Mclust {mclust} | R Documentation |
The optimal model according to BIC for EM initialized by hierarchical clustering for parameterized Gaussian mixture models.
Mclust(data, G=NULL, modelNames=NULL, prior=NULL, control=emControl(), initialization=NULL, warn=FALSE, ...)
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 .
|
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).
|
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:
|
warn |
A logical value indicating whether or not certain warnings (usually related to singularity) should be issued. The default is to suppress these warnings. |
... |
Catches unused arguments in indirect or list calls via do.call .
|
A list giving the optimal (according to BIC) parameters,
conditional probabilities z
, and loglikelihood,
together with the associated classification and its uncertainty.
The details of the output components are as follows:
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. |
z |
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class. |
classification |
map(z) : The classification corresponding to z .
|
uncertainty |
The uncertainty associated with the classification. |
Attributes: |
The input parameters other than the data. |
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
,
mclustBIC
,
mclustModelNames
,
mclustOptions
irisMclust <- Mclust(iris[,-5]) ## Not run: plot(irisMclust) ## End(Not run)