getXXX: Get diagnostic statistics

In mcmcOutput: Functions to Store, Manipulate and Display Markov Chain Monte Carlo (MCMC) Output

Get diagnostic statistics

Description

These functions calculated diagnostic statistics for objects of class mcmcOutput. Optionally, only values that are worse than a predefined threshold will be returned, and values can be sorted so that the worst are at the start of the output vector.

Usage

getMCE(x, pc=TRUE, bad=5, sort=TRUE)
getNeff(x, bad=10000, sort=TRUE)
getRhat(x, bad=1.1, sort=TRUE)

Arguments

x	an object of any class with MCMC output that can be coerced to class mcmcOutput.
pc	if TRUE, the value of the MC error as a percentage of the posterior SD will be returned.
bad	threshold for "bad" values: only values above this (for getMCE or getRhat) or below this (for getNeff) will be returned. If bad = NA, all values will be returned, including NAs.
sort	if TRUE, the values will be sorted, with the worst at the top.

Details

getRhat returns the Brooks-Gelman-Rubin (BGR) convergence diagnostic (Brooks & Gelman 1998), a non-parametric 'interval' estimator of the 'potential scale reduction factor' for MCMC output. Similar to the function coda::gelman.diag, but faster when thousands of parameters are involved and will not cause R to crash.

getMCE returns the Monte Carlo standard error calculated using the batch method of Lunn et al (2013, p77); see also Roberts (1996).

getNeff returns the effective number of draws taking account of autocorrelation within each chain. It is a wrapper for coda::effectiveSize.

Value

A named vector with the values of the diagnostic. Values of NA will be excluded unless bad = NA. It may have length 0 if no values are bad.

Author(s)

Mike Meredith

References

Brooks, S.P. & Gelman, A. (1998) General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434-455.

Lunn, D., Jackson, C., Best, N., Thomas, A., & Spiegelhalter, D. (2013) The BUGS book: a practical introduction to Bayesian analysis, Chapman and Hall.

Roberts, G.O. (1996). Markov chain concepts related to sampling algorithms. In Markov Chain Monte Carlo in practice (eds W.R. Gilks, D.J. Spiegelhalter & S. Richardson). Chapman & Hall, London.

Examples

data(mcmcListExample)
mco <- mcmcOutput(mcmcListExample)

getMCE(mco, bad=2)
getMCE(mco, bad=0)  # returns all except NAs
getMCE(mco, bad=NA)  # returns all including NAs
getMCE(mco, bad=NA, sort=FALSE)  # returns all, in original order

getNeff(mco, bad=2800)
getNeff(mco, bad=Inf)  # returns all except NAs
getNeff(mco, bad=NA)   # returns all including NAs

getRhat(mco)
getRhat(mco, bad=0)
getRhat(mco, bad=NA, sort=FALSE)

# Extract the values with 'bad' MCE and do plots:
( badNodes <- names(getMCE(mco, bad=2)) )
( badMco <- mco[badNodes] )
plot(badMco)

mcmcOutput documentation built on Nov. 18, 2022, 1:08 a.m.