trainModel,RGAModel-method {PDATK}R Documentation

Train a RGAModel Based on the Data in the assays slot.

Description

Uses the switchBox SWAP.Train.KTSP function to fit a number of k top scoring pair models to the data, filtering the results to the best models based on the specified paramters.

Usage

## S4 method for signature 'RGAModel'
trainModel(object, numModels = 10, minAccuracy = 0, ...)

Arguments

object

A RGAmodel object to train.

numModels

An integer specifying the number of models to train. Defaults to 10. We recommend using 1000+ for good results.

minAccuracy

A float specifying the balanced accuracy required to consider a model 'top scoring'. Defaults to 0. Must be in the range 0 to 1.

...

Fall through arguments to BiocParallel::bplapply.

Details

This function is parallelized with BiocParallel, thus if you wish to change the back-end for parallelization, number of threads, or any other parallelization configuration please pass BPPARAM to bplapply.

Value

A RGAModel object with the trained model in the model slot.

See Also

switchBox::SWAP.KTSP.Train BiocParallel::bplapply

Examples

data(sampleRGAmodel)
set.seed(getModelSeed(sampleRGAmodel))

# Set parallelization settings
BiocParallel::register(BiocParallel::SerialParam())

trainedRGAmodel <- trainModel(sampleRGAmodel, numModels=2, minAccuracy=0)


[Package PDATK version 1.0.2 Index]