priorPed: priorPed Object

In MasterBayes: ML and MCMC Methods for Pedigree Reconstruction and Analysis

priorPed Object

Description

An object containing the prior specifiactions for a model fitted using MCMCped. If prior distributions are not specified then improper priors are used, and a proper posterior distribution cannot be gauranteed.

Usage

priorPed(E1=999, E2=999, beta=list(mu=999, sigma=999),
         USdam=list(mu=999, sigma=999),  
         USsire=list(mu=999, sigma=999))

Arguments

E1	matrix of parameters for the beta distribution specifying the prior distribution. If Wang's (2004) model of genotyping error for co-dominant markers is used this is the probability of an allele dropping out. If CERVUS's (Kalinowski, 2006; Marshall, 1998) model of genotyping error for co-dominant markers is used this parameter is not used. If Hadfield's (2009) model of genotyping error for dominant markers is used this is the probability of a dominant allele being scored as a recessive allele. Rows correspond to error rate categories, columns to the beta shape parameters. The order of rows in E1 are the order in which the error rate categories appear in the categories argument of GdataPed (see dbeta). If perlocus=TRUE was passed to GdataPed, then the error rate categories are replicated across loci
E2	matrix of parameters for the beta distribution specifying the prior distribution. If Wang's (2004) or CERVUS's (Kalinowski, 2006; Marshall, 1998) model of genotyping error for co-dominant markers are used this is the probability of an allele being miss-scored. In the CERVUS model errors are not independent for the two alleles within a genotype and so if a genotyping error has occurred at one allele then a genotyping error occurs at the other allele with probability one. Accordingly, E2(2-E2) is the per-genotype rate defined in CERVUS. If Hadfield's (2009) model of genotyping error for dominant markers is used this is the probability of a recessive allele being scored as a dominant allele. Rows correspond to error rate categories, columns to the beta shape parameters. The order of rows in E1 are the order in which the error rate categories appear in the categories argument of GdataPed (see dbeta). If perlocus=TRUE was passed to GdataPed, then the error rate categories are replicated across loci
beta	list containing a vector for the mean, and a matrix for the variance-covariances of a multivariate normal distribution, that specifies the prior distribution for the population level parameters. The order of beta is the order in which the parameters appear in the MCMC ouput.
USdam	list containing vectors of means and standard deviations for log normal distributions that specify the prior distribution for the number of unsampled females. The order of USdam is the order in which the unsampled dam populations appear in the USdam argument of PdataPed (see dlnorm)
USsire	list containing vectors of means and standard deviations for log normal distributions that specify the prior distribution for the number of unsampled males. The order of USsire is the order in which the unsampled sire populations appear in the USsire argument of PdataPed (see dlnorm)

Value

list containing the arguments passed

Author(s)

Jarrod Hadfield [e-mail]

See Also

MCMCped

Examples

## Not run: 
# When each individual has only been genotyped once, and no pedigree 
# information exists, there is virtually no information available
# to estimate error rates.  The tiny amount of information comes 
# (dangerously) from the assumption of Hardy-Weinburg equilibrium.  
# The posterior distribution is similar to the prior:

data(WarblerG)
A<-extractA(WarblerG)

ped<-matrix(NA, 100,3)
ped[,1]<-1:100

G<-simgenotypes(A, E1=0.01, E2=0.01, ped=ped, no_dup=1)
GdP<-GdataPed(G=G$Gobs, id=G$id)
pP<-priorPed(E1=matrix(c(40,1600), nrow=1), E2=matrix(c(40,1600), nrow=1))

model1<-MCMCped(GdP=GdP, pP=pP)

#The posterior distribution recovers the prior distribution 

summary(model1$E1)
quantile(rbeta(1000, 40, 1600), prob=c(0.025, 0.25, 0.5, 0.75, 0.975))

## End(Not run)

MasterBayes documentation built on June 22, 2022, 5:06 p.m.