GROAN (version 1.3.1)

GROAN.run: Compare Genomic Regressors on a Noisy Dataset

Description

This function runs the experiment described in a GROAN.Workbench object, training regressor(s) on the data contained in a GROAN.NoisyDataSet object via parameter nds. The prediction accuracy is estimated either through crossvalidation or on separate test dataset supplied via parameter nds.test. It returns a GROAN.Result object, which have a summary function for quick inspection and can be fed to plotResult for visual comparisons. In case of crossvalidation the test dataset in the result object will report the [CV] suffix.
The experiment statistics are computed via measurePredictionPerformance.
Each time this function is invoked it will refer to a runId - an alphanumeric string identifying each specific run. The runId is usually generated internally, but it is possible to pass it if the intention is to join results from different runs for analysis purposes.

Usage

GROAN.run(nds, wb, nds.test = NULL, run.id = createRunId())

Value

a GROAN.Result object

Arguments

nds

a GROAN.NoisyDataSet object, containing the data (genotypes, phenotypes and so forth) plus a noiseInjector function

wb

a GROAN.Workbench object, containing the regressors to be tested together with the description of the experiment

nds.test

either a GROAN.NoisyDataSet or a list of GROAN.NoisyDataSet. The regression model(s) trained on nds will be tested on nds.test

run.id

an alphanumeric string identifying this specific run. If not passed it is generated using createRunId

See Also

measurePredictionPerformance

Examples

if (FALSE) {
#Complete examples are found in the vignette
vignette('GROAN.vignette', package='GROAN')

#Minimal example
#1) creating a noisy dataset with normal noise
nds = createNoisyDataset(
  name = 'PEA KI, normal noise',
  genotypes = GROAN.KI$SNPs,
  phenotypes = GROAN.KI$yield,
  noiseInjector = noiseInjector.norm,
  mean = 0,
  sd = sd(GROAN.KI$yield) * 0.5
)

#2) creating a GROAN.WorkBench using default regressor and crossvalidation preset
wb = createWorkbench()

#3) running the experiment
res = GROAN.run(nds, wb)

#4) examining results
summary(res)
plotResult(res)
}