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

This function fits closed population abundance models for “traditional” capture-mark-recapture data consisting of a single mark type using Bayesian analysis methods. Markov chain Monte Carlo (MCMC) is used to draw samples from the joint posterior distribution.

1 2 3 4 5 6 | ```
markClosed(Enc.Mat, covs = data.frame(), mod.p = ~1,
parms = c("pbeta", "N"), nchains = 1, iter = 12000, adapt = 1000,
bin = 50, thin = 1, burnin = 2000, taccept = 0.44,
tuneadjust = 0.95, proppbeta = 0.1, propzp = 1, propsigmap = 1,
npoints = 500, a = 25, mu0 = 0, sigma2_mu0 = 1.75,
initial.values = NULL, printlog = FALSE, ...)
``` |

`Enc.Mat` |
A matrix of observed encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions. With a single mark type, encounter histories consist of only non-detections (0) and type 1 encounters (1). |

`covs` |
A data frame of temporal covariates for detection probabilities (ignored unless |

`mod.p` |
Model formula for detection probability. For example, |

`parms` |
A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are logit-scale detection probability parameters (" |

`nchains` |
The number of parallel MCMC chains for the model. |

`iter` |
The number of MCMC iterations. |

`adapt` |
The number of iterations for proposal distribution adaptation. If |

`bin` |
Bin length for calculating acceptance rates during adaptive phase ( |

`thin` |
Thinning interval for monitored parameters. |

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

`taccept` |
Target acceptance rate during adaptive phase ( |

`tuneadjust` |
Adjustment term during adaptive phase ( |

`proppbeta` |
Scaler or vector (of length k) specifying the initial standard deviation of the Normal(pbeta[j], proppbeta[j]) proposal distribution. If |

`propzp` |
Scaler or vector (of length M) specifying the initial standard deviation of the Normal(zp[i], propzp[i]) proposal distribution. If |

`propsigmap` |
Scaler specifying the initial Gamma(shape = 1/ |

`npoints` |
Number of Gauss-Hermite quadrature points to use for numerical integration. Accuracy increases with number of points, but so does computation time. |

`a` |
Scale parameter for [sigma_z] ~ half-Cauchy(a) prior for the individual hetegeneity term sigma_zp = sqrt(sigma2_zp). Default is “uninformative” |

`mu0` |
Scaler or vector (of length k) specifying mean of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If |

`sigma2_mu0` |
Scaler or vector (of length k) specifying variance of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If |

`initial.values` |
Optional list of |

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

`...` |
Additional " |

The first time `markClosed`

(or `markCJS`

) is called, it will likely produce a firewall warning alerting users that R has requested the ability to accept incoming network connections. Incoming network connections are required to use parallel processing as implemented in `markClosed`

. Note that setting `parms="all"`

is required for any `markClosed`

model output to be used in `multimodelClosed`

.

A list containing the following:

`mcmc` |
Markov chain Monte Carlo object of class |

`mod.p` |
Model formula for detection probability (as specified by |

`mod.delta` |
Formula always |

`DM` |
A list of design matrices for detection probability generated for model |

`initial.values` |
A list containing the parameter and latent variable values at iteration |

`mms` |
An object of class |

Brett T. McClintock

1 2 3 4 5 6 7 8 9 10 | ```
# This example is excluded from testing to reduce package check time
# Example uses unrealistically low values for nchain, iter, and burnin
#Run single chain using the default model for simulated ``traditional'' data
data<-simdataClosed(delta_1=1,delta_2=0)$Enc.Mat
sim.dot<-markClosed(data)
#Posterior summary for monitored parameters
summary(sim.dot$mcmc)
plot(sim.dot$mcmc)
``` |

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