runTest {ClassifyR} | R Documentation |
For a data set of features and samples, the classification process is run. It consists of data transformation, feature selection, classifier training and testing (prediction of samples not used in training).
## S4 method for signature 'matrix' runTest(measurements, classes, ...) ## S4 method for signature 'DataFrame' runTest(measurements, classes, featureSets = NULL, metaFeatures = NULL, minimumOverlapPercent = 80, datasetName, classificationName, training, testing, params = list(SelectParams(), TrainParams(), PredictParams()), verbose = 1, .iteration = NULL) ## S4 method for signature 'MultiAssayExperiment' runTest(measurements, targets = names(measurements), ...)
measurements |
Either a |
classes |
Either a vector of class labels of class |
featureSets |
An object of type |
metaFeatures |
Either |
minimumOverlapPercent |
If |
targets |
If |
... |
Variables not used by the |
datasetName |
A name associated with the data set used. |
classificationName |
A name associated with the classification. |
training |
A vector which specifies the training samples. |
testing |
A vector which specifies the test samples. |
params |
A |
verbose |
Default: 1. A number between 0 and 3 for the amount of progress messages to give. A higher number will produce more messages as more lower-level functions print messages. |
.iteration |
Not to be set by a user. This value is used to keep track of the cross-validation
iteration, if called by |
This function only performs one classification and prediction. See runTests
for a driver function that enables a number of different cross-validation schemes to be applied
and uses this function to perform each iteration. datasetName
and classificationName
need to be provided.
If called directly by the user rather than being used internally by runTests
, a
SelectResult
object.
Dario Strbenac
#if(require(sparsediscrim)) #{ data(asthma) resubstituteParams <- ResubstituteParams(nFeatures = seq(5, 25, 5), performanceType = "balanced error", better = "lower") runTest(measurements, classes, datasetName = "Asthma", classificationName = "Different Means", params = list(SelectParams(limmaSelection, "Moderated t Statistic", resubstituteParams = resubstituteParams), TrainParams(DLDAtrainInterface), PredictParams(DLDApredictInterface) ), training = (1:ncol(measurements)) %% 2 == 0, testing = (1:ncol(measurements)) %% 2 != 0) #}