Description Usage Arguments Examples

Computes KIs for case profile(s) with all database profiles

1 2 3 | ```
ki.db(x, db, hyp.1, hyp.2 = "UN", freqs = get.freqs(x),
markers = intersect(get.markers(x), get.markers(db)), theta = 0,
ret.per.marker = FALSE, precomputed.kis)
``` |

`x` |
An integer matrix specifying either a single profile or a number of profiles. Alternatively an integer vector containing a single profile, e.g. obtained when a row is selected from a matrix of profiles. |

`db` |
An integer matrix which is the database of profiles. |

`hyp.1` |
A character string giving the hypothesis in the numerator of the |

`hyp.2` |
A character string giving the hypothesis in the denominator of the |

`freqs` |
A list specifying the allelic frequencies. Should contain a vector of allelic frequencies for each locus, named after that locus. |

`markers` |
Character vector stating the markers to use in the KI computation. Defaults to the intersection of the markers of |

`theta` |
Numeric value specifying the amount of background relatedness. |

`ret.per.marker` |
Logical. If TRUE, return a matrix of KIs, where the columns correspond to markers. |

`precomputed.kis` |
(optionally) a list of precomputed KIs, returned by |

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 26 27 | ```
data(freqsNLsgmplus)
fr <- freqsNLsgmplus
# sample a profile, a database and compute the Sibling Index (SI) with all database members
x <- sample.profiles(N=1,fr)
db <- sample.profiles(N=10^4,fr)
si <- ki.db(x,db=db,"FS")
# estimate the exceedance probabilities of an SI-threshold
t <- 1 # choose threshold SI=1
x <- sample.profiles(N=1,fr)
sibs <- sample.relatives(x,N=10^4,type="FS")
unrelated <- sample.profiles(N=10^4,fr)
mean(ki.db(x,db=sibs,"FS")>=t) # the vast majority of true siblings has an SI>=1
mean(ki.db(x,db=unrelated,"FS")>=t) # a few percent of unrelated persons have SI >= 1
# estimate distribution of SI for true siblings and unrelated persons
x <- sample.profiles(N=1,fr) #sample profile
sibs <- sample.relatives(x,N=10^4,type="FS") #sample sibs
unrelated <- sample.profiles(N=10^4,fr) #sample unrelated persons
sibs.si <- ki.db(x,db=sibs,"FS") #compute SI for true siblings
unrelated.si <- ki.db(x,db=unrelated,"FS") #compute SI for unrelated persons
#plot density estimates of SI
plot(density(log10(unrelated.si)),xlim=c(-10,10),lty="dashed",
xlab=expression(log[10](SI)),main="SI for true sibs and unrelated profiles")
lines(density(log10(sibs.si)))
``` |

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