summary.mclustDAtest {mclust} | R Documentation

## Classification and posterior probability from mclustDAtest.

### Description

Classifications from `mclustDAtest` and the corresponding posterior probabilities.

### Usage

    summary.mclustDAtest(object, pro, ...)

### Arguments

`object` | The output of `mclustDAtest`.
`pro` | Prior probabilities for each class in the training data.
`...` | Not used. For generic/method consistency.

### Value

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.

### 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

`mclustDAtest`

### Examples

    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

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