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