list of control parameters for EM. The defaults are set by the call emControl().

initialization

A list containing zero or more of the following components: [object Object],[object Object],[object Object]

Vinv

An estimate of the reciprocal hypervolume of the data region. The default is determined by applying function hypvol to the data. Used only if an initial guess as to which observations are noise is supplied.

warn

A logical value indicating whether or not certain warnings (usually related to singularity) should be issued when estimation fails. The default is to suppress these warnings.

x

An object of class "mclustBIC". If supplied, mclustBIC will use the settings in x to produce another object of class "mclustBIC", but with G and modelNames as spe

...

Catches unused arguments in indirect or list calls via do.call.

Value

• Bayesian Information Criterion for the specified mixture models numbers of clusters. Auxiliary information returned as attributes.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631. C. Fraley and A. E. Raftery (2005). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington.

C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

priorControl, emControl, mclustModel, summary.mclustBIC, hc, me, mclustModelNames, mclust.options

Examples

irisBIC <- mclustBIC(iris[,-5])
irisBIC
plot(irisBIC)

subset <- sample(1:nrow(iris), 100)
irisBIC <- mclustBIC(iris[,-5], initialization=list(subset =subset))
irisBIC
plot(irisBIC)

irisBIC1 <- mclustBIC(iris[,-5], G=seq(from=1,to=9,by=2), 
                    modelNames=c("EII", "EEI", "EEE"))
irisBIC1
plot(irisBIC1)
irisBIC2  <- mclustBIC(iris[,-5], G=seq(from=2,to=8,by=2), 
                       modelNames=c("VII", "VVI", "VVV"), x= irisBIC1)
irisBIC2
plot(irisBIC2)

nNoise <- 450
set.seed(0)
poissonNoise <- apply(apply( iris[,-5], 2, range), 2, function(x, n) 
                      runif(n, min = x[1]-.1, max = x[2]+.1), n = nNoise)
set.seed(0)
noiseInit <- sample(c(TRUE,FALSE),size=nrow(iris)+nNoise,replace=TRUE,
                    prob=c(3,1))
irisNdata <- rbind(iris[,-5], poissonNoise)
irisNbic <- mclustBIC(data = irisNdata,
                      initialization = list(noise = noiseInit))
irisNbic
plot(irisNbic)