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://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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