mixOmics (version 6.3.2)

unmap: 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

# NOT RUN {
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
# }