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maigesPack (version 1.36.0)

maigesClass-class: maigesClass class, store results of discrimination (or classification) analysis

Description

This class defines a structure to store the results from discrimination analysis. This type of analysis can be done using the functions classifyLDA, classifySVM, classifyKNN, classifyLDAsc, classifySVMsc or classifyKNNsc.

Arguments

Slots

W:
numeric matrix giving the W values of the genes tested. This information is useful for doing plots of the cliques.
CV:
numeric vector that store the number of correct classifications in the leave-one-out cross validation procedure.
SVD:
numeric vector that store the singular value decomposition from Fisher linear discriminant analysis.
cliques:
character matrix that gives the genes that constitute the cliques returned. The rows of the matrix represent the cliques while the columns represent the genes that form the clique.
cliques.idx:
numeric matrix similar to the above, storing the indexes (onto W slot) of the genes.
method:
character string giving the method of discrimination analysis used.
Date:
character string giving the date and time that the object was generated.
V.info:
list containg three characters. The first one is a string containing the R version used when the object was created. The second is a char vector with base packages and the last one is another char vector with additional packages and version numbers.

Methods

plot
signature(x = 'maigesClass'): plot method for maigesClass class. Display dispersion plots.
print
signature(x = 'maigesClass'): print method for maigesClass class.
show
signature(x = 'maigesClass'): show method for maigesClass class.
summary
signature(x = 'maigesClass'): summary method for maigesClass class.

Details

Objects of this class are produced by calling the functions classifyLDA, classifySVM, classifyKNN, classifyLDAsc, classifySVMsc or classifyKNNsc over an object of class maiges to search for cliques satisfying the criteria specified for classification.

See Also

classifyLDA, classifySVM, classifyKNN, classifyLDAsc, classifySVMsc and classifyKNNsc.