contLikDrop: contLikDrop

In bayesdnamix: Procedure for evaluate marginal likelihood for quantitative STR DNA mixture bayesian models

Description

contLikDrop evaluates the marginal likelihood of the STR DNA mixture given some assumed model by integrate out parameters for given allele drop-out probability for each contributors.

Usage

contLikDrop(nC, mixData, popFreq, refData = NULL, condOrder = NULL,
  prD = NULL, prC = 0, model = 1, pTau = function(x) {     return(1) },
  taumax = 100, maxeval = 10000, threshT = 50, fst = 0)

Arguments

nC	Number of contributors in model.
mixData	Evidence object with list elements adata[[i]] and hdata[[i]]. Each element has a loci-list with list-element 'i' storing qualitative data in 'adata' and quantitative data in 'hdata'.
popFreq	A list of allele frequencies for a given population.
refData	Reference objects with list element [[s]]$adata[[i]]. The list element has reference-list with list-element 's' having a loci-list adata with list-element 'i storing qualitative data.
condOrder	Specify conditioning references from refData (must be consistent order). For instance condOrder=(0,2,1,0) means that we restrict the model such that Ref2 and Ref3 are respectively conditioned as 2. contributor and 1. contributor in the model. condOrder=-1 means the reference is known-non contributor!
prD	A vector of allele drop-out probabilities (p_hom=p_het^2) for each contributors. Default is 0.
prC	A numeric for allele drop-in probability. Default is 0.
model	A integer for specification of model. See details for more information.
pTau	Prior function for tau-parameter. Flat prior is default.
taumax	Maximum range of tau-parameter. Default is 100.
maxeval	Maxumum number of evaluations in the interale function.
threshT	The analytical threshold given. Used when considering allele drop-outs.
fst	is the coancestry coeffecient. Default is 0.

Details

The procedure are doing numerical integration to approximate the marginal probability by integrate over noisance parameters. Mixture proportions have flat prior.

The user may specify probability of drop-out for each contributors.

The peak heights are scaled between [0,1] such that the peak heights are not accounted for in the models.

Function calls procedure in c++ by using the package Armadillo

Models: model='0'-unit weights,'1'-mixsep

Value

lik Marginalized likelihood of the hypothesis (model) given observed evidence.

Author(s)

Oyvind Bleka <Oyvind.Bleka.at.fhi.no>

bayesdnamix documentation built on May 2, 2019, 6:51 p.m.