tunePed {MasterBayes} | R Documentation

## tunePed Object

### Description

An object containing scaling constants for the tuning parameters used in the Metropolis-Hastings updates. The tuning parameters should be set so that the Metropolis-Hastings acceptance rates lie between 0.2 and 0.5. Initial tuning parameters for beta and the unsampled population size are obtained from the large sample variance-covariances of the Maximum Likelihood estimates.

### Usage

    tunePed(E1 = NULL, E2 = NULL, beta = NULL, USdam = NULL,
       USsire = NULL,...)

### Arguments

`E1` | vector of scaling parameters for the allelic dropout rates
`E2` | vector of scaling parameters for the stochastic error rates
`beta` | vector which is multiplied by `sqrt(10)` to get scaling parameters for beta
`USdam` | vector which is multiplied by `10` to get scaling parameters for the number of unsampled females
`USsire` | vector which is multiplied by `10` to get scaling parameters for the number of unsampled males
`...` | further arguments to be passed

### Details

The proposal distribution for all parameters is the multivariate normal, the variances of which are the large sample variance covariances of the Maximum Likelihood estimates multiplied by the scaling constants. For all parameters except `beta`, the covariance matrix for the proposal distribution has all off-diagonal elements set to zero. These parameters must be positive and so the proposal distribution is reflected at zero. A diagonal covariance matrices ensures that the proposal distribution remains symetric. For beta the covariances are not constrained at zero, and so the matrices are multiplied by the scaling constants in a way that preserves the correlational structure. The tuning parameters for the error rates are the scaling constants multiplied by 3e-5.

### Value

list containing the arguments passed

### Author(s)

Jarrod Hadfield j.hadfield@sheffield.ac.uk

### See Also

`MCMCped`

### Examples

    data(WarblerG)
    A<-extractA(WarblerG)

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

    G<-simgenotypes(A, ped=ped, E1=0.1, E2=0.001, no_dup=2)
    GdP<-GdataPed(G=G$Gobs, id=G$id)

    model1<-MCMCped(GdP=GdP, nitt=1500, thin=1, burnin=500)

    # The proposal distribution is to conservative for E1
    # and the update is accepted about 70% of the time

    plot(model1$E1)
    autocorr(model1$E1)

    # Succesive samples from the posterior distribution are
    # strongly autocorrelated.  Should of course run the chain
    # for longer with a larger thinning interval, but a greater
    # tuning parameter helps (now 3e-4, rather than 3e-5):

    model2<-MCMCped(GdP=GdP, tP=tunePed(E1=10), nitt=1500,
      thin=1, burnin=500)

    plot(model2$E1)
    autocorr(model2$E1)

[Package MasterBayes version 2.0 Index]