priorPed {MasterBayes} | R Documentation

## 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 of allelic dropout. 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 of stochastic genotyping error rates. Rows correspond to error rate categories, columns to the beta shape parameters. The order of rows in E2 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 are passed to the formula argument of `PdataPed`, with interactions appearing last. (see `dmvnorm` in the package mvtnorm)
`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`)
`...` | further arguments to be passed

### Value

list containing the arguments passed

### Author(s)

Jarrod Hadfield j.hadfield@sheffield.ac.uk

### See Also

`MCMCped`

### Examples

    # 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))

[Package MasterBayes version 2.0 Index]