getsampler: Define Function To Sample From MCMC Output

In rjmcmc: Reversible-Jump MCMC Using Post-Processing

Description

A utility function which accepts a matrix of MCMC output and creates a function which samples from the posterior distribution for the parameters of the model.

Usage

getsampler(modelfit, sampler.name = "post.draw", order = "default",
  envir = .GlobalEnv)

Arguments

modelfit	A matrix of output from a previously-run MCMC algorithm, with one column per variable and one row per iteration.
sampler.name	A string giving the desired name for the function to be defined.
order	A numeric vector of indices specifying the desired parameters to extract from modelfit, and in which order.
envir	The environment in which to define the sampling function. Defaults to the global environment.

Value

A function is defined in envir which randomly samples from the posterior distribution for the parameters. Note that this function does not take any arguments. A function generated in this way is suitable for passing to the rjmcmcpost function.

References

Plummer, M. (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on distributed statistical computing (Vol. 124, p. 125).

See Also

rjmcmcpost

Examples

# Generate synthetic 'MCMC output' for a model with 3 parameters. There is
# one column per parameter, and 1000 iterations.
matrix_output = matrix(c(runif(1000,0,1), rnorm(1000,5,2), rgamma(1000,2,2)), 1000, 3)

getsampler(modelfit=matrix_output, sampler.name="posterior1")
set.seed(100)
posterior1()

## Alternatively
posterior1b = getsampler(modelfit=matrix_output)  # this successfully defines a function named
# posterior1b but also defines an identical function corresponding to the value 
# of sampler.name, i.e. the default "post.draw" in this case.
set.seed(100)
posterior1b()
set.seed(100)
posterior1()

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