Description Usage Format Details Note References See Also Examples
Markov chain Monte Carlo results from stock assessment of cod (Gadus morhua) in Icelandic waters, showing estimated biomass by year in tonnes.
1 |
Data frame containing 1000 rows and 34 columns (years 1971 to 2004).
Each column contains the results of 1 million MCMC iterations, after thinning to every 1000th iteration.
The MCMC analysis started at the best fit, so no burn-in period was discarded.
Biomass is the total weight of all individuals in a population, in this case ages 4 and older.
This data frame is a subset of the xmcmc
list
from the scape package, which contains further documentation
about the data and model. More specifically, xbio <- xmcmc$B
.
The MCMC analysis was run using the AD Model Builder software (http://www.admb-project.org/).
Fournier, D. A., Skaug, H. J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M. N., Nielsen, A. and Sibert, J. (2012) AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optimization Methods and Software, 27, 233–249.
Magnusson, A., Punt, A. E. and Hilborn, R. (2013) Measuring uncertainty in fisheries stock assessment: the delta method, bootstrap, and MCMC. Fish and Fisheries, 14, 325–342.
xpar
(parameters), xrec
(recruitment),
xbio
(biomass), and xpro
(projected future
biomass) are MCMC data frames to explore.
plotMCMC-package
gives an overview of the package.
1 2 3 4 5 6 7 8 | plotDens(xbio$"2004", points=TRUE, div=1000, main="2004\n",
xlab="Biomass age 4+ (1000 t)", tick.number=6, strip=FALSE)
plotQuant(xbio, div=1000, xlab="Year", ylab="Biomass age 4+ (kt)")
plotQuant(xbio, style="bars", div=1000, sfrac=0, xlab="Year",
ylab="Biomass age 4+ (kt)")
plotQuant(xbio, style="lines", div=1000, xlab="Year",
ylab="Biomass age 4+ (kt)")
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