mcgibbsit: Warnes and Raftery's MCGibbsit MCMC diagnostic

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

mcgibbsit provides an implementation of Warnes & Raftery's MCGibbsit run-length diagnostic for a set of (not-necessarily independent) MCMC samplers. It combines the estimate error-bounding approach of Raftery and Lewis with the between chain variance verses within chain variance approach of Gelman and Rubin.

Usage

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mcgibbsit(data, q=0.025, r=0.0125, s=0.95, converge.eps=0.001,
          correct.cor=TRUE)

Arguments

data

an ‘mcmc’ object.

q

quantile(s) to be estimated.

r

the desired margin of error of the estimate.

s

the probability of obtaining an estimate in the interval

converge.eps

Precision required for estimate of time to convergence.

correct.cor

should the between-chain correlation correction (R) be computed and applied. Set to false for independent MCMC chains.

Details

mcgibbsit computes the minimum run length Nmin, required burn in M, total run length N, run length inflation due to auto-correlation, I, and the run length inflation due to between-chain correlation, R for a set of exchangeable MCMC simulations which need not be independent.

The normal usage is to perform an initial MCMC run of some pre-determined length (e.g., 300 iterations) for each of a set of k (e.g., k=20) MCMC samplers. The output from these samplers is then read in to create an mcmc.list object and mcgibbsit is run specifying the desired accuracy of estimation for quantiles of interest. This will return the minimum number of iterations to achieve the specified error bound. The set of MCMC samplers is now run so that the total number of iterations exceeds this minimum, and mcgibbsit is again called. This should continue until the number of iterations already complete is less than the minimum number computed by mcgibbsit.

If the initial number of iterations in data is too small to perform the calculations, an error message is printed indicating the minimum pilot run length.

The parameters q, r, s, converge.eps, and correct.cor can be supplied as vectors. This will cause mcgibbsit to produce a list of results, with one element produced for each set of values. I.e., setting q=(0.025,0.975), r=(0.0125,0.005) will yield a list containing two mcgibbsit objects, one computed with parameters q=0.025, r=0.0125, and the other with q=0.975, r=0.005.

Value

An mcgibbsit object with components

call

parameters used to call 'mcgibbsit'

params

values of r, s, and q used

resmatrix

a matrix with 6 columns:

Nmin

The minimum required sample size for a chain with no correlation between consecutive samples. Positive autocorrelation will increase the required sample size above this minimum value.

M

The number of ‘burn in’ iterations to be discarded (total over all chains).

N

The number of iterations after burn in required to estimate the quantile q to within an accuracy of +/- r with probability p (total over all chains).

Total

Overall number of iterations required (M + N).

I

An estimate (the ‘dependence factor’) of the extent to which auto-correlation inflates the required sample size. Values of ‘I’ larger than 5 indicate strong autocorrelation which may be due to a poor choice of starting value, high posterior correlations, or ‘stickiness’ of the MCMC algorithm.

R

An estimate of the extent to which between-chain correlation inflates the required sample size. Large values of 'R' indicate that there is significant correlation between the chains and may be indicative of a lack of convergence or a poor multi-chain algorithm.

nchains

the number of MCMC chains in the data

len

the length of each chain

Author(s)

Gregory R. Warnes greg@warnes.net based on the the R function raftery.diag which is part of the 'CODA' library. raftery.diag, in turn, is based on the FORTRAN program ‘gibbsit’ written by Steven Lewis which is available from the Statlib archive.

References

Warnes, G.W. (2004). The Normal Kernel Coupler: An adaptive MCMC method for efficiently sampling from multi-modal distributions, https://digital.lib.washington.edu/researchworks/handle/1773/9541

Warnes, G.W. (2000). Multi-Chain and Parallel Algorithms for Markov Chain Monte Carlo. Dissertation, Department of Biostatistics, University of Washington, https://www.stat.washington.edu/research/reports/2001/tr395.pdf

Raftery, A.E. and Lewis, S.M. (1992). One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science, 7, 493-497.

Raftery, A.E. and Lewis, S.M. (1995). The number of iterations, convergence diagnostics and generic Metropolis algorithms. In Practical Markov Chain Monte Carlo (W.R. Gilks, D.J. Spiegelhalter and S. Richardson, eds.). London, U.K.: Chapman and Hall.

See Also

read.mcmc

Examples

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# this is a totally useless example, but it does exercise the code
for(i in 1:20){
  x <- matrix(rnorm(1000),ncol=4)
  x[,4] <- x[,4] + 1/3 * (x[,1] + x[,2] + x[,3])
  colnames(x) <- c("alpha","beta","gamma", "nu")
  write.csv(x, file=paste("mcmc",i,"csv",sep="."), row.names=FALSE)
}

data <- read.mcmc(20, "mcmc.#.csv", sep=",")

mcgibbsit(data)

mcgibbsit documentation built on May 2, 2019, 2:05 a.m.