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 future projected
biomass in tonnes.

1 |

Data frame containing 1000 rows and 4 columns (years 2004 to 2007).

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.

The projections are based on a fixed harvest rate, where 25% of the biomass (ages 4 and older) is caught every year.

This data frame is a subset of the `xproj`

list
from the scape package, which contains further documentation
about the data and model. More specifically,
`xpro <- xproj$"0.25"`

.

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.

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