KinMix: Create a DNA mixture model, with possibly related...

In KinMixLite: Inference About Relationships from DNA Mixtures

Create a DNA mixture model, with possibly related contributors

Description

Create a DNA mixture model, with possibly related contributors.

Usage

KinMix(data,k,C,database,K=character(0),reference.profiles = NULL, 
	contribs=NULL,typed.gts=NULL,IBD=NULL,targets=NULL,pars=NULL,mle=FALSE,
	dir=character(0),domainnamelist=NULL,
	load=FALSE,write=FALSE,dyes=NULL, 
	triangulate=TRUE,compile=TRUE,compress=TRUE,use.order=TRUE)

Arguments

data	A list containing one data.frame for each DNA mixture. Note, that in the special case of analysing just one mixture, this still has to be specified as list(data). Each dataset should contain variables marker, allele, and frequency. Optionally, also a column for each reference profile specified in K.
k	Number of contributors.
C	A list of thresholds, one for each mixture.
database	A data.frame containing at least variables marker, allele, frequency.
K	Names of reference profiles; these can be chosen freely, but should match (possibly only a subset of) the names specified by the reference profiles.
reference.profiles	A data.frame containing allele counts for each reference profile, if not specified in data.
contribs	vector of character tags identifying contributors to the mixture
typed.gts	list of named genotype profiles
IBD	IBD pattern distribution, or any object accepted as an argument to as.IBD
targets	vector of character tags identifying individuals related according to IBD
pars	optionally, a mixpar object providing parameter values for peak height model
mle	logical: should mixML be called to estimate parameters by MLE?
dir	Location of network files if loading or saving the networks.
domainnamelist	Names of marker-wise network files (without hkb-extension). Default is the set of markers.
load	Read networks from disk?
write	Save networks as hkb files?
dyes	A list containing a list of dyes indexed by markers
triangulate	Triangulate the networks? Default is to triangulate the network using a good elimination order.
compile	Compile the networks?
compress	Compress the network? Defaults to TRUE and is strongly recommended for models with a large number of contributors.
use.order	Should the default elimination order be used for triangulation? Otherwise the "total.weight" heuristic for triangulation in Hugin is used.

Details

Generalises DNAmixture to allow relatedness as in rpt.IBD

Value

An object of class DNAmixture. This contains amongst other things

markers	The joint set of markers used for the mixtures specified.
domains	For models involving unknown contributors, a list containing one Bayesian network (hugin.domain) per marker; see buildMixtureDomains for details on the networks
data	A list containing for each marker the combined allele frequencies, peak heights, and reference profiles as produced by DNAmixtureData.

It may also contain

mle	Maximum likelihood estimates of the peak height model parameters
logL	log-likelihood of model evaluated at pars

See Also

[DNAmixturesLite]{DNAmixture}

Examples

data(test2data)

## Fit 2-person mixture - baseline model

mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db)
pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3)))
baseline<-logL(mixD)(pars)
baseline

mixD<-DNAmixture(list(epg),k=2,C=list(0.001),database=db)
pars<-mixpar(rho=list(2),eta=list(100),xi=list(0.1),phi=list(c(U1=0.7,U2=0.3)))
mlD <- mixML(mixD, pars)
print(mlD$mle)
pars<-mlD$mle

baseline<-logL(mixD)(pars)
baseline

## Fit 2-person mixture model in which contributor 1 is parent of a typed individual Cgt

mixD<-KinMix(list(epg),k=2,C=list(0.001),database=db,
	contribs=c('F'),typed.gts=list(C=Cgt),IBD='parent-child',targets=c('F','C'),
	pars=pars)
log10LR<-(mixD$logL-baseline)/log(10)
cat('log10 LR',log10LR,'\n')

## Fit 2-person mixture model in which contributor 1 is father of a typed individual Cgt 
## with mother Mgt

mixD<-KinMix(list(epg),k=2,C=list(0.001),database=db,
	contribs=c('F'),typed.gts=list(M=Mgt,C=Cgt),IBD='trio',targets=c('F','M','C'),
	pars=pars)
log10LR<-(mixD$logL-baseline)/log(10)
cat('log10 LR',log10LR,'\n')

KinMixLite documentation built on Aug. 8, 2025, 7 p.m.