| plotMCMC-package {plotMCMC} | R Documentation |
MCMC Diagnostic Plots
Description
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.
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 |
Note
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.
Author(s)
Arni Magnusson and Ian Stewart.
References
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.
See Also
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.