trainModel,RLSModel-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 '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)

[Package PDATK version 1.2.0 Index]