| summary.mclustDAtest {mclust} | R Documentation |
Classifications from mclustDAtest and the corresponding
posterior probabilities.
summary.mclustDAtest(object, pro, ...)
object |
The output of mclustDAtest.
|
pro |
Prior probabilities for each class in the training data. |
... |
Not used. For generic/method consistency. |
A list with the following two components:
classfication |
The classification from mclustDAtest
|
z |
Matrix of posterior probabilities in which the [i,j]th entry
is the probability of observation i belonging to class
j.
|
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.
set.seed(0)
n <- 100 ## create artificial data
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))
## Not run:
par(pty = "s")
mclust2Dplot(x, classification = xclass, type="classification", ask=FALSE)
## End(Not run)
odd <- seq(1, 2*n, 2)
train <- mclustDAtrain(x[odd, ], labels = xclass[odd]) ## training step
summary(train)
even <- seq(1, 2*n, 2)
test <- mclustDAtest(x[even, ], train) ## compute model densities
testSummary <- summary(test) ## classify training set
names(testSummary)
testSummary$class
testSummary$z