contLikStutter: contLikStutter

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

Description

contLikStutter evaluates the marginal likelihood of the STR DNA mixture given some assumed a bayesian model by integrate out parameters.

Usage

contLikStutter(nC, mixData, popFreq, refData = NULL, condOrder = NULL,
  xi = NULL, prC = 0, model = 2, pTau = function(x) {     return(1) },
  taumax = 100, maxeval = 5000, threshT = 50, fst = 0, lambda = 0,
  pXi = function(x) {     return(1) })

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!
xi	A numeric giving stutter-ratio if it is known. Default is NULL, meaning it is integrated out.
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 1000.
maxeval	Maxumum number of evaluations in the interale function. Default is 5000.
threshT	The detection threshold given. Used when considering probability of allele drop-outs.
fst	is the coancestry coeffecient. Default is 0.
lambda	Parameter in modeled peak height shifted exponential model. Default is 0.
pXi	Prior function for xi-parameter (stutter). Flat prior on [0,1] is default.

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.

Model 1 is gaussian model: yj~N(sum(y)/2*nj*m,sum(y)*tau). Inspired by Tvedebrink. Model 2 is gamma model: yj~N(sum(y)/(2*tau)*nj*m,tau). Inspired by Cowell.

Function calls procedure in c++ by using the package Armadillo and Boost.

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.