Markov chain Monte Carlo methods for estimating the joint posterior distribution of a pedigree and the parameters that predict its structure using genetic and non-genetic data. These parameters can be associated with covariates of fecundity such as a sexually selected trait or age, or can be associated with spatial or heritable traits that relate parents to specific offspring. Population size, allele frequencies, allelic dropout rates, and stochastic genotyping error rates can also be simultaneously estimated.
MCMCped(PdP=PdataPed(), GdP=GdataPed(), sP=startPed(), tP=tunePed(),
pP=priorPed(), mm.tol=999, nitt = 13000, thin = 10, burnin =
3000, write_postG = FALSE, write_postA=FALSE, write_postP =
"MARGINAL", checkP = FALSE, jointP = TRUE, DSapprox=FALSE, verbose=TRUE)optional PdataPed object containing phenotypic data
optional GdataPed object containing genetic data
optional startPed object containing starting parameterisation
optional tunePed object containg tuning parameters for Metropolis Hastings updates
optional priorPed object containg prior specifications
maximum number of mismatches tollerated
number of MCMC iterations
thinning interval of the Markov chain
the number of initial iterations to be discarded
if TRUE the marignal posterior distribution of true genotypes is stored
if TRUE the joint posterior distribution of allele frequencies is stored
if "MARGINAL" the marginal distribution of parents is stored. If "JOINT" the joint distribution of parents (the pedigree) is stored.
if TRUE the pedigree is checked for legality, and illegal pedigrees rejected. If FALSE it is assumed that any potential parent would produce a legal pedigree, i.e one without circuits, in the terminology of graph theory.
if TRUE both parents are sampled simultaneously, if FALSE each parent is sampled conditional on the other. TRUE should mix faster, but FALSE should iterate faster, especially when relational="MATE" is passed to varPed
if TRUE the likelihood for models in which a relational="MATE" variable is passed is approximated. This can be much more efficient because the denominator of the multinomial is the summed linear pedictors for combinations in which i=m or j=m where m referes to the "MATE" at the current iteration.
if TRUE posterior samples and the Metropolis Hastings accpetance rates of beta, USdam, USsire, E1, E2 are printed to the screen every 1000 iterations.
an mcmc object containing samples from the posterior distribution of the population level parameters
an mcmc object containing samples from the posterior distribution of the number of unsampled females
an mcmc object containing samples from the posterior distribution of the number of unsampled males
an mcmc object containing samples from the posterior distribution of allelic dropout rates for codominant markers or the probability of mis-scoring a dominant allele as recessive for dominant markers
an mcmc object containing samples from the posterior distribution of stochasting genotyping error rates for codominant markers or the probability of mis-scoring a recessive allele as dominant for dominant markers
list of marginal distributions of true genotypes at each locus
list of mcmc objects containing samples from the posterior distribution of the base population allele frequencies at each locus
either samples from the posterior distribution of the pedigree, or the marginal distribution of parents
Hadfield J.D. et al (2006) Molecular Ecology 15 3715-31
# NOT RUN {
data(WarblerP)
data(WarblerG)
GdP<-GdataPed(WarblerG)
var1<-expression(varPed(c("lat", "long"), gender="Male",
relational="OFFSPRING"))
# paternity is to be modelled as a function of distance
# between offspring and male territories
res1<-expression(varPed("offspring", restrict=0))
# indivdiuals from the offspring generation are excluded as parents
res2<-expression(varPed("terr", gender="Female", relational="OFFSPRING",
restrict="=="))
# mothers not from the offspring territory are excluded
PdP<-PdataPed(formula=list(var1,res1,res2), data=WarblerP, USsire=FALSE)
tP<-tunePed(beta=30)
model1<-MCMCped(PdP=PdP, GdP=GdP, tP=tP, nitt=300, thin=1, burnin=0)
plot(model1$beta)
# }
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