
BIC for parameterized Gaussian mixture models fitted by EM algorithm initialized by model-based hierarchical clustering.
mclustBIC(data, G = NULL, modelNames = NULL,
prior = NULL, control = emControl(),
initialization = list(hcPairs = NULL,
subset = NULL,
noise = NULL),
Vinv = NULL, warn = mclust.options("warn"),
x = NULL, verbose = interactive(),
...)
Return an object of class 'mclustBIC'
containing the Bayesian Information
Criterion for the specified mixture models numbers of clusters.
Auxiliary information returned as attributes.
The corresponding print
method shows the matrix of values and the top models according to the BIC criterion.
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.
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
.
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
mclust.options("emModelNames")
for multivariate data (n > d)
c("EII", "VII", "EEI", "EVI", "VEI", "VVI")
the spherical and diagonal models for multivariate data (n <= d)
unless the argument x
is specified, in which case
the default is taken from the values associated with x
.
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
priorControl
.
A list of control parameters for EM. The defaults are set by the call
emControl()
.
A list containing zero or more of the following components:
hcPairs
A matrix of merge pairs for hierarchical clustering such as produced
by function hc
.
For multivariate data, the default is to compute a hierarchical
agglomerative clustering tree by applying function hc
with
model specified by mclust.options("hcModelName")
, and
data transformation set by mclust.options("hcUse")
.
All the input or a subset as indicated by the subset
argument is
used for initial clustering.
The hierarchical clustering results are then used to start the EM
algorithm from a given partition.
For univariate data, the default is to use quantiles to start the EM
algorithm. However, hierarchical clustering could also be used by
calling hc
with model specified as "V"
or "E"
.
subset
A logical or numeric vector specifying a subset of the data
to be used in the initial hierarchical clustering phase.
By default no subset is used unless the number of observations exceeds
the value specified by mclust.options("subset")
.
The subset
argument is ignored if hcPairs
are provided.
Note that to guarantee exact reproducibility of results a seed must be
specified (see set.seed
).
noise
A logical or numeric vector indicating an initial guess as to which observations are noise in the data. If numeric the entries should correspond to row indexes of the data. If supplied, a noise term will be added to the model in the estimation.
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.
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when
estimation fails.
The default is controlled by mclust.options
.
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
.
A logical controlling if a text progress bar is displayed during the
fitting procedure. By default is TRUE
if the session is
interactive, and FALSE
otherwise.
Catches unused arguments in indirect or list calls via do.call
.
summary.mclustBIC
,
priorControl
,
emControl
,
mclustModel
,
hc
,
me
,
mclustModelNames
,
mclust.options
irisBIC <- mclustBIC(iris[,-5])
irisBIC
plot(irisBIC)
# \donttest{
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, G = 1:5,
initialization = list(noise = noiseInit))
irisNbic
plot(irisNbic)
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