R: MclustDA Training

mclustDAtrain {mclust}

R Documentation

MclustDA Training

Description

Training phase for MclustDA discriminant analysis.

Usage

mclustDAtrain(data, labels, G=NULL, modelNames=NULL, prior=NULL, 
              control=emControl(), initialization=NULL, warn=FALSE, 
              verbose=TRUE, ...)

Arguments

`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 `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.
`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: hcPairs A matrix of merge pairs for hierarchical clustering such as produced by function `hc`. The default is to compute a hierarchical clustering tree by applying function `hc` with `modelName = "E"` to univariate data and `modelName = "VVV"` to multivariate data or a subset as indicated by the `subset` argument. The hierarchical clustering results are used as starting values for EM. subset A logical or numeric vector specifying a subset of the data to be used in the initial hierarchical clustering phase.
`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: `verbose=TRUE`.
`...`	Catches unused arguments in indirect or list calls via `do.call`.

Details

Except for labels and verbose, the arguments are the same as those for mclustBIC.

Value

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.

References

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.

Examples

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])

[Package mclust version 3.4.11 Index]