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 |
modelNames |
A vector of character strings indicating the models to be fitted
in the EM phase of clustering. 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 to suppress these warnings. |
... |
Catches unused arguments in indirect or list calls via |
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. |
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 |
|
uncertainty |
The uncertainty associated with the classification. |
Attributes: |
The input parameters other than the data. |
C. Fraley and A. E. Raftery (2006, revised 2010). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
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, revised 2009). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, 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.
priorControl
,
emControl
,
mclustBIC
,
mclustModelNames
,
mclustOptions
irisMclust <- Mclust(iris[,-5]) ## Not run: plot(irisMclust) ## End(Not run)