mclust (version 3.4.7)

unmap: Indicator Variables given Classification

Description

Converts a classification into a matrix of indicator variables.

Usage

unmap(classification, groups=NULL, noise=NULL, ...)

Arguments

classification

A numeric or character vector. Typically the distinct entries of this vector would represent a classification of observations in a data set.

groups

A numeric or character vector indicating the groups from which classification is drawn. If not supplied, the default is to assumed to be the unique entries of classification.

noise

A single numeric or character value used to indicate the value of groups corresponding to noise.

...

Catches unused arguments in indirect or list calls via 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 sorted order classification. If a noise value of symbol is designated, the corresponding indicator variables are relocated to the last column of the matrix.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.

See Also

map, estep, me

Examples

z <- unmap(iris[,5])
z[1:5, ]
  
emEst <- me(modelName = "VVV", data = iris[,-5], z = z)
emEst$z[1:5,]
  
map(emEst$z)