trainModel-RGAModel-method: Train a RGAModel Based on the Data in the assays slot.
In bhklab/PDATK: Pancreatic Ductal Adenocarcinoma Tool-Kit

Description Usage Arguments Details Value See Also Examples

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.

1 2	## S4 method for signature 'RGAModel' trainModel(object, numModels = 10, minAccuracy = 0, ...)

`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`.

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.

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

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

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

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

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