GeneralizationError-class {geNetClassifier} | R Documentation

## Class "GeneralizationError" (slot of GeNetClassifierReturn)

### Description

Contains the estimation of the Generalization Error and the gene stats for `geNetClassifier` executed with the given data and parameters. \ Calculated by 5-fold cross-validation.

### Slots

`accuracy`:

  `"Matrix"`. Accuracy and call rate.

`sensitivitySpecificity`:

  `"Matrix"`. Sensitivity, Specificity, Matthews Correlation Coefficient and Call Rate for each of the classes.

`confMatrix`:

  `"Matrix"`. Confussion matrix.

`probMatrix`:

  `"Matrix"`. Probabilities of belonging to each class for the assigned samples. Helps identifying where errors are likely to occur even though there were not actual errors in the cross-validation.

`querySummary`:

  `"List"`. Stats regarding the probability and number of assigned test samples to each class.

`classificationGenes.stats`:

  `"List"`. Some basic statistics regarding the chosen genes.

`classificationGenes.num`:

  `"Matrix"`. Number of genes used for each of the 5 cross-validaton classifiers.

### Methods

overview

  `signature(object = "GeneralizationError")`: Shows an overview of all the slots in the object.

### Author(s)

Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain

### See Also

Main package function and classifier training: `geNetClassifier`

### Examples

    ######
    # Load data and train a classifier
    ######

    # Load an expressionSet:
    library(leukemiasEset)
    data(leukemiasEset)

    # Select the train samples:
    trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58)
    # summary(leukemiasEset$LeukemiaType[trainSamples])

    # Train a classifier or load a trained one:
    # Note: Required 'estimateGError=TRUE'
    # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples],
    #    sampleLabels="LeukemiaType", plotsName="leukemiasClassifier",
    #    estimateGError=TRUE)
    data(leukemiasClassifier) # Sample trained classifier

    # Global view of the returned object and its structure:
    leukemiasClassifier
    names(leukemiasClassifier)

    #########
    # Exploring the cross validation stats
    # Note: Required 'estimateGError=TRUE' in geNetClassifier()
    #########
    # Generalization Error estimated by cross-validation:
    leukemiasClassifier@generalizationError
    overview(leukemiasClassifier@generalizationError)
    	# i.e. probabilityMatrix:
    	leukemiasClassifier@generalizationError@probMatrix
    	# i.e. statistics of the genes chosen in any of the CV loops for for AML:
    	leukemiasClassifier@generalizationError@classificationGenes.stats$AML

[Package geNetClassifier version 1.34.0 Index]