# calc.ece: Empirical cross-entropy (ECE) calculation In comparison: Multivariate Likelihood Ratio Calculation and Evaluation

## Description

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

## Usage

 `1` ```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`

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.

`isotone::gpava()`, `calibrate.set()`

## Examples

 ```1 2 3 4``` ```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.