Description Usage Arguments Details Value Author(s) References See Also Examples

This function performs Bayesian multimodel inference for a set of 'multimark' closed population abundance models using the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm proposed by Barker & Link (2013).

1 2 3 4 |

`modlist` |
A list of individual model output lists returned by |

`modprior` |
Vector of length |

`monparms` |
Parameters to monitor. Only parameters common to all models can be monitored (e.g., " |

`miter` |
The number of RJMCMC iterations per chain. If |

`mburnin` |
Number of burn-in iterations ( |

`mthin` |
Thinning interval for monitored parameters. |

`M1` |
Integer vector indicating the initial model for each chain, where |

`pbetapropsd` |
Scaler specifying the standard deviation of the Normal(0, pbetapropsd) proposal distribution for " |

`zppropsd` |
Scaler specifying the standard deviation of the Normal(0, zppropsd) proposal distribution for " |

`sigppropshape` |
Scaler specifying the shape parameter of the invGamma(shape = sigppropshape, scale = sigppropscale) proposal distribution for |

`sigppropscale` |
Scaler specifying the scale parameter of the invGamma(shape = sigppropshape, scale = sigppropscale) proposal distribution for |

`printlog` |
Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when |

Note that setting `parms="all"`

is required when fitting individual `multimarkClosed`

or `markClosed`

models to be included in `modlist`

.

A list containing the following:

`rjmcmc` |
Reversible jump Markov chain Monte Carlo object of class |

`pos.prob` |
A list of calculated posterior model probabilities for each chain, including the overall posterior model probabilities across all chains. |

Brett T. McClintock

Barker, R. J. and Link. W. A. 2013. Bayesian multimodel inference by RJMCMC: a Gibbs sampling approach. The American Statistician 67: 150-156.

`multimarkClosed`

, `markClosed`

, `processdata`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
# This example is excluded from testing to reduce package check time
# Example uses unrealistically low values for nchain, iter, and burnin
#Generate object of class "multimarksetup"
setup <- processdata(bobcat)
#Run single chain using the default model for bobcat data. Note parms="all".
bobcat.dot <- multimarkClosed(mms=setup,parms="all",iter=1000,adapt=500,burnin=500)
#Run single chain for bobcat data with time effects. Note parms="all".
bobcat.time <- multimarkClosed(mms=setup,mod.p=~time,parms="all",iter=1000,adapt=500,burnin=500)
#Perform RJMCMC using defaults
modlist <- list(mod1=bobcat.dot,mod2=bobcat.time)
bobcat.M <- multimodelClosed(modlist=modlist,monparms=c("N","p"))
#Posterior model probabilities
bobcat.M$pos.prob
#multimodel posterior summary for abundance
summary(bobcat.M$rjmcmc[,"N"])
``` |

multimark documentation built on May 1, 2019, 7:05 p.m.

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