easyHardFeatures: Extract Chosen Features from an EasyHardClassifier Object

In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Description

The features are described by a data frame. One column is named "dataset" and the other is named "feature". This provides identifiability in case when multiple types of data have features with the same name.

Usage

  ## S4 method for signature 'EasyHardClassifier'
easyHardFeatures(easyHardClassifier)

Arguments

easyHardClassifier

An EasyHardClassifier object

.

Details

If any of the features are from the colun data of the input MultiAssayExperiment, the dataset value will be "clinical".

Value

To be consistent with other functions for extracting features from a trained model, a list of length two. The first element is for feature rankings, which is not meaningful for an easy-hard classifier, so it is NULL. The second element is the selected features.

Author(s)

Dario Strbenac

Examples

  genesMatrix <- matrix(c(rnorm(90, 9, 1),
                          9.5, 9.4, 5.2, 5.3, 5.4, 9.4, 9.6, 9.9, 9.1, 9.8),
		        ncol = 10, byrow = TRUE)
  colnames(genesMatrix) <- paste("Sample", 1:10)
  rownames(genesMatrix) <- paste("Gene", 1:10)
  genders <- factor(c("Male", "Male", "Female", "Female", "Female",
                      "Female", "Female", "Female", "Female", "Female"))

  # Scenario: Male gender can predict the hard-to-classify Sample 1.
  clinical <- DataFrame(age = c(31, 34, 32, 39, 33, 38, 34, 37, 35, 36),
                        gender = genders,
                        class = factor(rep(c("Poor", "Good"), each = 5)),
		        row.names = colnames(genesMatrix))
  dataset <- MultiAssayExperiment(ExperimentList(RNA = genesMatrix), clinical)
  trained <- easyHardClassifierTrain(dataset, easyClassifierParams = list(minCardinality = 2, minPurity = 0.9),
                                     hardClassifierParams = list(SelectParams(featureSelection = differentMeansSelection,
                                                                 selectionName = "Difference in Means",
                                                                 resubstituteParams = ResubstituteParams(1:10, "balanced error", "lower")), TrainParams(), PredictParams()))

  easyHardFeatures(trained)

ClassifyR documentation built on Nov. 8, 2020, 6:53 p.m.