easyHardFeatures {ClassifyR} | R Documentation

## Extract Chosen Features from an EasyHardClassifier Object

### 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)

[Package ClassifyR version 2.10.0 Index]