Model-based clustering based on parameterized finite Gaussian mixture models. Models are estimated by EM algorithm initialized by hierarchical model-based agglomerative clustering. The optimal model is then selected according to BIC.
Mclust(data, G = NULL, modelNames = NULL,
prior = NULL,
control = emControl(),
initialization = NULL,
warn = mclust.options("warn"),
x = NULL,
verbose = interactive(), …)A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations (\(n\)) and columns correspond to variables (\(d\)).
An integer vector specifying the numbers of mixture components
(clusters) for which the BIC is to be calculated.
The default is G=1:9.
A vector of character strings indicating the models to be fitted in the EM phase of clustering. The default is:
for univariate data (\(d = 1\)): c("E", "V")
for multivariate data (\(n > d\)): all the models available in mclust.options("emModelNames")
for multivariate data (\(n <= d\)): the spherical and diagonal models, i.e. c("EII", "VII", "EEI", "EVI", "VEI", "VVI")
The help file for mclustModelNames describes the available models.
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
priorControl.
Note that, as described in defaultPrior, in the multivariate
case only 10 out of 14 models may be used in conjunction with a prior, i.e.
those available in MCLUST up to version 4.4.
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:
hcPairsA 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".
subsetA 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"). Note that to
guarantee exact reproducibility of results a seed must be specified
(see set.seed).
noiseA 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.
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued.
The default is controlled by mclust.options.
An object of class 'mclustBIC'. If supplied, BIC values for models
that have already been computed and are available in x are not
recomputed.
All arguments, with the exception of 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.
An object of class 'Mclust' providing the optimal (according to BIC)
mixture model estimation.
The details of the output components are as follows:
The matched call
The input data matrix.
A character string denoting the model at which the optimal BIC occurs.
The number of observations in the data.
The dimension of the data.
The optimal number of mixture components.
All BIC values.
The log-likelihood corresponding to the optimal BIC.
The number of estimated parameters.
BIC value of the selected model.
ICL value of the selected model.
The hypervolume parameter for the noise component if required, otherwise set to NULL (see hypvol).
A list with the following components:
proA vector whose kth component is the mixing proportion for the kth component of the mixture model. If missing, equal proportions are assumed.
meanThe mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
varianceA list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for mclustVariance
for details.
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class.
The classification corresponding to z, i.e. map(z).
The uncertainty associated with the classification.
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.
Fraley C., Raftery A. E., Murphy T. B. and Scrucca L. (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.
C. Fraley and A. E. Raftery (2007) Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification, 24, 155-181.
summary.Mclust,
plot.Mclust,
priorControl,
emControl,
hc,
mclustBIC,
mclustModelNames,
mclust.options
# NOT RUN {
mod1 <- Mclust(iris[,1:4])
summary(mod1)
mod2 <- Mclust(iris[,1:4], G = 3)
summary(mod2, parameters = TRUE)
# Using prior
mod3 <- Mclust(iris[,1:4], prior = priorControl())
summary(mod3)
mod4 <- Mclust(iris[,1:4], prior = priorControl(functionName="defaultPrior", shrinkage=0.1))
summary(mod4)
# Clustering of faithful data with some artificial noise added
nNoise <- 100
set.seed(0) # to make it reproducible
Noise <- apply(faithful, 2, function(x)
runif(nNoise, min = min(x)-.1, max = max(x)+.1))
data <- rbind(faithful, Noise)
plot(faithful)
points(Noise, pch = 20, cex = 0.5, col = "lightgrey")
set.seed(0)
NoiseInit <- sample(c(TRUE,FALSE), size = nrow(faithful)+nNoise,
replace = TRUE, prob = c(3,1)/4)
mod5 <- Mclust(data, initialization = list(noise = NoiseInit))
summary(mod5, parameter = TRUE)
plot(mod5, what = "classification")
# }
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