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 G=1:9 , unless the argument x is specified,
in which case the default is taken from the values associated
with x .
|
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 c("E", "V") for univariate data and
mclustOptions()\$emModelNames for multivariate data (n > d),
the spherical and diagonal models
c("EII", "VII", "EEI", "EVI", "VEI", "VVI")
for multivariate data (n <= d),
unless the argument x is specified, in which case
the default is taken from the values asscoiated with x .
|
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:
|
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 specified in the arguments. Models that have already been computed
in x are not recomputed. All arguments to mclustBIC
except data , G and modelName are
ignored and their values are set as specified in the attributes of
x .
Defaults for G and modelNames are taken from x .
|
... |
Catches unused arguments in indirect or list calls via do.call .
|
Bayesian Information Criterion for the specified mixture models numbers of clusters. Auxiliary information returned as attributes.
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 (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
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)