likestim.loc: Maximum likelihood estimation per locus of the number of...
In forensim: Statistical tools for the interpretation of forensic DNA mixtures

Description Usage Arguments Details Value Author(s) References See Also Examples

The likestim.loc function returns the estimation of the number of contributors, at each locus, obtained by maximizing the likelihood.

1	likestim.loc(mix, freq, refpop = NULL, theta = NULL, loc = NULL)

`mix`	a `simumix` object
`freq`	a `tabfreq` object containing the allele frequencies to use for the calculation
`refpop`	the reference population from which to extract the allele frequencies used in the likelihood calculation. Default set to NULL, if `tabfreq` contains more than one population, `refpop` must be specified
`theta`	a float from [0,1[ giving Wright's Fst coefficient. `theta` acounts for population subdivision while computing the likelihood of the data.
`loc`	loci to be considered in the estimation. Default (set to NULL) corresponds to all loci.

The number of contributors which maximizes the likelihood of the data observed in the mixture is searched in the discrete interval [1,6]. In most cases this interval is a plausible range for the number of contributors.

A matrix of dimension loc x 2. The first colum, max, gives the maximum likelihood estimation of the number of contributors for each locus in row. The second column, maxvalue, gives the corresponding likelihood value.

Hinda Haned <contact@hindahaned.info>

Haned H, Pene L, Lobry JR, Dufour AB, Pontier D. Estimating the number of contributors to forensic DNA mixtures: Does maximum likelihood perform better than maximum allele count? J Forensic Sci, accepted 2010.

Egeland T , Dalen I, Mostad PF. Estimating the number of contributors to a DNA profile. Int J Legal Med 2003, 117: 271-275

Curran, JM , Triggs CM, Buckleton J , Weir BS. Interpreting DNA Mixtures in Structured Populations. J Forensic Sci 1999, 44(5): 987-995

likestim for multiloci estimations

data(strusa)
#simulation of 1000 genotypes from the Hispanic allele frequencies
gen<-simugeno(strusa,n=c(0,0,100))
#4-person mixture
mix4 <- simumix(gen,ncontri=c(0,0,4))
likestim.loc(mix4,strusa,refpop="Hisp")