Collects and checks necessary parameters required for classifier training. The empty constructor is provided for convenience.
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.
A function which will construct a classifier, and also
possibly make the predictions. The first argument must be a
object. The second argument must be a vector of classes. If doesTests is
TRUE, the third
argument must be a
matrix of test data. The function must also accept a parameter named
verbose The function's return value can be either a trained classifier when
FALSE or a vector of class predictions if
features as columns.
classifier also performs
and returns predictions.
Character vector. Names of any variables created in prior stages by
runTest that need to be passed to
Other named parameters which will be used by the classifier.
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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. # It doesn't formally have a verbose parameter, but it is effectively consumed by ... in its formal definition.