summary.EMclustN {mclust} | R Documentation |
Optimal model characteristics and classification
for EMclustN
results.
summary.EMclustN(object, data, G, modelNames, ...)
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'. |
G |
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 numbers of mixture components
used in the original analysis.
|
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. |
A list giving the optimal (according to BIC) parameters,
conditional probabilities z
, and loglikelihood,
together with the associated classification and its uncertainty.
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.
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"))