trainModel-RLSModel-method: Train a PCOSP Model Based on The Data the assay...

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 'RLSModel'
trainModel(object, numModels = 10, minAccuracy = 0, ...)

Arguments

object	A PCOSP object to train.
numModels	An integer specifying the number of models to train. Defaults to 10. We recommend using 1000+ for good results.
minAccuracy	This parameter should be set to zero, since we do not expect the permuted models to perform well. Setting this higher will result in an ensemble with very few models included.
...	Fall through arguments to BiocParallel::bplapply. Use this to configure parallelization options. By default the settings inferred in BiocParallel::bpparam() will be used.

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 PCOSP object with the trained model in the model slot.

See Also

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

Examples

data(sampleRLSmodel)
set.seed(getModelSeed(sampleRLSmodel))

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

trainedRLSmodel <- trainModel(sampleRLSmodel, numModels=2)

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