mclustModel {mclust} | R Documentation |
Determines the best model from clustering via mclustBIC
for a given set of model parameterizations and numbers of components.
mclustModel(data, BICvalues, G, modelNames, ...)
data |
The matrix or vector of observations used to generate `object'. |
BICvalues |
An "mclustBIC" object,
which is the result of applying mclustBIC
to data .
|
G |
A vector of integers giving the numbers of mixture components (clusters)
from which the best model according to BIC will be selected
(as.character(G) must be a subset of the row names of
BICvalues ).
The default is to select the best model for all numbers
of mixture components used to obtain BICvalues .
|
modelNames |
A vector of integers giving the model parameterizations
from which the best model according to BIC will be selected
(as.character(model) must be a subset of the column names of
BICvalues ).
The default is to select the best model for parameterizations
used to obtain BICvalues .
|
... |
Not used. For generic/method consistency. |
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 corresponding to the optimal BIC. |
n |
The number of observations in the data. |
d |
The dimension of the data. |
G |
The number of mixture components in the model corresponding to the optimal BIC. |
bic |
The 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. |
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 (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
irisBIC <- mclustBIC(iris[,-5]) mclustModel(iris[,-5], irisBIC) mclustModel(iris[,-5], irisBIC, G = 1:6, modelNames = c("VII", "VVI", "VVV"))