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 |
Posterior plots:
plotDens |
posterior(s) |
Examples:
xpar |
model parameters |
xrec |
recruitment |
xbio |
biomass |
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.