GROAN.run | R Documentation |
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.
GROAN.run(nds, wb, nds.test = NULL, run.id = createRunId())
nds |
a GROAN.NoisyDataSet object, containing the data (genotypes, phenotypes and so forth) plus a |
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 |
run.id |
an alphanumeric string identifying this specific run. If not passed it is generated using createRunId |
a GROAN.Result
object
measurePredictionPerformance
## Not run: #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) ## End(Not run)
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