LRpowerPlot: Plot LR distributions under two hypotheses

In forrel: Forensic Pedigree Analysis and Relatedness Inference

Plot LR distributions under two hypotheses

Description

Simulates genetic data under each of two pedigree hypotheses and calculates the LR comparing H1 against H2 for each simulation. The resulting log10 LR distributions are plotted together, illustrating how well the hypotheses can be distinguished. The plot also shows the density overlap and, if a threshold is given, the exceedance probabilities under each hypothesis.

Usage

LRpowerPlot(
  numeratorPed = NULL,
  denominatorPed = NULL,
  ids = NULL,
  markers = NULL,
  nsim = 500,
  seed = NULL,
  threshold = 10000,
  data = NULL,
  returnData = FALSE,
  title = NULL,
  bw = NULL,
  col = c("#E69F00", "#0072B2"),
  verbose = TRUE
)

Arguments

numeratorPed, denominatorPed	Pedigrees describing H1 and H2. If denominatorPed is NULL, H2 is formed by treating all members of H1 as unrelated.
ids	Individuals to simulate.
markers	Marker names or indices to include, or a named list of frequency vectors defining new markers. By default all attached markers.
nsim	Number of simulations under each hypothesis.
seed	Integer seed for the random number generator.
threshold	An LR threshold. If given, the plot includes exceedance probabilities. Default: 10000.
data	Precomputed data, either output from LRpowerPlot(..., returnData = TRUE) or a list of two LRpowerResult objects, with H1 true first and H2 true second.
returnData	If TRUE, return the simulated log10 LRs instead of a plot.
title	Plot title.
bw	Density bandwidth on the log10 LR scale. By default it is estimated from the pooled simulations. Increase bw for smoother curves and decrease it to show more detail.
col	Two colours for the H1-true and H2-true distributions.
verbose	A logical.

Value

A ggplot object, or if returnData = TRUE, a data frame with columns hypothesis, sim and log10LR.

See Also

LRpower()

Examples

if(requireNamespace("ggplot2", quietly = TRUE)) {

db = NorwegianFrequencies[1:5]  # small example database

### Example 1: Sibs vs unrelated (increase nsim!)
ids = c("A", "B")
H1 = nuclearPed(children = ids)

LRpowerPlot(H1, ids = ids, markers = db, nsim = 10, seed = 123)


### Example 2: Full sibs vs half sibs
ids = c("A", "B")
H1 = nuclearPed(children = ids)
H2 = halfSibPed() |> relabel(old = 4:5, new = ids)

LRpowerPlot(H1, H2, ids = ids, markers = db, nsim = 10, seed = 123,
            title = "H1: Full sibs, H2: Half sibs")


### Example 3: Full sibs vs half sibs, including shared parent
ids = c("A", "B", "C")
H1 = nuclearPed(fa = ids[1], children = ids[2:3])
H2 = halfSibPed() |> relabel(old = c(2,4:5), new = ids)

LRpowerPlot(H1, H2, ids = ids, markers = db, nsim = 10, seed = 123,
            title = "Full vs. half sibs, when parent is available")


# Example 4: Paternity case (requires mutation modelling!)
H1 = nuclearPed() |>
  setMarkers(locusAttributes = db) |>
  setMutmod(model = "equal", rate = 0.01)

LRpowerPlot(H1, ids = c(1,3), nsim = 10, seed = 123)

# Alternative syntax: With returnData = TRUE
dat = LRpowerPlot(H1, ids = c(1,3), nsim = 10, returnData = TRUE)

LRpowerPlot(data = dat, threshold = 1e6, col = 2:3)

}

forrel documentation built on Oct. 6, 2026, 5:06 p.m.