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

This function fits Cormack-Jolly-Seber (CJS) open population models for survival probability (*φ*) and capture probability (*p*) 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 7 8 | ```
markCJS(Enc.Mat, covs = data.frame(), mod.p = ~1, mod.phi = ~1,
parms = c("pbeta", "phibeta"), 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, propphibeta = 0.1, propzphi = 1,
propsigmaphi = 1, pbeta0 = 0, pSigma0 = 1, phibeta0 = 0,
phiSigma0 = 1, l0p = 1, d0p = 0.01, l0phi = 1, d0phi = 0.01,
initial.values = NULL, link = "probit", 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 ( |

`mod.phi` |
Model formula for survival probability ( |

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

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

`iter` |
The number of MCMC iterations. |

`adapt` |
Ignored; no adaptive phase is needed for "probit" link. |

`bin` |
Ignored; no adaptive phase is needed for "probit" link. |

`thin` |
Thinning interval for monitored parameters. |

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

`taccept` |
Ignored; no adaptive phase is needed for "probit" link. |

`tuneadjust` |
Ignored; no adaptive phase is needed for "probit" link. |

`proppbeta` |
Ignored; no adaptive phase is needed for "probit" link. |

`propzp` |
Ignored; no adaptive phase is needed for "probit" link. |

`propsigmap` |
Ignored; no adaptive phase is needed for "probit" link. |

`propphibeta` |
Ignored; no adaptive phase is needed for "probit" link. |

`propzphi` |
Ignored; no adaptive phase is needed for "probit" link. |

`propsigmaphi` |
Ignored; no adaptive phase is needed for "probit" link. |

`pbeta0` |
Scaler or vector (of length k) specifying mean of pbeta ~ multivariateNormal(pbeta0, pSigma0) prior. If |

`pSigma0` |
Scaler or k x k matrix specifying covariance matrix of pbeta ~ multivariateNormal(pbeta0, pSigma0) prior. If |

`phibeta0` |
Scaler or vector (of length k) specifying mean of phibeta ~ multivariateNormal(phibeta0, phiSigma0) prior. If |

`phiSigma0` |
Scaler or k x k matrix specifying covariance matrix of phibeta ~ multivariateNormal(phibeta0, phiSigma0) prior. If |

`l0p` |
Specifies "shape" parameter for [sigma2_zp] ~ invGamma(l0p,d0p) prior. Default is |

`d0p` |
Specifies "scale" parameter for [sigma2_zp] ~ invGamma(l0p,d0p) prior. Default is |

`l0phi` |
Specifies "shape" parameter for [sigma2_zphi] ~ invGamma(l0phi,d0phi) prior. Default is |

`d0phi` |
Specifies "scale" parameter for [sigma2_zphi] ~ invGamma(l0phi,d0phi) prior. Default is |

`initial.values` |
OOptional list of |

`link` |
Link function for survival and capture probabilities. Only probit link is currently implemented. |

`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 `markCJS`

(or `markClosed`

) 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 `multimarkCJS`

. Note that setting `parms="all"`

is required for any `markCJS`

model output to be used in `multimodelCJS`

.

A list containing the following:

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

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

`mod.phi` |
Model formula for survival probability (as specified by |

`mod.delta` |
Formula always |

`DM` |
A list of design matrices for detection and survival probability respectively generated by |

`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 11 12 13 14 15 16 17 18 | ```
# These examples are excluded from testing to reduce package check time
# Example uses unrealistically low values for nchain, iter, and burnin
#Simulate open population data using defaults
data <- simdataCJS(delta_1=1,delta_2=0)$Enc.Mat
#Fit default open population model
sim.dot <- markCJS(data)
#Posterior summary for monitored parameters
summary(sim.dot$mcmc)
plot(sim.dot$mcmc)
#Fit ``age'' model with 2 age classes (e.g., juvenile and adult) for survival
#using 'parameters' and 'right' arguments from RMark::make.design.data
sim.age <- markCJS(data,mod.phi=~age,
parameters=list(Phi=list(age.bins=c(0,1,4))),right=FALSE)
summary(getprobsCJS(sim.age))
``` |

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

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