bmk (version 1.0)

bmkconverge: bmkcoverge: Convergence via the Hellinger distance

Description

MCMC chain convergence diagnostic.

Usage

bmkconverge(inputlist1, binsize = 1000)

Arguments

inputlist1

A list of the MCMC chains

binsize

a scalar giving how large each bin should be for consecutive batches. outputs the Hellinger distances between the sampled distribution for one scenario against the other.

Details

This takes an MCMC chain and divides it into batches of size binsize and calculates the Hellinger distance between consecutive batches.

References

Boone EL, Merrick JR and Krachey MJ. A Hellinger distance approach to MCMC diagnostics. Journal of Statistical Computation and Simulation, DOI:10.1080/00949655.2012.729588.

Examples

## Not run: 
# library(dismo); library(MCMCpack);
# data(Anguilla_train)
# b0mean <- 0
# b0precision <- (1/5)^2
# mcmclen = 1000
# burn=10000
# MCMC.one <- MCMClogit(Angaus ~ SegSumT+DSDist+USNative+as.factor(Method)+DSMaxSlope+USSlope,
#                 data=Anguilla_train,burnin=burn, mcmc=mcmclen, beta.start=-1,
#                 b0=b0mean, B0=b0precision)
#  ## End(Not run)
 data(MCMCsamples)
 mcmclen <- 1000
 bmkconverge(MCMC.one,mcmclen/10)