calc.ece: Empirical cross-entropy calculation

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

View source: R/calc.ece.r

Description

Calculates the empirical cross-entropy 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

array of likelihood ratios for same source item comparisons

LR.ds

array of likelihood ratios for different source item comparisons

prior

array of ordinates for the prior in ascending order, and between 0 and 1. Default is 99 divisions of 0.01 to 0.99

Value

Returns an S4 object of class ece

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.

Note

The empirical cross-entropy for a set of comparisons for items of known origin can be used as a measure of performance of the comparisons. This function takes the likelihood ratios.

Author(s)

David Lucy [email protected] - http://www.maths.lancs.ac.uk/~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

gpava
ece
calibrate.set
calc.ece

Examples

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#library(comparison)
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

comparison documentation built on May 29, 2017, 9:08 a.m.