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

This function fits spatial 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 7 8 | ```
markClosedSCR(Enc.Mat, trapCoords, studyArea = NULL, buffer = NULL,
ncells = 1024, covs = data.frame(), mod.p = ~1,
detection = "half-normal", 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,
propsigma = 1, propcenter = NULL, sigma_bounds = NULL, mu0 = 0,
sigma2_mu0 = 1.75, initial.values = NULL, scalemax = 10,
printlog = FALSE, ...)
``` |

`Enc.Mat` |
A matrix containing the observed encounter histories with rows corresponding to individuals and ( |

`trapCoords` |
A matrix of dimension |

`studyArea` |
is a 3-column matrix containing the coordinates for the centroids a contiguous grid of cells that define the study area and available habitat. Each row corresponds to a grid cell. The first 2 columns (“x” and “y”) indicate the Cartesian x- and y-coordinate for the centroid of each grid cell, and the third column (“avail”) indicates whether the cell is available habitat (=1) or not (=0). All cells must have the same resolution. If |

`buffer` |
A scaler in same units as |

`ncells` |
The number of grid cells in the study area when |

`covs` |
A data frame of time- and/or trap-dependent covariates for detection probabilities (ignored unless |

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

`detection` |
Model for detection probability as a function of distance from activity centers . Must be " |

`parms` |
A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are cloglog-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 |

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

`propcenter` |
Scaler specifying the neighborhood distance when proposing updates to activity centers. When |

`sigma_bounds` |
Positive vector of length 2 for the lower and upper bounds for the [sigma_scr] ~ Uniform(sigma_bounds[1], sigma_bounds[2]) (or [sqrt(lambda)] when |

`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 |

`scalemax` |
Upper bound for internal re-scaling of grid cell centroid coordinates. Default is |

`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 `markClosedSCR`

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 |

`mod.det` |
Model formula for detection function (as specified by |

`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

Gopalaswamy, A.M., Royle, J.A., Hines, J.E., Singh, P., Jathanna, D., Kumar, N. and Karanth, K.U. 2012. Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models. *Methods in Ecology and Evolution* 3:1067-1072.

King, R., McClintock, B. T., Kidney, D., and Borchers, D. L. 2016. Capture-recapture abundance estimation using a semi-complete data likelihood approach. *The Annals of Applied Statistics* 10: 264-285

Royle, J.A., Karanth, K.U., Gopalaswamy, A.M. and Kumar, N.S. 2009. Bayesian inference in camera trapping studies for a class of spatial capture-recapture models. *Ecology* 90: 3233-3244.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# 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 ``traditional'' tiger data of Royle et al (2009)
Enc.Mat<-tiger$Enc.Mat
trapCoords<-tiger$trapCoords
studyArea<-tiger$studyArea
tiger.dot<-markClosedSCR(Enc.Mat,trapCoords,studyArea,iter=100,adapt=50,burnin=50)
#Posterior summary for monitored parameters
summary(tiger.dot$mcmc)
plot(tiger.dot$mcmc)
``` |

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

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