scapeMCMC-package {scapeMCMC} | R Documentation |
Markov-chain Monte Carlo diagnostic plots, accompanying the scape package. The purpose of the package is to combine existing tools from the coda and lattice packages, and make it easy to adjust graphical details. It can be useful for anyone using MCMC analysis, regardless of the application.
Import Coleraine MCMC results:
importMCMC | traces of likelihoods, parameters, biomass and recruitment |
importProj | future projections of biomass and catch |
plotTrace | trends |
plotAuto | thinning |
plotCumu | convergence |
plotSplom | confounding of parameters |
plotDens | posterior(s) |
plotQuant | multiple posteriors on a common y axis |
xmcmc , xproj | MCMC results and projections |
The vignette ‘scape/doc/dsc.pdf’ provides background information
and introduces the design of scape and scapeMCMC, but is
not updated. Additional details are found on the
help(package="scapeMCMC")
page.
The plot functions assume that MCMC results are stored either as a plain vector (single chain) or in named columns (multiple chains). It should be easy for users to arrange their MCMC results in this way. The examples demonstrate how several data frames can be bound together in nested lists.
The functions Args
and ll
(package gdata) can be
useful for browsing unwieldy functions and objects.
Arni Magnusson arnima@u.washington.edu and Ian Stewart.
The scape package provides diagnostic plot functions for statistical catch-at-age model fit to data, recommended before starting MCMC analysis. The coda package is a suite of diagnostic functions for MCMC analysis, many of which are used in scapeMCMC.