mclustBIC {mclust} | R Documentation |
BIC for EM initialized by model-based hierarchical clustering for parameterized Gaussian mixture models.
mclustBIC(data, G=NULL, modelNames=NULL, prior=NULL, control=emControl(), initialization=list(hcPairs=NULL, subset=NULL, noise=NULL), Vinv=NULL, warn=FALSE, x=NULL, ...)
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 |
modelNames |
A vector of character strings indicating the models to be fitted
in the EM phase of clustering. The help file for
|
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
initialization |
A list containing zero or more of the following components:
|
Vinv |
An estimate of the reciprocal hypervolume of the data region.
The default is determined by applying function |
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 |
... |
Catches unused arguments in indirect or list calls via |
Bayesian Information Criterion for the specified mixture models numbers of clusters. Auxiliary information returned as attributes.
C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
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.
priorControl
,
emControl
,
mclustModel
,
summary.mclustBIC
,
hc
,
me
,
mclustModelNames
,
mclustOptions
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)