trainModel,PCOSP-method {PDATK} | R Documentation

## Train a PCOSP Model Based on The Data the assay `trainMatrix`.

### 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 'PCOSP'
    trainModel(object, numModels = 10, minAccuracy = 0.6, ...)

### 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` | 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.

### 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(samplePCOSPmodel)

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

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

[Package PDATK version 1.2.0 Index]