Computes the sensitivity of class prediction

Description

Computes the sensitivity of class prediction.

Usage

## S3 method for class 'BigBang':
sensitivityClass(o, cm, ...)

Arguments

Details

Sensitivity is the probability that a sample of class X will be predicted as the same class X. High sensitivity detect true positives. Sensitivity = TP / (TP + FN) TP - True Positives: Example for class A, TP = Paa FN - False Negatives: Example for class A, FN = Pab + Pac + Pax Confusion Matrix: [ Predicted Class ] ClassA ClassB ClassC "misclass" ClassA Paa Pab Pac Pax ClassB Pba Pbb Pbc Pbx ClassC Pca Pcb Pcc Pcx

Value

A vector with the sensitivities of prediction for every class.

Author(s)

Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf

References

Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675

See Also

For more information see BigBang. *classPredictionMatrix(), *confusionMatrix().

Examples

   #bb is a BigBang object
   cpm <- classPredictionMatrix(bb)
   cpm
   cm <- confusionMatrix(bb)
   cm
   #equivalent and quicker because classPredictionMatrix is provided
   cm <- confusionMatrix(bb, cpm)
   cm
 
   specificityClass(bb, cm)
   specificityClass(bb, cpm)
   specificityClass(bb)
   # all are equivalent
   sensitivityClass(bb, cpm)
   sensitivityClass(bb, cm)
   sensitivityClass(bb)
   # all are equivalent