two.level.density.LR: Calculate the likelihood ratio using multivariate KDEs

In comparison: Multivariate Likelihood Ratio Calculation and Evaluation

Calculate the likelihood ratio using multivariate KDEs

Description

Takes a compitem object which represents some control item, and a compitem object which represents a recovered item, then uses information from a compcovar object, which represents the information from the population, to calculate a likelihood ratio as a measure of the evidence given by the observations for the same/different source propositions.

Usage

two.level.density.LR(control, recovered, background)

Arguments

control	a compitem object with the control item information.
recovered	a compitem object with the recovered item information.
background	a compcovar object with the population information.

Value

an estimate of the likelihood ratio

References

Aitken, C.G.G. & Lucy, D. (2004) Evaluation of trace evidence in the form of multivariate data. Applied Statistics: 53(1); 109-122.

Examples

library(comparison)
# load Greg Zadora's glass data
data(glass)

# calculate a compcovar object based upon glass
# using K, Ca and Fe - warning - could take time
# on slower machines
Z = two.level.components(glass, c(7,8,9), 1)

# calculate a compitem object representing the control item
control = two.level.comparison.items(glass[1:6,], c(7,8,9))

# calculate a compitem object representing the recovered item
# known to be from the same item (item 1)
recovered.1 = two.level.comparison.items(glass[7:12,], c(7,8,9))

# calculate a compitem object representing the recovered item
# known to be from a different item (item 2)
recovered.2 = two.level.comparison.items(glass[19:24,], c(7,8,9))


# calculate the likelihood ratio for a known
# same source comparison - should be 20.59322
# 2020-08-01 Both this version and the previous version return 20.58967
lr.1 = two.level.density.LR(control, recovered.1, Z)
lr.1

# calculate the likelihood ratio for a known
# different source comparison - should be 0.02901532
# 2020-08-01 Both this version and the previous version return 0.01161392
lr.2 = two.level.density.LR(control, recovered.2, Z)
lr.2

comparison documentation built on Aug. 25, 2023, 5:14 p.m.