predict.classifierOutput: Predict method for 'classifierOutput' objects

In MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

Description

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

Usage

## 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 <[e-mail]>

See Also

MLearn and classifierOutput.

Examples

## Not run: 
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])

## End(Not run) # end of dontrun

MLInterfaces documentation built on Nov. 8, 2020, 8:14 p.m.