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

This function performs Bayesian multimodel inference for a set of 'multimark' open population survival (i.e., Cormack-Jolly-Seber) models using the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm proposed by Barker & Link (2013).

1 2 3 4 5 6 | ```
multimodelCJS(modlist, modprior = rep(1/length(modlist),
length(modlist)), monparms = "phi", miter = NULL, mburnin = 0,
mthin = 1, M1 = NULL, pbetapropsd = 1, zppropsd = NULL,
phibetapropsd = 1, zphipropsd = NULL, sigppropshape = 1,
sigppropscale = 0.01, sigphipropshape = 1, sigphipropscale = 0.01,
printlog = FALSE)
``` |

`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 " |

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

`zphipropsd` |
Scaler specifying the standard deviation of the Normal(0, zphipropsd) 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 " |

`sigphipropshape` |
Scaler specifying the shape parameter of the invGamma(shape = sigphipropshape, scale = sigphipropscale) proposal distribution for " |

`sigphipropscale` |
Scaler specifying the scale parameter of the invGamma(shape = sigphipropshape, scale = sigphipropscale) 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 `multimarkCJS`

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# 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" from simulated data
data_type = "always"
noccas <- 5
phibetaTime <- seq(2,0,length=noccas-1) # declining trend in survival
data <- simdataCJS(noccas=5,phibeta=phibetaTime,data.type=data_type)
setup <- processdata(data$Enc.Mat,data.type=data_type)
#Run single chain using the default model. Note parms="all".
sim.pdot.phidot <- multimarkCJS(mms=setup,parms="all",iter=1000,adapt=500,burnin=500)
#Run single chain with temporal trend for phi. Note parms="all".
sim.pdot.phiTime <- multimarkCJS(mms=setup,mod.phi=~Time,parms="all",iter=1000,adapt=500,burnin=500)
#Perform RJMCMC using defaults
modlist <- list(mod1=sim.pdot.phidot,mod2=sim.pdot.phiTime)
sim.M <- multimodelCJS(modlist=modlist)
#Posterior model probabilities
sim.M$pos.prob
#multimodel posterior summary for survival (display first cohort only)
summary(sim.M$rjmcmc[,paste0("phi[1,",1:(noccas-1),"]")])
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.