Parameters for Classifier Training

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Description

Collects and checks necessary parameters required for classifier training. The empty constructor is provided for convenience.

Constructor

TrainParams() Creates a default TrainParams object. The classifier function is DLDA. Users should create an appropriate TrainParams object for the characteristics of their data, once they are familiar with this software.

TrainParams(classifier, transposeExpression, doesTests, ...) Creates a TrainParams object which stores the function which will do the classifier building and parameters that the function will use.

classifier

A function which will construct a classifier, and also possibly make the predictions. The first argument must be a matrix object. The second argument must be a vector of classes. The third argument must be verbose. If doesTests is TRUE, the third argument must be a matrix of test data and the fourth argument is verbose. The function's return value can be either a trained classifier when doesTests is FALSE or a vector of class predictions if doesTests is TRUE.

transposeExpression

Set to TRUE if classifier expects features as columns.

doesTests

Set to TRUE if classifier also performs and returns predictions.

intermediate

Character vector. Names of any variables created in prior stages by runTest that need to be passed to classifier.

...

Other named parameters which will be used by the classifier.

Author(s)

Dario Strbenac

Examples

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if(require(sparsediscrim))
  trainParams <- TrainParams(dlda, transposeExpression = TRUE, doesTests = FALSE)
# sparsediscrim has a separate predict method for trained DLDA objects.
# dlda expects features in columns, and samples in rows.

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