mclust (version 2.1-14)

mclustDAtest: MclustDA Testing

Description

Testing phase for MclustDA discriminant analysis.

Usage

mclustDAtest(data, models)

Arguments

data

A numeric vector, matrix, or data frame of observations to be classified.

models

A list of MCLUST-style models including parameters, usually the result of applying mclustDAtrain to some training data.

Value

• A matrix in which the [i,j]th entry is the density for that test observation i in the model for class j.

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.

See Also

summary.mclustDAtest, mclustDAtrain

Examples

n <- 250 ## create artificial data
set.seed(0)
x <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)),
           matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1])
xclass <- c(rep(1,n),rep(2,n))
par(pty = "s")
mclust2Dplot(x, classification = xclass, type="classification", ask=FALSE)

odd <- seq(1, 2*n, 2)
train <- mclustDAtrain(x[odd, ], labels = xclass[odd]) ## training step
summary(train)

even <- odd + 1
test <- mclustDAtest(x[even, ], train) ## compute model densities
summary(test)$class ## classify training set