Converts a classification into a matrix of indicator variables.

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.

map, estep, me

## Examples

z <- unmap(iris[,5])
z[1:5, ]
#>      [,1] [,2] [,3]
#> [1,]    1    0    0
#> [2,]    1    0    0
#> [3,]    1    0    0
#> [4,]    1    0    0
#> [5,]    1    0    0

emEst <- me(modelName = "VVV", data = iris[,-5], z = z)
emEst$z[1:5,] #> [,1] [,2] [,3] #> [1,] 1 1.340380e-44 1.861339e-34 #> [2,] 1 2.201405e-31 6.676298e-28 #> [3,] 1 1.896748e-36 1.102178e-29 #> [4,] 1 3.488647e-32 6.409600e-26 #> [5,] 1 4.393475e-47 7.745885e-35 map(emEst$z)
#>   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 2 3 2
#>  [75] 2 2 2 3 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3
#> [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
#> [149] 3 3