lr_compute_pigmentation: Compute Likelihood Ratios for Pigmentation Traits

In mispitools: Missing Person Identification Tools

Compute Likelihood Ratios for Pigmentation Traits

Description

Computes likelihood ratios (LRs) for each unique combination of hair color, skin color, and eye color by dividing conditioned proportions (numerators) by reference proportions (denominators).

Usage

lr_compute_pigmentation(conditioned, unconditioned)

Arguments

conditioned	A data.frame with columns hair_colour, skin_colour, eye_colour, and numerators. Typically output from compute_conditioned_prop.
unconditioned	A data.frame with columns hair_colour, skin_colour, eye_colour, and f_h_s_y. Typically output from compute_reference_prop.

Value

A data.frame with:

• hair_colour, skin_colour, eye_colour: Trait combination

• f_h_s_y: Population frequency (denominator)

• numerators: Conditioned probability (numerator)

• LR: Likelihood ratio = numerators / f_h_s_y

Combinations not present in both inputs are excluded.

References

Marsico FL, et al. (2023). "Likelihood ratios for non-genetic evidence in missing person cases." Forensic Science International: Genetics, 66, 102891. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fsigen.2023.102891")}

See Also

compute_conditioned_prop for computing numerators, compute_reference_prop for computing denominators, lr_pigmentation for simulating LR distributions.

Examples

# Generate population data
pop_data <- sim_reference_pop(n = 500, seed = 123)

# Compute proportions
conditioned <- compute_conditioned_prop(pop_data, 1, 1, 1, 0.01, 0.01, 0.01)
unconditioned <- compute_reference_prop(pop_data)

# Compute LRs
lrs <- lr_compute_pigmentation(conditioned, unconditioned)
head(lrs)

# Highest LRs (most discriminating combinations)
lrs[order(-lrs$LR), ][1:5, ]

mispitools documentation built on Aug. 26, 2026, 1:08 a.m.