trainModel-RGAModel-method: Train a RGAModel Based on the Data in the assays slot.

In bhklab/PanCuRx: Pancreatic Ductal Adenocarcinoma Tool-Kit

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)

bhklab/PanCuRx documentation built on Dec. 30, 2021, 4:59 p.m.