mclustDAtrain {mclust} | R Documentation |
Training phase for MclustDA discriminant analysis.
mclustDAtrain(data, labels, G=NULL, modelNames=NULL, prior=NULL, control=emControl(), initialization=NULL, warn=FALSE, verbose=TRUE, ...)
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. |
labels |
A numeric or character vector assigning a class label to each observation. |
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 when estimation fails. The default is to suppress these warnings. |
verbose |
A logical value indicating whether or not to print the models and
numbers of components for each class.
Default: |
... |
Catches unused arguments in indirect or list calls via |
Except for labels
and verbose
, the arguments are the
same as those for mclustBIC
.
A list in which each element gives the parameters and other summary
information for the model best fitting each class according to BIC.
Attributes are the input parameters other than data
, labels
and verbose
.
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.
summary.mclustDAtrain
,
mclustDAtest
,
mclustBIC
odd <- seq(1, nrow(cross), by = 2) train <- mclustDAtrain(cross[odd,-1], labels = cross[odd,1]) ## training step summary(train) even <- odd + 1 test <- mclustDAtest(cross[even,-1], train) ## compute model densities clEven <- summary(test)$class ## classify training set classError(clEven,cross[even,1])