rjmcmcpost: Perform Reversible-Jump MCMC Post-Processing

In rjmcmc: Reversible-Jump MCMC Using Post-Processing

Description

Performs Bayesian multimodel inference, estimating Bayes factors and posterior model probabilities for N candidate models. Using the 'universal parameter' restriction in Barker & Link (2013), RJMCMC is treated as a Gibbs sampling problem, where the algorithm alternates between updating the model and the model specific parameters. Transformation Jacobians are computed using automatic differentiation so do not need to be specified.

Usage

rjmcmcpost(post.draw, g, ginv, likelihood, param.prior, model.prior,
  chainlength = 10000, TM.thin = chainlength/10, save.all = FALSE,
  progress = TRUE)

Arguments

post.draw	A list of N functions that randomly draw from the posterior distribution under each model. Generally these functions sample from the output of a model fitted using MCMC.
g	A list of N functions specifying the bijections from the universal parameter psi to each model-specific parameter set.
ginv	A list of N functions specifying the bijections from each model-specific parameter set to psi. These are the inverse transformations of g.
likelihood	A list of N functions specifying the log-likelihood functions for the data under each model.
param.prior	A list of N functions specifying the log-prior distributions for each model-specific parameter vector.
model.prior	A numeric vector of the prior model probabilities. Note that this argument is not required to sum to one as it is automatically normalised.
chainlength	How many iterations to run the Markov chain for.
TM.thin	How regularly to calculate transition matrices as the chain progresses.
save.all	A logical determining whether to save the value of the universal parameter at each iteration, as well as the corresponding likelihoods, priors and posteriors. If TRUE, the output object occupies significantly more memory.
progress	A logical determining whether a progress bar is drawn.

Value

Returns an object of class rj (see rjmethods). If save.all=TRUE, the output has named elements result, densities, psidraws, progress and meta. If save.all=FALSE, the densities and psidraws elements are omitted.

result contains useful point estimates, progress contains snapshots of these estimates over time, and meta contains information about the function call.

References

Barker, R. J. and Link, W. A. (2013) Bayesian multimodel inference by RJMCMC: A Gibbs sampling approach. The American Statistician, 67(3), 150-156.

See Also

adiff getsampler defaultpost

Examples

## Comparing two binomial models -- see Barker & Link (2013) for further details.

y=c(8,16); sumy=sum(y)
n=c(20,30); sumn=sum(n)

L1=function(p){if((all(p>=0))&&(all(p<=1))) sum(dbinom(y,n,p,log=TRUE)) else -Inf}
L2=function(p){if((p[1]>=0)&&(p[1]<=1)) sum(dbinom(y,n,p[1],log=TRUE)) else -Inf}

g1=function(psi){p=psi}
g2=function(psi){w=n[1]/sum(n); p=c(w*psi[1]+(1-w)*psi[2],psi[2])}
ginv1=function(p){p}
ginv2=function(p){c(sum(n)/n[1]*p[1]-n[2]/n[1]*p[2],p[2])}

p.prior1=function(p){sum(dbeta(p,1,1,log=TRUE))}
p.prior2=function(p){dbeta(p[1],1,1,log=TRUE)+dbeta(p[2],17,15,log=TRUE)}

draw1=function(){rbeta(2,y+1,n-y+1)}
draw2=function(){c(rbeta(1,sumy+1,sumn-sumy+1),rbeta(1,17,15))}

out=rjmcmcpost(post.draw=list(draw1,draw2), g=list(g1,g2), ginv=list(ginv1,ginv2),
               likelihood=list(L1,L2), param.prior=list(p.prior1,p.prior2),
               model.prior=c(0.5,0.5), chainlength=1500)

rjmcmc documentation built on July 9, 2019, 5:03 p.m.