calc.ece: Empirical cross-entropy (ECE) calculation

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/calc.ece.R

Description

Calculates the empirical cross-entropy (ECE) for likelihood ratios from a sequence same and different item comparisons.

Usage

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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

@references D. Ramos and J. Gonzalez-Rodrigues, (2008) "Cross-entropy analysis of the information in forensic speaker recognition," in Proc. IEEE Odyssey, Speaker Lang. Recognit. Workshop. 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

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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

Example output



comparison documentation built on Aug. 5, 2020, 5:07 p.m.