comparison (version 1.0.8)

calc.ece: 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))

Value

Returns an S3 object of class ece

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.

Author

David Lucy

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.

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