logistic.apply.calibration: Calculate the calibrated LRs with the model precomputed

Description

This function perform the logistic calibration on the provided data. In the context of likelihood ratios, the 'ideal' value for the LR is Infinity for the same source dataset, and 0 for the different-sources dataset. The 'post' values are fixed to 1 for the same source and 0 for the same different-sources datasets (corresponding to the posterior probability P(H_ss|E)).

Usage

logistic.apply.calibration(LR, model)

Value

a list with the calibrated LR values

Arguments

LR: a vector of likelihood ratios to be calibrated (raw values).
model: a logistic.calibrate.set() fitted model to be applied. This variable can be the reture of the logistic.calibrate.set() or the logistic.calibrate.set()$fit variable.

Author

Marco De Donno

Examples

Run this code

 # the list of LRs for the same source proposition
LR.same = c(0.5, 2, 4, 6, 8, 10)
# the list of LRs for the different source proposition
LR.different = c(0.2, 0.4, 0.6, 0.8, 1.1)
# compute the logistic calibration on the data
model = logistic.calibrate.get.model(LR.same, LR.different) 
 # the list of news LRs (to be calibrated)
LR.unknown = c(0.6, 0.7, 1.2, 5)
# compute the calibrated LRs for the list with the model
logistic.apply.calibration(LR.unknown, model)

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