plotMCMC-package: MCMC Diagnostic Plots
In plotMCMC: MCMC Diagnostic Plots

Description Details Note Author(s) References See Also

Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the coda and lattice packages, and make it easy to adjust graphical details.

Diagnostic plots:

`plotTrace`	trends
`plotAuto`	thinning
`plotCumu`	convergence
`plotSplom`	confounding of parameters

Posterior plots:

`plotDens`	posterior(s)
`plotQuant`	multiple posteriors on a common y axis

Examples:

`xpar`	model parameters
`xrec`	recruitment
`xbio`	biomass
`xpro`	future projected biomass

browseVignettes() shows a vignette with all the example plots.

The plot functions assume that MCMC results are stored either as a plain numeric vector (single chain) or in a data.frame (multiple chains). The mcmc class is also supported.

Arni Magnusson and Ian Stewart.

Fournier, D. A., Skaug, H. J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M. N., Nielsen, A. and Sibert, J. (2012) AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software, 27, 233–249.

Magnusson, A., Punt, A. E. and Hilborn, R. (2013) Measuring uncertainty in fisheries stock assessment: the delta method, bootstrap, and MCMC. Fish and Fisheries, 14, 325–342.

The coda package is a suite of diagnostic functions and plots for MCMC analysis, many of which are used in plotMCMC.

Many plotMCMC graphics are trellis plots, rendered with the lattice package.

The functions Args and ll (package gdata) can be useful for browsing unwieldy functions and objects.

plotMCMC documentation built on Nov. 23, 2020, 5:08 p.m.