summary.EMclustN: summary function for EMclustN

Description

Optimal model characteristics and classification for EMclustN results.

Usage

summary.EMclustN(object, data, G, modelNames, ...)

Arguments

object

An "EMclustN" object, whch is the result of a pplying EMclustN to data with an initail noise estimate.

data

The matrix or vector of observations used to generate `object'.

A vector of integers giving the numbers of mixture components (clusters) over which the summary is to take place (as.character(G) must be a subset of the column names of `object'). The default is to summarize over all of the n

modelNames

A vector of character strings denoting the models over which the summary is to take place (must be a subset of the row names of `object'). The default is to summarize over all models used in the original analysis.

...

Not used. For generic/method consistency.

Value

A list giving the optimal (according to BIC) parameters, conditional probabilities z, and loglikelihood, together with the associated classification and its uncertainty.

References

C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust. C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.

Examples

Run this code

data(iris)
irisMatrix <- as.matrix(iris[,1:4])

b <- apply( irisMatrix, 2, range)
n <- 450
set.seed(0)
poissonNoise <- apply(b, 2, function(x, n=n) 
                      runif(n, min = x[1]-0.1, max = x[2]+.1), n = n)
set.seed(0)
noiseInit <- sample(c(TRUE,FALSE),size=150+450,replace=TRUE,prob=c(3,1))
irisNoise <- rbind(irisMatrix, poissonNoise)

Bic <- EMclustN(data=irisNoise, noise = noiseInit)
summary(Bic, irisNoise)
summary(Bic, irisNoise, G = 0:6, modelName = c("VII", "VVI", "VVV"))