trainModel-PCOSP-method: Train a PCOSP Model Based on The Data the assay...
In bhklab/PanCuRx: 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 'PCOSP' trainModel(object, numModels = 10, minAccuracy = 0.6, ...)

`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`	A `float` specifying the balanced accurary required to consider a model 'top scoring'. Defaults to 0.6. Must be in the range [0, 1].
`...`	Fall through arguments to `BiocParallel::bplapply`. Use this to configure parallelization options. By default the settings inferred in `BiocParallel::bpparam()` will be used.

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 PCOSP object with the trained model in the model slot.

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

data(samplePCOSPmodel)

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

set.seed(getModelSeed(samplePCOSPmodel))
trainModel(samplePCOSPmodel, numModels=2, minAccuracy=0.6)