externalValidation.stats {geNetClassifier} | R Documentation

## Statistics of the external validation.

### Description

Taking as input the confussion matrix resulting from external validation calculates the global Accuracy, Call Rate, Sensitivity, Specificity and Matthews Correlation Coefficient.

### Usage

    externalValidation.stats(confussionMatrix, numDecimals = 2)

### Arguments

`confussionMatrix` | Confussion matrix containing the real class as rows and the assigned class as columns.
`numDecimals` | Integer. Number of decimals to show on the statistics.

### Value

List:

- global General classifier stats.
  Accuracy: Percentage of correctly assigned samples within all assigned samples.
  CallRate: Percentage of samples wich were assigned to a class.

- byClass Stats by class.
  Sensitivity: Percentage of samples of each class which were correctly identified (Rate of true positives)
  Specificity: Percentage of samples assigned to a given class that really belonged to the class (Rate of true negatives)
  MCC (Matthews Correlation Coefficient): Measure wich takes into account both, true and false positives and negatives. (100%: Perfect assignments)

  confMatrix Confussion matrix.

### 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`
Querying the classifier: `queryGeNetClassifier`
Generating the probability matrix: `externalValidation.probMatrix`

### Examples

    ##########################
    ## Classifier training
    ##########################

    # 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:
    # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples],
    #    sampleLabels="LeukemiaType", plotsName="leukemiasClassifier")
    data(leukemiasClassifier) # Sample trained classifier

    ##########################
    ## External Validation:
    ##########################
    # Select the samples to query the classifier
    #   - External validation: samples not used for training
    testSamples <- c(1:60)[-trainSamples]

    # Make a query to the classifier:
    queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples])

    # Create the confusion matrix
    confMatrix <- table(leukemiasEset[,testSamples]$LeukemiaType,queryResult$class)

    # Calculate its accuracy, call rate, sensitivity and specificity:
    externalValidation.stats(confMatrix)

[Package geNetClassifier version 1.26.0 Index]