An inferior may to estimate the drop-out probability compared to using the peak heights from the electropherogram. However, to compare the performance with Gill et al. (2007) this implements a theoretical approach based on their line of arguments.
estimatePD(n0, m, pnoa = NULL, probs = NULL, theta = 0, locuswise = FALSE)
| n0 | Vector of observed allele counts - same length as the number of loci |
|---|---|
| m | The number of contributors |
| pnoa | The vector of \(\P(N(m)=n)\) for \(n=1,\ldots,2Lm\), where \(L\) is the number of loci and \(m\) is the number of contributors OR |
| probs | List of vectors with allele probabilities for each locus |
| theta | The coancestery coefficient |
| locuswise | Logical. Indicating whether computations should be done locuswise. |
Returns the MLE of \(\Pr(D)\) based on equation (10) in Tvedebrink (2014)
Computes the \(\Pr(D)\) that maximises equation (10) in Tvedebrink (2014).
Gill, P., A. Kirkham, and J. Curran (2007). LoComatioN: A software tool for the analysis of low copy number DNA profiles. Forensic Science International 166(2-3): 128 - 138.
T. Tvedebrink (2014). 'On the exact distribution of the number of alleles in DNA mixtures', International Journal of Legal Medicine; 128(3):427--37. <https://doi.org/10.1007/s00414-013-0951-3>
## Simulate some allele frequencies: freqs <- simAlleleFreqs() ## Assume 15 alleles are observed in a 2-person DNA mixture with 10 loci: estimatePD(n0 = 15, m = 2, probs = freqs)#> p(D) #> 0.5380859