Dummy matrix for an outcome factor
Converts a class or group vector or factor into a matrix of indicator variables.
unmap(classification, groups = NULL, noise = NULL)
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
|
noise |
A single numeric or character value used to indicate the value
of |
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.
Ignacio Gonzalez, Kim-Anh Le Cao, Pierre Monget, AL J Abadi
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.
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
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