xpro: MCMC Results for Future Projections

In arnima-github/plotMCMC: MCMC Diagnostic Plots

MCMC Results for Future Projections

Description

Markov chain Monte Carlo results from stock assessment of cod (Gadus morhua) in Icelandic waters, showing future projected biomass in tonnes.

Usage

xpro

Format

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

Details

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.

Note

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. Specifically, xpro <- xproj$"0.25".

The MCMC analysis was run using the AD Model Builder software (https://www.admb-project.org).

References

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10556788.2011.597854")}

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-2979.2012.00473.x")}

See Also

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.

Examples

plotQuant(xpro, axes=1:2, div=1000, xlab="Year",
          ylab="Biomass age 4+ (kt)")

plotSplom(xpro, axes=TRUE, between=1, div=1000, main="Future biomass",
          cex.labels=1.5, pch=".", cex=3)

arnima-github/plotMCMC documentation built on Dec. 4, 2023, 8:16 p.m.