# exact.q: Compute exact LR exceedance probabilities In DNAprofiles: DNA Profiling Evidence Analysis

## Description

Compute exact LR exceedance probabilities

## Usage

 1 exact.q(t, dists) 

## Arguments

 t numeric (vector), threshold dists list of per-locus probability distributions of a likelihood ratio

## Details

For a combined likelihood ratio

LR=LR_1 LR_2 \times LR_m,

define q_{t|H} as the probability that the LR exceeds t under hypothesis H, i.e.:

q_{t|H} := P(LR>t|H).

The hypothesis H can be H_p, H_d or even another hypothesis. The current function computes q_{t|H} by brute force.

## Value

numeric (vector) with estimated probabilities

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 data(freqsNLsgmplus) x <- sample.profiles(N = 1, freqsNLsgmplus) # dist of PI for true parent/offspring pairs hp <- ki.dist(x = x, hyp.1="PO",hyp.2="UN",hyp.true="PO",freqs.ki=freqsNLsgmplus) # dist of PI for unrelated pairs hd <- ki.dist(x = x, hyp.1="PO",hyp.2="UN",hyp.true="UN",freqs.ki=freqsNLsgmplus) set.seed(100) # estimate P(PI>1e4) for true PO sim.q(t=1e4,dists=hp) # estimate P(PI>1e4) for unrelated pairs sim.q(t=1e4,dists=hd) # small probability, so no samples exceed t=1e6 # importance sampling can estimate the small probability reliably # by sampling from H_p and weighting the samples appropriately sim.q(t=1e4,dists=hd,dists.sample=hp) # compare to exact values exact.q(t = 1e4, dists=hp) exact.q(t = 1e4, dists=hd) 

DNAprofiles documentation built on Jan. 15, 2017, 9:27 p.m.