mclust (version 2.1-8)

unmap: 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)