DNAtools (version 0.2-5)

estimatePD: Estimate the drop-out probability based on number of alleles

Description

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.

Usage

estimatePD(n0, m, pnoa = NULL, probs = NULL, theta = 0, locuswise = FALSE)

Value

Returns the MLE of \(\Pr(D)\) based on equation (10) in Tvedebrink (2014)

Arguments

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.

Author

Torben Tvedebrink

Details

Computes the \(\Pr(D)\) that maximises equation (10) in Tvedebrink (2014).

References

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>

Examples

  ## 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)