unmap: Indicator Variables given Classification

In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

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.

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)

mclust documentation built on Nov. 16, 2023, 5:10 p.m.