predict.classifierOutput: Predict method for 'classifierOutput' objects

Description Usage Arguments Details Value Note Author(s) See Also Examples

Description

This function predicts values based on models trained with MLInterfaces' MLearn interface to many machine learning algorithms.

Usage

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## S3 method for class 'classifierOutput'
predict(object, newdata, ...)

Arguments

object

An instance of class classifierOutput.

newdata

An object containing the new input data: either a matrix, a data.frame or an ExpressionSet.

...

Other arguments to be passed to the algorithm-specific predict methods.

Details

This S3 method will extract the ML model from the classifierOutput instance and call either a generic predict method or, if available, a specficly written wrapper to do classes prediction and class probabilities.

Value

Currently, a list with

testPredictions

A factor with class predictions.

testScores

A numeric or matrix with class probabilities.

Note

The function output will most likely be updated in a near future to a classifierOutput (or similar) object.

Author(s)

Laurent Gatto <lg390@cam.ac.uk>

See Also

MLearn and classifierOutput.

Examples

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set.seed(1234)
data(sample.ExpressionSet)
trainInd <- 1:16

clout.svm <- MLearn(type~., sample.ExpressionSet[100:250,], svmI, trainInd)
predict(clout.svm, sample.ExpressionSet[100:250,-trainInd])

clout.ksvm <- MLearn(type~., sample.ExpressionSet[100:250,], ksvmI, trainInd)
predict(clout.ksvm, sample.ExpressionSet[100:250,-trainInd])

clout.nnet <- MLearn(type~., sample.ExpressionSet[100:250,], nnetI, trainInd, size=3, decay=.01 )
predict(clout.nnet, sample.ExpressionSet[100:250,-trainInd])

clout.knn <- MLearn(type~., sample.ExpressionSet[100:250,], knnI(k=3), trainInd)
predict(clout.knn, sample.ExpressionSet[100:250,-trainInd],k=1)
predict(clout.knn, sample.ExpressionSet[100:250,-trainInd],k=3)

clout.plsda <- MLearn(type~., sample.ExpressionSet[100:250,], plsdaI, trainInd)
predict(clout.plsda, sample.ExpressionSet[100:250,-trainInd])

clout.nb <- MLearn(type~., sample.ExpressionSet[100:250,], naiveBayesI, trainInd)
predict(clout.nb, sample.ExpressionSet[100:250,-trainInd])

# this can fail if training set does not yield sufficient diversity in response vector;
# setting seed seems to help with this example, but other applications may have problems
#
clout.rf <- MLearn(type~., sample.ExpressionSet[100:250,], randomForestI, trainInd)
predict(clout.rf, sample.ExpressionSet[100:250,-trainInd])


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