unmap {mclust} | R Documentation

## Indicator Variables given Classification

### Description

Converts a classification into a matrix of indicator variables.

### Usage

      unmap(classification, noise, ...)

### Arguments

`classification` | A numeric or character vector. Typically the distinct entries of this vector would represent a classification of observations in a data set.
`noise` | A single numeric or character value used to indicate observations corresponding to noise.
`...` | Provided to allow lists with elements other than the arguments can be passed in indirect or list calls with `do.call`.

### Value

An n by m matrix of (0,1) indicator variables, where n is the length of `classification` and m is the number of unique values or symbols in `classification`. Columns are labeled by the unique values in `classification`, and the `[i,j]`th entry is 1 if `classification[i]` is the jth unique value or symbol in order of appearance in the `classification`. If a `noise` value of symbol is designated, the corresponding indicator variables are located in the last column of the matrix.

### 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

`map`, `estep`, `me`

### Examples

    data(iris)
    irisMatrix <- as.matrix(iris[,1:4])
    irisClass <- iris[,5]

    z <- unmap(irisClass)
    z

    emEst <- me(modelName = "VVV", data = irisMatrix, z = z)
    emEst$z

    map(emEst$z)

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