unmap {mixOmics} | R Documentation

## Dummy matrix for an outcome factor

### Description

Converts a class or group vector or factor into a matrix of indicator variables.

### Usage

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

### Arguments

`classification` | A numeric or character vector or factor. 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.

### Value

An n by K matrix of (0,1) indicator variables, where n is the length of samples and K the number of classes in the outcome.

If a `noise` value of symbol is designated, the corresponding indicator variables are relocated to the last column of the matrix.

Note: - you can remap an unmap vector using the function `map` from the package mclust. - this function should be used to unmap an outcome vector as in the non-supervised methods of mixOmics. For other supervised analyses such as (s)PLS-DA, (s)gccaDA this function is used internally.

### 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, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

### Examples

    data(nutrimouse)
    Y = unmap(nutrimouse$diet)
    Y
    data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
    # data could then used as an input in wrapper.rgcca, which is not, technically,
    # a supervised method, see ??wrapper.rgcca

[Package mixOmics version 6.10.9 Index]