Description Usage Arguments Value References See Also Examples
The optimal model according to BIC for EM initialized by hierarchical clustering for parameterized Gaussian mixture models.
1 2 3 4 5 6 7 
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 default is:
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:
 
warn 
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued.
The default is controlled by  
x 
An object of class  
verbose 
A logical controlling if a text progress bar is displayed during the
fitting procedure. By default is  
... 
Catches unused arguments in indirect or list calls via 
An object of class 'Mclust'
providing the optimal (according to BIC)
mixture model estimation.
The details of the output components are as follows:
call 
The matched call 
data 
The input data matrix. 
modelName 
A character string denoting the model at which the optimal BIC occurs. 
n 
The number of observations in the data. 
d 
The dimension of the data. 
G 
The optimal number of mixture components. 
BIC 
All BIC values. 
bic 
Optimal BIC value. 
loglik 
The loglikelihood corresponding to the optimal BIC. 
df 
The number of estimated parameters. 
hypvol 
The hypervolume parameter for the noise component if required, otherwise set to 
parameters 
A list with the following components:

z 
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class. 
classification 
The classification corresponding to 
uncertainty 
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. 205233.
Fraley C. and Raftery A. E. (2002) Modelbased clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611631.
Fraley C., Raftery A. E., Murphy T. B. and Scrucca L. (2012) mclust Version 4 for R: Normal Mixture Modeling for ModelBased 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 modelbased clustering. Journal of Classification, 24, 155181.
summary.Mclust
,
plot.Mclust
,
priorControl
,
emControl
,
hc
,
mclustBIC
,
mclustModelNames
,
mclust.options
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  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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