Description Usage Arguments Details Value Examples

Estimate LR exceedance probabilities (with importance sampling)

1 |

`t` |
numeric (vector), threshold |

`dists` |
list of per-locus probability distributions of a likelihood ratio |

`N` |
integer, number of samples |

`dists.sample` |
if dists.sample is not equal to dists, then importance sampling is applied. Sampling is done according to dists.sample, while exceedance probabilities are estimated for dists. |

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 estimates *q_{t|H}* by taking *N* samples from the distributions specified by the `dists`

parameter and computing the empirical fraction of the product of the samples that exceeds *t*.

Importance sampling can be used by supplying different distributions to sample from and to estimate the exceedance probabilities for. For instance, the exceedance probability for *H_d* can be estimated by sampling from *H_p* and an appropriate weighting of the samples. See the paper and examples for details.

numeric (vector) with estimated probabilities

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
data(freqsNLngm)
# dist of PI for true parent/offspring pairs
hp <- ki.dist(hyp.1="PO",hyp.2="UN",hyp.true="PO",freqs.ki=freqsNLngm)
# dist of PI for unrelated pairs
hd <- ki.dist(hyp.1="PO",hyp.2="UN",hyp.true="UN",freqs.ki=freqsNLngm)
set.seed(100)
# estimate P(PI>1e6) for true PO
sim.q(t=1e6,dists=hp)
# estimate P(PI>1e6) for unrelated pairs
sim.q(t=1e6,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=1e6,dists=hd,dists.sample=hp)
``` |

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