| calc.ece {comparison} | R Documentation |
Empirical cross-entropy (ECE) calculation
Description
Calculates the empirical cross-entropy (ECE) for likelihood ratios from a sequence same and different item comparisons.
Usage
calc.ece(LR.ss, LR.ds, prior = seq(from = 0.01, to = 0.99, length = 99))
Arguments
LR.ss |
a vector of likelihood ratios (LRs) from same source calculations |
LR.ds |
a vector of LRs from different source calculations |
prior |
a vector of ordinates for the prior in ascending order, and between 0 and 1. Default is 99 divisions of 0.01 to 0.99. |
Details
Acknowledgements
The function to calculate the values of the likelihood ratio for the
calibrated.set draws heavily upon the opt_loglr.m function from
Niko Brummer's FoCal package for Matlab.
Value
Returns an S3 object of class ece
Author(s)
David Lucy
References
Ramos, D. & Gonzalez-Rodriguez, J. (2008) Cross-entropy analysis of the information in forensic speaker recognition; IEEE Odyssey. Zadora, G. & Ramos, D. (2010) Evaluation of glass samples for forensic purposes - an application of likelihood ratio model and information-theoretical approach. Chemometrics and Intelligent Laboratory: 102; 63-83.
See Also
isotone::gpava(), calibrate.set()
Examples
LR.same = c(0.5, 2, 4, 6, 8, 10) # the same has 1 LR < 1
LR.different = c(0.2, 0.4, 0.6, 0.8, 1.1) # the different has 1 LR > 1
ece.1 = calc.ece(LR.same, LR.different) # simplest invocation
plot(ece.1) # use plot method