List of output returned by multimarkClosedSCR.

Posterior samples for density.

List of output returned by multimarkCJS

Link function for p and \phi. Must be "probit" or "logit". Note that multimarkCJS is currently implemented for the probit link only.

Posterior samples for capture probability (p[c,t]) for each release cohort (c=1,\ldots,T-1) and sampling occasion (t=2,\ldots,T).

Posterior samples for survival probability (\phi[c,k]) for each release cohort (c=1,\ldots,T-1) and interval (k=1,\ldots,T-1).

List of output returned by multimarkClosed.

Link function for detection probability. Must be "logit" or "probit". Note that multimarkClosed is currently implemented for the logit link only.

Posterior samples for capture probability (p) for each sampling occasion.

Posterior samples for recapture probability (c) for each sampling occasion.

List of output returned by multimarkClosedSCR.

Link function for detection probability. Must be "cloglog". Note that multimarkClosedSCR is currently implemented for the cloglog link only.

Posterior samples for capture probability (p) for each sampling occasion (first index) and trap (second index).

Posterior samples for recapture probability (c) for each sampling occasion (first index) and trap (second index).

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).

A data frame of temporal covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of sampling occasions. Covariate names cannot be "time", "age", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

Model formula for detection probability (p). For example, mod.p=~1 specifies no effects (i.e., intercept only), mod.p~time specifies temporal effects, mod.p~age specifies age effects, mod.p~h specifies individual heterogeneity, and mod.p~time+age specifies additive temporal and age effects.

Model formula for survival probability (\phi). For example, mod.phi=~1 specifies no effects (i.e., intercept only), mod.phi~time specifies temporal effects, mod.phi~age specifies age effects, mod.phi~h specifies individual heterogeneity, and mod.phi~time+age specifies additive temporal and age effects.

A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are probit-scale detection probability parameters ("pbeta" for p and "phibeta" for \phi), probit-scale individual heterogeneity variance terms ("sigma2_zp" for p and "sigma2_zphi" for \phi), and probit-scale individual effects ("zp" and "zphi"). Latent variable indicators for whether each individual was alive (1) or dead (0) during each sampling occasion ("q") and the log likelihood ("loglike") may also be monitored. Setting parms="all" monitors all possible parameters and latent variables.

The number of parallel MCMC chains for the model.

The number of MCMC iterations.

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

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

Thinning interval for monitored parameters.

Number of burn-in iterations (0 <= burnin < iter).

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

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

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

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

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

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

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

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

Scaler or vector (of length k) specifying mean of pbeta ~ multivariateNormal(pbeta0, pSigma0) prior. If pbeta0 is a scaler, then this value is used for all j = 1, ..., k. Default is pbeta0 = 0.

Scaler or k x k matrix specifying covariance matrix of pbeta ~ multivariateNormal(pbeta0, pSigma0) prior. If pSigma0 is a scaler, then this value is used for all pSigma0[j,j] for j = 1, ..., k (with pSigma[j,l] = 0 for all j \ne l). Default is pSigma0 = 1.

Scaler or vector (of length k) specifying mean of phibeta ~ multivariateNormal(phibeta0, phiSigma0) prior. If phibeta0 is a scaler, then this value is used for all j = 1, ..., k. Default is phibeta0 = 0.

Scaler or k x k matrix specifying covariance matrix of phibeta ~ multivariateNormal(phibeta0, phiSigma0) prior. If phiSigma0 is a scaler, then this value is used for all phiSigma0[j,j] for j = 1, ..., k (with phiSigma[j,l] = 0 for all j \ne l). Default is phiSigma0 = 1.

Specifies "shape" parameter for [sigma2_zp] ~ invGamma(l0p,d0p) prior. Default is l0p = 1.

Specifies "scale" parameter for [sigma2_zp] ~ invGamma(l0p,d0p) prior. Default is d0p = 0.01.

Specifies "shape" parameter for [sigma2_zphi] ~ invGamma(l0phi,d0phi) prior. Default is l0phi = 1.

Specifies "scale" parameter for [sigma2_zphi] ~ invGamma(l0phi,d0phi) prior. Default is d0phi = 0.01.

OOptional list of nchain list(s) specifying initial values for "pbeta", "phibeta", "sigma2_zp", "sigma2_zphi", "zp", "zphi", and "q". Default is initial.values = NULL, which causes initial values to be generated automatically.

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

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Additional "parameters" arguments for specifying mod.p and mod.phi. See RMark::make.design.data.

Markov chain Monte Carlo object of class mcmc.list.

Model formula for detection probability (as specified by mod.p above).

Model formula for survival probability (as specified by mod.phi above).

Formula always NULL; only for internal use in multimodelCJS.

A list of design matrices for detection and survival probability respectively generated by mod.p and mod.phi, where DM$p is the design matrix for capture probability (p) and DM$phi is the design matrix for survival probability (\phi).

A list containing the parameter and latent variable values at iteration iter for each chain. Values are provided for "pbeta", "phibeta", "sigma2_zp", "sigma2_zphi", "zp", "zphi", and "q".

An object of class multimarksetup

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).

A data frame of temporal covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of sampling occasions. Covariate names cannot be "time", "age", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

Model formula for detection probability. For example, mod.p=~1 specifies no effects (i.e., intercept only), mod.p~time specifies temporal effects, mod.p~c specifies behavioral reponse (i.e., trap "happy" or "shy"), mod.p~h specifies individual heterogeneity, and mod.p~time+c specifies additive temporal and behavioral effects.

A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are logit-scale detection probability parameters ("pbeta"), population abundance ("N"), logit-scale individual heterogeneity variance term ("sigma2_zp"), and logit-scale individual effects ("zp"). The log posterior density ("logPosterior") may also be monitored. Setting parms="all" monitors all possible parameters and latent variables.

The number of parallel MCMC chains for the model.

The number of MCMC iterations.

The number of iterations for proposal distribution adaptation. If adapt = 0 then no adaptation occurs.

Bin length for calculating acceptance rates during adaptive phase (0 < bin <= iter).

Thinning interval for monitored parameters.

Number of burn-in iterations (0 <= burnin < iter).

Target acceptance rate during adaptive phase (0 < taccept <= 1). Acceptance rate is monitored every bin iterations. Default is taccept = 0.44.

Adjustment term during adaptive phase (0 < tuneadjust <= 1). If acceptance rate is less than taccept, then proposal term (proppbeta, propzp, or propsigmap) is multiplied by tuneadjust. If acceptance rate is greater than or equal to taccept, then proposal term is divided by tuneadjust. Default is tuneadjust = 0.95.

Scaler or vector (of length k) specifying the initial standard deviation of the Normal(pbeta[j], proppbeta[j]) proposal distribution. If proppbeta is a scaler, then this value is used for all j = 1, ..., k. Default is proppbeta = 0.1.

Scaler or vector (of length M) specifying the initial standard deviation of the Normal(zp[i], propzp[i]) proposal distribution. If propzp is a scaler, then this value is used for all i = 1, ..., M individuals. Default is propzp = 1.

Scaler specifying the initial Gamma(shape = 1/propsigmap, scale = sigma_zp * propsigmap) proposal distribution for sigma_zp = sqrt(sigma2_zp). Default is propsigmap=1.

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

Scale parameter for [sigma_z] ~ half-Cauchy(a) prior for the individual hetegeneity term sigma_zp = sqrt(sigma2_zp). Default is “uninformative” a = 25.

Scaler or vector (of length k) specifying mean of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is mu0 = 0.

Scaler or vector (of length k) specifying variance of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If sigma2_mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is sigma2_mu0 = 1.75.

Optional list of nchain list(s) specifying initial values for "pbeta", "zp", "sigma2_zp", and "N". Default is initial.values = NULL, which causes initial values to be generated automatically.

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Additional "parameters" arguments for specifying mod.p. See make.design.data.

Markov chain Monte Carlo object of class mcmc.list.

Model formula for detection probability (as specified by mod.p above).

Formula always NULL; only for internal use in multimodelClosed.

A list of design matrices for detection probability generated for model mod.p, where DM$p is the design matrix for initial capture probability (p) and DM$c is the design matrix for recapture probability (c).

A list containing the parameter and latent variable values at iteration iter for each chain. Values are provided for "pbeta", "zp", "sigma2_zp", and "N".

An object of class multimarksetup

A matrix containing the observed encounter histories with rows corresponding to individuals and (ntraps*noccas) columns corresponding to traps and sampling occasions. The first noccas columns correspond to trap 1, the second noccas columns corresopond to trap 2, etc.

A matrix of dimension ntraps x (2 + noccas) indicating the Cartesian coordinates and operating occasions for the traps, where rows correspond to trap, the first column the x-coordinate (“x”), and the second column the y-coordinate (“y”). The last noccas columns indicate whether or not the trap was operating on each of the occasions, where ‘1’ indciates the trap was operating and ‘0’ indicates the trap was not operating. Ignored unless mms=NULL.

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 studyArea=NULL (the default) and mms=NULL, then a square study area grid composed of ncells cells of available habitat is drawn around the bounding box of trapCoords based on buffer. Ignored unless mms=NULL. Note that rows should be ordered by raster cell order (raster cell numbers start at 1 in the upper left corner, and increase from left to right, and then from top to bottom).

A scaler in same units as trapCoords indicating the buffer around the bounding box of trapCoords for defining the study area when studyArea=NULL. Ignored unless studyArea=NULL.

The number of grid cells in the study area when studyArea=NULL. The square root of ncells must be a whole number. Default is ncells=1024. Ignored unless studyArea=NULL and mms=NULL.

A data frame of time- and/or trap-dependent covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of traps times the number of sampling occasions (ntraps*noccas), where the first noccas rows correspond to trap 1, the noccas rows correspond to trap 2, etc. Covariate names cannot be "time", "age", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

Model formula for detection probability. For example, mod.p=~1 specifies no effects (i.e., intercept only), mod.p~time specifies temporal effects, mod.p~c specifies behavioral reponse (i.e., trap "happy" or "shy"), mod.p~trap specifies trap effects, and mod.p~time+c specifies additive temporal and behavioral effects.

Model for detection probability as a function of distance from activity centers . Must be "half-normal" (of the form \exp{(-d^2 / (2*\sigma^2))}, where d is distance) or "exponential" (of the form \exp{(-d / \lambda)}).

A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are cloglog-scale detection probability parameters ("pbeta"), population abundance ("N"), and cloglog-scale distance term for the detection function ("sigma2_scr" when detection=``half-normal'' or "lambda" when detection=``exponential''). Individual activity centers ("centers") and the log posterior density ("logPosterior") may also be monitored. Setting parms="all" monitors all possible parameters and latent variables.

The number of parallel MCMC chains for the model.

The number of MCMC iterations.

The number of iterations for proposal distribution adaptation. If adapt = 0 then no adaptation occurs.

Bin length for calculating acceptance rates during adaptive phase (0 < bin <= iter).

Thinning interval for monitored parameters.

Number of burn-in iterations (0 <= burnin < iter).

Target acceptance rate during adaptive phase (0 < taccept <= 1). Acceptance rate is monitored every bin iterations. Default is taccept = 0.44.

Adjustment term during adaptive phase (0 < tuneadjust <= 1). If acceptance rate is less than taccept, then proposal term (proppbeta or propsigma) is multiplied by tuneadjust. If acceptance rate is greater than or equal to taccept, then proposal term is divided by tuneadjust. Default is tuneadjust = 0.95.

Scaler or vector (of length k) specifying the initial standard deviation of the Normal(pbeta[j], proppbeta[j]) proposal distribution. If proppbeta is a scaler, then this value is used for all j = 1, ..., k. Default is proppbeta = 0.1.

Scaler specifying the initial Gamma(shape = 1/propsigma, scale = sigma_scr * propsigma) proposal distribution for sigma_scr = sqrt(sigma2_scr). Default is propsigma=1.

Scaler specifying the neighborhood distance when proposing updates to activity centers. When propcenter=NULL (the default), then propcenter = a*10, where a is the cell size for the study area grid, and each cell has (at most) approximately 300 neighbors.

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 detection=``exponential'') prior for the detection function term sigma_scr = sqrt(sigma2_scr) (or sqrt(lambda)). When sigma_bounds = NULL (the default), then sigma_bounds = c(1.e-6,max(diff(range(studyArea[,"x"])),diff(range(studyArea[,"y"])))).

Scaler or vector (of length k) specifying mean of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is mu0 = 0.

Scaler or vector (of length k) specifying variance of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If sigma2_mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is sigma2_mu0 = 1.75.

Optional list of nchain list(s) specifying initial values for "pbeta", "N", "sigma2_scr", and "centers". Default is initial.values = NULL, which causes initial values to be generated automatically.

Upper bound for internal re-scaling of grid cell centroid coordinates. Default is scalemax=10, which re-scales the centroids to be between 0 and 10. Re-scaling is done internally to avoid numerical overflows during model fitting.

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Additional "parameters" arguments for specifying mod.p. See make.design.data.

Markov chain Monte Carlo object of class mcmc.list.

Model formula for detection probability (as specified by mod.p above).

Formula always NULL; only for internal use in multimodelClosedSCR.

Model formula for detection function (as specified by detection above).

A list of design matrices for detection probability generated for model mod.p, where DM$p is the design matrix for initial capture probability (p) and DM$c is the design matrix for recapture probability (c).

A list containing the parameter and latent variable values at iteration iter for each chain. Values are provided for "pbeta", "N", "sigma2_scr", and "centers".

An object of class multimarkSCRsetup

A matrix of observed encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions (ignored unless mms=NULL).

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

A data frame of temporal covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of sampling occasions. Covariate names cannot be "time", "age", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

An optional object of class multimarksetup-class; if NULL it is created. See processdata.

Model formula for detection probability (p). For example, mod.p=~1 specifies no effects (i.e., intercept only), mod.p~time specifies temporal effects, mod.p~age specifies age effects, mod.p~h specifies individual heterogeneity, and mod.p~time+age specifies additive temporal and age effects.

Model formula for survival probability (\phi). For example, mod.phi=~1 specifies no effects (i.e., intercept only), mod.phi~time specifies temporal effects, mod.phi~age specifies age effects, mod.phi~h specifies individual heterogeneity, and mod.phi~time+age specifies additive temporal and age effects.

Model formula for conditional probabilities of type 1 (delta_1) and type 2 (delta_2) encounters, given detection. Currently only mod.delta=~1 (i.e., \delta_1 = \delta_2) and mod.delta=~type (i.e., \delta_1 \ne \delta_2) are implemented.

A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are probit-scale detection probability parameters ("pbeta" for p and "phibeta" for \phi), conditional probability of type 1 or type 2 encounter, given detection ("delta)", probability of simultaneous type 1 and type 2 detection, given both types encountered ("alpha"), probit-scale individual heterogeneity variance terms ("sigma2_zp" for p and "sigma2_zphi" for \phi), probit-scale individual effects ("zp" and "zphi"), and the probability that a randomly selected individual from the M = nrow(Enc.Mat) observed individuals belongs to the n unique individuals encountered at least once ("psi"). Individual encounter history indices ("H"), latent variable indicators for whether each individual was alive (1) or dead (0) during each sampling occasion ("q"), and the log likelihood ("loglike") may also be monitored. Setting parms="all" monitors all possible parameters and latent variables.

The number of parallel MCMC chains for the model.

The number of MCMC iterations.

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

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

Thinning interval for monitored parameters.

Number of burn-in iterations (0 <= burnin < iter).

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

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

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

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

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

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

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

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

Maximum number of basis vectors to use when proposing latent history frequency updates. Default is maxnumbasis = 1, but higher values can potentially improve mixing.

Scaler or vector (of length k) specifying mean of pbeta ~ multivariateNormal(pbeta0, pSigma0) prior. If pbeta0 is a scaler, then this value is used for all j = 1, ..., k. Default is pbeta0 = 0.

Scaler or k x k matrix specifying covariance matrix of pbeta ~ multivariateNormal(pbeta0, pSigma0) prior. If pSigma0 is a scaler, then this value is used for all pSigma0[j,j] for j = 1, ..., k (with pSigma[j,l] = 0 for all j \ne l). Default is pSigma0 = 1.

Scaler or vector (of length k) specifying mean of phibeta ~ multivariateNormal(phibeta0, phiSigma0) prior. If phibeta0 is a scaler, then this value is used for all j = 1, ..., k. Default is phibeta0 = 0.

Scaler or k x k matrix specifying covariance matrix of phibeta ~ multivariateNormal(phibeta0, phiSigma0) prior. If phiSigma0 is a scaler, then this value is used for all phiSigma0[j,j] for j = 1, ..., k (with phiSigma[j,l] = 0 for all j \ne l). Default is phiSigma0 = 1.

Specifies "shape" parameter for [sigma2_zp] ~ invGamma(l0p,d0p) prior. Default is l0p = 1.

Specifies "scale" parameter for [sigma2_zp] ~ invGamma(l0p,d0p) prior. Default is d0p = 0.01.

Specifies "shape" parameter for [sigma2_zphi] ~ invGamma(l0phi,d0phi) prior. Default is l0phi = 1.

Specifies "scale" parameter for [sigma2_zphi] ~ invGamma(l0phi,d0phi) prior. Default is d0phi = 0.01.

Scaler or vector (of length d) specifying the prior for the conditional (on detection) probability of type 1 (delta_1), type 2 (delta_2), and both type 1 and type 2 encounters (1-delta_1-delta_2). If a0delta is a scaler, then this value is used for all a0delta[j] for j = 1, ..., d. For mod.delta=~type, d=3 with [delta_1, delta_2, 1-delta_1-delta_2] ~ Dirichlet(a0delta) prior. For mod.delta=~1, d=2 with [tau] ~ Beta(a0delta[1],a0delta[2]) prior, where (delta_1,delta_2,1-delta_1-delta_2) = (tau/2,tau/2,1-tau). See McClintock et al. (2013) for more details.

Specifies "shape1" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when data.type = "sometimes". Default is a0alpha = 1. Note that when a0alpha = 1 and b0alpha = 1, then [alpha] ~ Unif(0,1).

Specifies "shape2" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when data.type = "sometimes". Default is b0alpha = 1. Note that when a0alpha = 1 and b0alpha = 1, then [alpha] ~ Unif(0,1).

Specifies "shape1" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is a0psi = 1.

Specifies "shape2" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is b0psi = 1.

Optional list of nchain list(s) specifying initial values for parameters and latent variables. Default is initial.values = NULL, which causes initial values to be generated automatically. In addition to the parameters ("pbeta", "phibeta", "delta_1", "delta_2", "alpha", "sigma2_zp", "sigma2_zphi", "zp", "zphi", and "psi"), initial values can be specified for the initial latent history frequencies ("x"), initial individual encounter history indices ("H"), and initial latent variable indicators for whether each individual was alive (1) or dead (0) during each sampling occasion ("q").

Optional integer vector indicating whether the encounter history of an individual is known with certainty (i.e., the observed encounter history is the true encounter history). Encounter histories with at least one type 4 encounter are automatically assumed to be known, and known does not need to be specified unless there exist encounter histories that do not contain a type 4 encounter that happen to be known with certainty (e.g., from independent telemetry studies). If specified, known = c(v_1,v_2,...,v_M) must be a vector of length M = nrow(Enc.Mat) where v_i = 1 if the encounter history for individual i is known (v_i = 0 otherwise). Note that known all-zero encounter histories (e.g., ‘000’) are ignored.

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

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Additional "parameters" arguments for specifying mod.p and mod.phi. See RMark::make.design.data.

Markov chain Monte Carlo object of class mcmc.list.

Model formula for detection probability (as specified by mod.p above).

Model formula for survival probability (as specified by mod.phi above).

Formula always NULL; only for internal use in multimodelCJS.

A list of design matrices for detection and survival probability respectively generated by mod.p and mod.phi, where DM$p is the design matrix for capture probability (p) and DM$phi is the design matrix for survival probability (\phi).

A list containing the parameter and latent variable values at iteration iter for each chain. Values are provided for "pbeta", "phibeta", "delta_1", "delta_2", "alpha", "sigma2_zp" "sigma2_zphi", "zp", "zphi", "psi", "x", "H", and "q".

An object of class multimarksetup

A matrix of observed encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions (ignored unless mms=NULL).

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

A data frame of temporal covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of sampling occasions. Covariate names cannot be "time", "c", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

An optional object of class multimarksetup-class; if NULL it is created. See processdata.

Model formula for detection probability. For example, mod.p=~1 specifies no effects (i.e., intercept only), mod.p~time specifies temporal effects, mod.p~c specifies behavioral reponse (i.e., trap "happy" or "shy"), mod.p~h specifies individual heterogeneity, and mod.p~time+c specifies additive temporal and behavioral effects.

Model formula for conditional probabilities of type 1 (delta_1) and type 2 (delta_2) encounters, given detection. Currently only mod.delta=~1 (i.e., \delta_1 = \delta_2) and mod.delta=~type (i.e., \delta_1 \ne \delta_2) are implemented.

A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are logit-scale detection probability parameters ("pbeta"), population abundance ("N"), conditional probability of type 1 or type 2 encounter, given detection ("delta)", probability of simultaneous type 1 and type 2 detection, given both types encountered ("alpha"), logit-scale individual heterogeneity variance term ("sigma2_zp"), logit-scale individual effects ("zp"), and the probability that a randomly selected individual from the M = nrow(Enc.Mat) observed individuals belongs to the n unique individuals encountered at least once ("psi"). Individual encounter history indices ("H") and the log posterior density ("logPosterior") may also be monitored. Setting parms="all" monitors all possible parameters and latent variables.

The number of parallel MCMC chains for the model.

The number of MCMC iterations.

The number of iterations for proposal distribution adaptation. If adapt = 0 then no adaptation occurs.

Bin length for calculating acceptance rates during adaptive phase (0 < bin <= iter).

Thinning interval for monitored parameters.

Number of burn-in iterations (0 <= burnin < iter).

Target acceptance rate during adaptive phase (0 < taccept <= 1). Acceptance rate is monitored every bin iterations. Default is taccept = 0.44.

Adjustment term during adaptive phase (0 < tuneadjust <= 1). If acceptance rate is less than taccept, then proposal term (proppbeta, propzp, or propsigmap) is multiplied by tuneadjust. If acceptance rate is greater than or equal to taccept, then proposal term is divided by tuneadjust. Default is tuneadjust = 0.95.

Scaler or vector (of length k) specifying the initial standard deviation of the Normal(pbeta[j], proppbeta[j]) proposal distribution. If proppbeta is a scaler, then this value is used for all j = 1, ..., k. Default is proppbeta = 0.1.

Scaler or vector (of length M) specifying the initial standard deviation of the Normal(zp[i], propzp[i]) proposal distribution. If propzp is a scaler, then this value is used for all i = 1, ..., M individuals. Default is propzp = 1.

Scaler specifying the initial Gamma(shape = 1/propsigmap, scale = sigma_zp * propsigmap) proposal distribution for sigma_zp = sqrt(sigma2_zp). Default is propsigmap=1.

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

Maximum number of basis vectors to use when proposing latent history frequency updates. Default is maxnumbasis = 1, but higher values can potentially improve mixing.

Scaler or vector (of length d) specifying the prior for the conditional (on detection) probability of type 1 (delta_1), type 2 (delta_2), and both type 1 and type 2 encounters (1-delta_1-delta_2). If a0delta is a scaler, then this value is used for all a0delta[j] for j = 1, ..., d. For mod.delta=~type, d=3 with [delta_1, delta_2, 1-delta_1-delta_2] ~ Dirichlet(a0delta) prior. For mod.delta=~1, d=2 with [tau] ~ Beta(a0delta[1],a0delta[2]) prior, where (delta_1,delta_2,1-delta_1-delta_2) = (tau/2,tau/2,1-tau). See McClintock et al. (2013) for more details.

Specifies "shape1" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when data.type = "sometimes". Default is a0alpha = 1. Note that when a0alpha = 1 and b0alpha = 1, then [alpha] ~ Unif(0,1).

Specifies "shape2" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when data.type = "sometimes". Default is b0alpha = 1. Note that when a0alpha = 1 and b0alpha = 1, then [alpha] ~ Unif(0,1).

Scale parameter for [sigma_z] ~ half-Cauchy(a) prior for the individual hetegeneity term sigma_zp = sqrt(sigma2_zp). Default is “uninformative” a = 25.

Scaler or vector (of length k) specifying mean of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is mu0 = 0.

Scaler or vector (of length k) specifying variance of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If sigma2_mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is sigma2_mu0 = 1.75.

Specifies "shape1" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is a0psi = 1.

Specifies "shape2" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is b0psi = 1.

Optional list of nchain list(s) specifying initial values for parameters and latent variables. Default is initial.values = NULL, which causes initial values to be generated automatically. In addition to the parameters ("pbeta", "N", "delta_1", "delta_2", "alpha", "sigma2_zp", "zp", and "psi"), initial values can be specified for the initial latent history frequencies ("x") and initial individual encounter history indices ("H").

Optional integer vector indicating whether the encounter history of an individual is known with certainty (i.e., the observed encounter history is the true encounter history). Encounter histories with at least one type 4 encounter are automatically assumed to be known, and known does not need to be specified unless there exist encounter histories that do not contain a type 4 encounter that happen to be known with certainty (e.g., from independent telemetry studies). If specified, known = c(v_1,v_2,...,v_M) must be a vector of length M = nrow(Enc.Mat) where v_i = 1 if the encounter history for individual i is known (v_i = 0 otherwise). Note that known all-zero encounter histories (e.g., ‘000’) are ignored.

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Additional "parameters" arguments for specifying mod.p. See make.design.data.

Markov chain Monte Carlo object of class mcmc.list.

Model formula for detection probability (as specified by mod.p above).

Model formula for conditional probability of type 1 or type 2 encounter, given detection (as specified by mod.delta above).

A list of design matrices for detection probability generated for model mod.p, where DM$p is the design matrix for initial capture probability (p) and DM$c is the design matrix for recapture probability (c).

A list containing the parameter and latent variable values at iteration iter for each chain. Values are provided for "pbeta", "N", "delta_1", "delta_2", "alpha", "sigma2_zp", "zp", "psi", "x", and "H".

An object of class multimarksetup

A matrix containing the observed encounter histories with rows corresponding to individuals and (ntraps*noccas) columns corresponding to traps and sampling occasions. The first noccas columns correspond to trap 1, the second noccas columns corresopond to trap 2, etc. Ignored unless mms=NULL.

A matrix of dimension ntraps x (2 + noccas) indicating the Cartesian coordinates and operating occasions for the traps, where rows correspond to trap, the first column the x-coordinate (“x”), and the second column the y-coordinate (“y”). The last noccas columns indicate whether or not the trap was operating on each of the occasions, where ‘1’ indciates the trap was operating and ‘0’ indicates the trap was not operating. Ignored unless mms=NULL.

is a 3-column matrix containing the coordinates for the centroids of 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 be square and have the same resolution. If studyArea=NULL (the default) and mms=NULL, then a square study area grid composed of ncells cells of available habitat is drawn around the bounding box of trapCoords based on buffer. Ignored unless mms=NULL. Note that rows should be ordered in raster cell order (raster cell numbers start at 1 in the upper left corner, and increase from left to right, and then from top to bottom).

A scaler in same units as trapCoords indicating the buffer around the bounding box of trapCoords for defining the study area when studyArea=NULL. Ignored unless studyArea=NULL and mms=NULL.

The number of grid cells in the study area when studyArea=NULL. The square root of ncells must be a whole number. Default is ncells=1024. Ignored unless studyArea=NULL and mms=NULL.

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

A data frame of time- and/or trap-dependent covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of traps times the number of sampling occasions (ntraps*noccas), where the first noccas rows correspond to trap 1, the second noccas rows correspond to trap 2, etc. Covariate names cannot be "time", "age", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

An optional object of class multimarkSCRsetup-class; if NULL it is created. See processdataSCR.

Model formula for detection probability as a function of distance from activity centers. For example, mod.p=~1 specifies no effects (i.e., intercept only) other than distance, mod.p~time specifies temporal effects, mod.p~c specifies behavioral reponse (i.e., trap "happy" or "shy"), mod.p~trap specifies trap effects, and mod.p~time+c specifies additive temporal and behavioral effects.

Model formula for conditional probabilities of type 1 (delta_1) and type 2 (delta_2) encounters, given detection. Currently only mod.delta=~1 (i.e., \delta_1 = \delta_2) and mod.delta=~type (i.e., \delta_1 \ne \delta_2) are implemented.

Model for detection probability as a function of distance from activity centers . Must be "half-normal" (of the form \exp{(-d^2 / (2*\sigma^2))}, where d is distance) or "exponential" (of the form \exp{(-d / \lambda)}).

A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are cloglog-scale detection probability parameters ("pbeta"), population abundance ("N"), conditional probability of type 1 or type 2 encounter, given detection ("delta)", probability of simultaneous type 1 and type 2 detection, given both types encountered ("alpha"), cloglog-scale distance term for the detection function ("sigma2_scr" when detection=``half-normal'' or "lambda" when detection=``exponential''), and the probability that a randomly selected individual from the M = nrow(Enc.Mat) observed individuals belongs to the n unique individuals encountered at least once ("psi"). Individual activity centers ("centers"), encounter history indices ("H"), and the log posterior density ("logPosterior") may also be monitored. Setting parms="all" monitors all possible parameters and latent variables.

The number of parallel MCMC chains for the model.

The number of MCMC iterations.

The number of iterations for proposal distribution adaptation. If adapt = 0 then no adaptation occurs.

Bin length for calculating acceptance rates during adaptive phase (0 < bin <= iter).

Thinning interval for monitored parameters.

Number of burn-in iterations (0 <= burnin < iter).

Target acceptance rate during adaptive phase (0 < taccept <= 1). Acceptance rate is monitored every bin iterations. Default is taccept = 0.44.

Adjustment term during adaptive phase (0 < tuneadjust <= 1). If acceptance rate is less than taccept, then proposal term (proppbeta or propsigma) is multiplied by tuneadjust. If acceptance rate is greater than or equal to taccept, then proposal term is divided by tuneadjust. Default is tuneadjust = 0.95.

Scaler or vector (of length k) specifying the initial standard deviation of the Normal(pbeta[j], proppbeta[j]) proposal distribution. If proppbeta is a scaler, then this value is used for all j = 1, ..., k. Default is proppbeta = 0.1.

Scaler specifying the initial Gamma(shape = 1/propsigma, scale = sigma_scr * propsigma) proposal distribution for sigma_scr = sqrt(sigma2_scr) (or sqrt(lambda) = lambda if detection=``exponential''). Default is propsigma=1.

Scaler specifying the neighborhood distance when proposing updates to activity centers. When propcenter=NULL (the default), then propcenter = a*10, where a is the cell size for the study area grid, and each cell has (at most) approximately 300 neighbors.

Maximum number of basis vectors to use when proposing latent history frequency updates. Default is maxnumbasis = 1, but higher values can potentially improve mixing.

Scaler or vector (of length d) specifying the prior for the conditional (on detection) probability of type 1 (delta_1), type 2 (delta_2), and both type 1 and type 2 encounters (1-delta_1-delta_2). If a0delta is a scaler, then this value is used for all a0delta[j] for j = 1, ..., d. For mod.delta=~type, d=3 with [delta_1, delta_2, 1-delta_1-delta_2] ~ Dirichlet(a0delta) prior. For mod.delta=~1, d=2 with [tau] ~ Beta(a0delta[1],a0delta[2]) prior, where (delta_1,delta_2,1-delta_1-delta_2) = (tau/2,tau/2,1-tau). See McClintock et al. (2013) for more details.

Specifies "shape1" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when data.type = "sometimes". Default is a0alpha = 1. Note that when a0alpha = 1 and b0alpha = 1, then [alpha] ~ Unif(0,1).

Specifies "shape2" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when data.type = "sometimes". Default is b0alpha = 1. Note that when a0alpha = 1 and b0alpha = 1, then [alpha] ~ Unif(0,1).

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 detection=``exponential'') prior for the detection function term sigma_scr = sqrt(sigma2_scr) (or sqrt(lambda)). When sigma_bounds = NULL (the default), then sigma_bounds = c(1.e-6,max(diff(range(studyArea[,"x"])),diff(range(studyArea[,"y"])))).

Scaler or vector (of length k) specifying mean of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is mu0 = 0.

Scaler or vector (of length k) specifying variance of pbeta[j] ~ Normal(mu0[j], sigma2_mu0[j]) prior. If sigma2_mu0 is a scaler, then this value is used for all j = 1, ..., k. Default is sigma2_mu0 = 1.75.

Specifies "shape1" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is a0psi = 1.

Specifies "shape2" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is b0psi = 1.

Optional list of nchain list(s) specifying initial values for parameters and latent variables. Default is initial.values = NULL, which causes initial values to be generated automatically. In addition to the parameters ("pbeta", "N", "delta_1", "delta_2", "alpha", "sigma2_scr", "centers", and "psi"), initial values can be specified for the initial latent history frequencies ("x") and initial individual encounter history indices ("H").

Optional integer vector indicating whether the encounter history of an individual is known with certainty (i.e., the observed encounter history is the true encounter history). Encounter histories with at least one type 4 encounter are automatically assumed to be known, and known does not need to be specified unless there exist encounter histories that do not contain a type 4 encounter that happen to be known with certainty (e.g., from independent telemetry studies). If specified, known = c(v_1,v_2,...,v_M) must be a vector of length M = nrow(Enc.Mat) where v_i = 1 if the encounter history for individual i is known (v_i = 0 otherwise). Note that known all-zero encounter histories (e.g., ‘000’) are ignored.

Upper bound for internal re-scaling of grid cell centroid coordinates. Default is scalemax=10, which re-scales the centroids to be between 0 and 10. Re-scaling is done internally to avoid numerical overflows during model fitting. Ignored unless mms=NULL.

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Additional "parameters" arguments for specifying mod.p. See make.design.data.

Markov chain Monte Carlo object of class mcmc.list.

Model formula for detection probability (as specified by mod.p above).

Model formula for conditional probability of type 1 or type 2 encounter, given detection (as specified by mod.delta above).

Model formula for detection function (as specified by detection above).

A list of design matrices for detection probability generated for model mod.p, where DM$p is the design matrix for initial capture probability (p) and DM$c is the design matrix for recapture probability (c).

A list containing the parameter and latent variable values at iteration iter for each chain. Values are provided for "pbeta", "N", "delta_1", "delta_2", "alpha", "sigma2_scr", "centers", "psi", "x", and "H".

An object of class multimarkSCRsetup

A list of individual model output lists returned by multimarkCJS. The models must have the same number of chains and MCMC iterations.

Vector of length length(modlist) containing prior model probabilities. Default is modprior = rep(1/length(modlist), length(modlist)).

Parameters to monitor. Only parameters common to all models can be monitored (e.g., "pbeta[(Intercept)]", "phibeta[(Intercept)]", "psi"), but derived survival ("phi") and capture ("p") probabilities can also be monitored. Default is monparms = "phi".

The number of RJMCMC iterations per chain. If NULL, then the number of MCMC iterations for each individual model chain is used.

Number of burn-in iterations (0 <= mburnin < miter).

Thinning interval for monitored parameters.

Integer vector indicating the initial model for each chain, where M1_j=i initializes the RJMCMC algorithm for chain j in the model corresponding to modlist[[i]] for i=1,..., length(modlist). If NULL, the algorithm for all chains is initialized in the most general model. Default is M1=NULL.

Scaler specifying the standard deviation of the Normal(0, pbetapropsd) proposal distribution for "pbeta" parameters. Default is pbetapropsd=1. See Barker & Link (2013) for more details.

Scaler specifying the standard deviation of the Normal(0, zppropsd) proposal distribution for "zp" parameters. Only applies if at least one (but not all) model(s) include individual hetergeneity in detection probability. If NULL, zppropsd = sqrt(sigma2_zp) is used. Default is zppropsd=NULL. See Barker & Link (2013) for more details.

Scaler specifying the standard deviation of the Normal(0, phibetapropsd) proposal distribution for "phibeta" parameters. Default is phibetapropsd=1. See Barker & Link (2013) for more details.

Scaler specifying the standard deviation of the Normal(0, zphipropsd) proposal distribution for "zphi" parameters. Only applies if at least one (but not all) model(s) include individual hetergeneity in survival probability. If NULL, zphipropsd = sqrt(sigma2_zphi) is used. Default is zphipropsd=NULL. See Barker & Link (2013) for more details.

Scaler specifying the shape parameter of the invGamma(shape = sigppropshape, scale = sigppropscale) proposal distribution for "sigma2_zp". Only applies if at least one (but not all) model(s) include individual hetergeneity in detection probability. Default is sigppropshape=1. See Barker & Link (2013) for more details.

Scaler specifying the scale parameter of the invGamma(shape = sigppropshape, scale = sigppropscale) proposal distribution for "sigma2_zp". Only applies if at least one (but not all) model(s) include individual hetergeneity in detection probability. Default is sigppropscale=0.01. See Barker & Link (2013) for more details.

Scaler specifying the shape parameter of the invGamma(shape = sigphipropshape, scale = sigphipropscale) proposal distribution for "sigma2_zphi". Only applies if at least one (but not all) model(s) include individual hetergeneity in survival probability. Default is sigphipropshape=1. See Barker & Link (2013) for more details.

Scaler specifying the scale parameter of the invGamma(shape = sigphipropshape, scale = sigphipropscale) proposal distribution for "sigma_zphi". Only applies if at least one (but not all) model(s) include individual hetergeneity in survival probability. Default is sigphipropscale=0.01. See Barker & Link (2013) for more details.

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Reversible jump Markov chain Monte Carlo object of class mcmc.list. Includes RJMCMC output for monitored parameters and the current model at each iteration ("M").

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

A list of individual model output lists returned by multimarkClosed or markClosed. The models must have the same number of chains and MCMC iterations.

Vector of length length(modlist) containing prior model probabilities. Default is modprior = rep(1/length(modlist), length(modlist)).

Parameters to monitor. Only parameters common to all models can be monitored (e.g., "pbeta[(Intercept)]", "N"), but derived capture ("p") and recapture ("c") probabilities can also be monitored. Default is monparms = "N".

The number of RJMCMC iterations per chain. If NULL, then the number of MCMC iterations for each individual model chain is used.

Number of burn-in iterations (0 <= mburnin < miter).

Thinning interval for monitored parameters.

Integer vector indicating the initial model for each chain, where M1_j=i initializes the RJMCMC algorithm for chain j in the model corresponding to modlist[[i]] for i=1,..., length(modlist). If NULL, the algorithm for all chains is initialized in the most general model. Default is M1=NULL.

Scaler specifying the standard deviation of the Normal(0, pbetapropsd) proposal distribution for "pbeta" parameters. Default is pbetapropsd=1. See Barker & Link (2013) for more details.

Scaler specifying the standard deviation of the Normal(0, zppropsd) proposal distribution for "zp" parameters. Only applies if at least one (but not all) model(s) include individual hetergeneity in detection probability. If NULL, zppropsd = sqrt(sigma2_zp) is used. Default is zppropsd=NULL. See Barker & Link (2013) for more details.

Scaler specifying the shape parameter of the invGamma(shape = sigppropshape, scale = sigppropscale) proposal distribution for sigma_zp. Only applies if at least one (but not all) model(s) include individual hetergeneity in detection probability. Default is sigppropshape=6. See Barker & Link (2013) for more details.

Scaler specifying the scale parameter of the invGamma(shape = sigppropshape, scale = sigppropscale) proposal distribution for sigma_zp. Only applies if at least one (but not all) model(s) include individual hetergeneity in detection probability. Default is sigppropscale=4. See Barker & Link (2013) for more details.

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Reversible jump Markov chain Monte Carlo object of class mcmc.list. Includes RJMCMC output for monitored parameters and the current model at each iteration ("M").

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

A list of individual model output lists returned by multimarkClosedSCR or markClosedSCR. The models must have the same number of chains and MCMC iterations.

Vector of length length(modlist) containing prior model probabilities. Default is modprior = rep(1/length(modlist), length(modlist)).

Parameters to monitor. Only parameters common to all models can be monitored (e.g., "pbeta[(Intercept)]", "N", "sigma2_scr"), but derived density ("D") as well as capture ("p") and recapture ("c") probabilities (at distance zero from activity centers) can also be monitored. Default is monparms = "N".

The number of RJMCMC iterations per chain. If NULL, then the number of MCMC iterations for each individual model chain is used.

Number of burn-in iterations (0 <= mburnin < miter).

Thinning interval for monitored parameters.

Integer vector indicating the initial model for each chain, where M1_j=i initializes the RJMCMC algorithm for chain j in the model corresponding to modlist[[i]] for i=1,..., length(modlist). If NULL, the algorithm for all chains is initialized in the most general model. Default is M1=NULL.

Scaler specifying the standard deviation of the Normal(0, pbetapropsd) proposal distribution for "pbeta" parameters. Default is pbetapropsd=1. See Barker & Link (2013) for more details.

Scaler specifying the mean of the inverse Gamma proposal distribution for sigma2_scr (or lambda if detection=``exponential''). Only applies if models do not have the same detection function (i.e., “half-normal” or “exponential”). Default is sigpropmean=0.8. See Barker & Link (2013) for more details.

Scaler specifying the standard deviation of the inverse Gamma proposal distribution for sigma2_scr (or lambda if detection=``exponential''). Only applies if models do not have the same detection function (i.e., “half-normal” or “exponential”). Default is sigpropsd=0.4. See Barker & Link (2013) for more details.

Logical indicating whether to print the progress of chains and any errors to a log file in the working directory. Ignored when nchains=1. Updates are printed to log file as 1% increments of iter of each chain are completed. With >1 chains, setting printlog=TRUE is probably most useful for Windows users because progress and errors are automatically printed to the R console for "Unix-like" machines (i.e., Mac and Linux) when printlog=FALSE. Default is printlog=FALSE.

Reversible jump Markov chain Monte Carlo object of class mcmc.list. Includes RJMCMC output for monitored parameters and the current model at each iteration ("M").

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

An optional object of class multimarkSCRsetup-class from which the (re-scaled) study area and trap coordinates are plotted.

A matrix of dimension ntraps x (2 + noccas) indicating the Cartesian coordinates and operating occasions for the traps, where rows correspond to trap, the first column the x-coordinate, and the second column the y-coordinate. The last noccas columns indicate whether or not the trap was operating on each of the occasions, where ‘1’ indciates the trap was operating and ‘0’ indicates the trap was not operating. Ignored unless mms=NULL.

A 3-column matrix defining the study area and available habitat. Each row corresponds to a grid cell. The first 2 columns indicate the Cartesian x- and y-coordinate for the centroid of each grid cell, and the third column indicates whether the cell is available habitat (=1) or not (=0). All cells must have the same resolution. Ignored unless mms=NULL. Note that rows should be ordered in raster cell order (raster cell numbers start at 1 in the upper left corner, and increase from left to right, and then from top to bottom).

An optional vector indicating the grid cell (i.e., the row of studyArea) that contains the true (latent) activity centers for each individual. If mms is provided, then centers must be of length nrow(Enc.Mat) (i.e., a center must be provided for each observed individual).

Logical indicating whether to draw lines from activity centers to respective traps at which each individual was captured. Default is trapLines=FALSE. Ignored when mms=NULL or centers=NULL.

A matrix of observed encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions (ignored unless mms=NULL).

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

A data frame of temporal covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of sampling occasions. Covariate names cannot be "time", "age", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

Optional integer vector indicating whether the encounter history of an individual is known with certainty (i.e., the observed encounter history is the true encounter history). Encounter histories with at least one type 4 encounter are automatically assumed to be known, and known does not need to be specified unless there exist encounter histories that do not contain a type 4 encounter that happen to be known with certainty (e.g., from independent telemetry studies). If specified, known = c(v_1,v_2,...,v_M) must be a vector of length M = nrow(Enc.Mat) where v_i = 1 if the encounter history for individual i is known (v_i = 0 otherwise). Note that known all-zero encounter histories (e.g., ‘000’) are ignored.

A matrix containing the observed encounter histories with rows corresponding to individuals and (ntraps*noccas) columns corresponding to traps and sampling occasions. The first noccas columns correspond to trap 1, the second noccas columns corresopond to trap 2, etc. Ignored unless mms=NULL.

A matrix of dimension ntraps x (2 + noccas) indicating the Cartesian coordinates and operating occasions for the traps, where rows correspond to trap, the first column the x-coordinate, and the second column the y-coordinate. The last noccas columns indicate whether or not the trap was operating on each of the occasions, where ‘1’ indciates the trap was operating and ‘0’ indicates the trap was not operating.

is a 3-column matrix containing the coordinates for the centroids of a contiguous grid of cells that define the study area and available habitat. Each row corresponds to a grid cell. The first 2 columns indicate the Cartesian x- and y-coordinate for the centroid of each grid cell, and the third column indicates whether the cell is available habitat (=1) or not (=0). All cells must be square and have the same resolution. If studyArea=NULL (the default), then a square study area grid composed of ncells cells of available habitat is drawn around the bounding box of trapCoords based on buffer. Note that rows should be ordered in raster cell order (raster cell numbers start at 1 in the upper left corner, and increase from left to right, and then from top to bottom).

A scaler in same units as trapCoords indicating the buffer around the bounding box of trapCoords for defining the study area when studyArea=NULL. Ignored unless studyArea=NULL.

The number of grid cells in the study area when studyArea=NULL. The square root of ncells must be a whole number. Default is ncells=1024. Ignored unless studyArea=NULL.

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

A data frame of time- and/or trap-dependent covariates for detection probabilities (ignored unless mms=NULL). The number of rows in the data frame must equal the number of traps times the number of sampling occasions (ntraps*noccas), where the first noccas rows correspond to trap 1, the second noccas rows correspond to trap 2, etc. Covariate names cannot be "time", "age", or "h"; these names are reserved for temporal, behavioral, and individual effects when specifying mod.p and mod.phi.

Optional integer vector indicating whether the encounter history of an individual is known with certainty (i.e., the observed encounter history is the true encounter history). Encounter histories with at least one type 4 encounter are automatically assumed to be known, and known does not need to be specified unless there exist encounter histories that do not contain a type 4 encounter that happen to be known with certainty (e.g., from independent telemetry studies). If specified, known = c(v_1,v_2,...,v_M) must be a vector of length M = nrow(Enc.Mat) where v_i = 1 if the encounter history for individual i is known (v_i = 0 otherwise). Note that known all-zero encounter histories (e.g., ‘000’) are ignored.

Upper bound for internal re-scaling of grid cell centroid coordinates. Default is scalemax=10, which re-scales the centroids to be between 0 and 10. Re-scaling is done internally to avoid numerical overflows during model fitting.

Number of individuals.

Number of sampling occasions. floor(N/noccas) individuals are first encountered on each occasion.

Logit- or probit-scale intercept term(s) for capture probability (p). Must be a scaler or vector of length noccas.

Logit- or probit-scale individual heterogeneity variance term for capture probability (p).

Logit- or probit-scale intercept term(s) for survival probability (\phi). Must be a scaler or vector of length noccas.

Logit- or probit-scale individual heterogeneity variance term for survival probability (\phi).

Conditional probability of type 1 encounter, given detection.

Conditional probability of type 2 encounter, given detection.

Conditional probability of simultaneous type 1 and type 2 detection, given both types encountered. Only applies when data.type="sometimes".

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

Link function for detection probability. Must be "logit" or "probit". Note that multimarkCJS is currently implemented for the probit link only.

A matrix containing the observed encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions.

A matrix containing the true (latent) encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions.

True population size or abundance.

The number of sampling occasions.

Logit- or probit-scale intercept term(s) for capture probability (p). Must be a scaler or vector of length noccas.

Additive logit- or probit-scale behavioral effect term for recapture probability (c).

Logit- or probit-scale individual heterogeneity variance term.

Conditional probability of type 1 encounter, given detection.

Conditional probability of type 2 encounter, given detection.

Conditional probability of simultaneous type 1 and type 2 detection, given both types encountered. Only applies when data.type="sometimes".

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

Link function for detection probability. Must be "logit" or "probit". Note that multimarkClosed is currently implemented for the logit link only.

A matrix containing the observed encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions.

A matrix containing the true (latent) encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions.

True population size or abundance.

The number of traps. If trapCoords=NULL, the square root of ntraps must be a whole number in order to create a regular grid of trap coordinates on a square.

Scaler indicating the number of sampling occasions per trap.

Complementary loglog-scale intercept term for detection probability (p). Must be a scaler or vector of length noccas.

Additive complementary loglog-scale behavioral effect term for recapture probability (c).

Complementary loglog-scale term for effect of distance in the “half-normal” detection function. Ignored unless detection=``half-normal''.

Complementary loglog-scale term for effect of distance in the “exponential” detection function. Ignored unless detection=``exponential''.

Conditional probability of type 1 encounter, given detection.

Conditional probability of type 2 encounter, given detection.

Conditional probability of simultaneous type 1 and type 2 detection, given both types encountered. Only applies when data.type="sometimes".

Specifies the encounter history data type. All data types include non-detections (type 0 encounter), type 1 encounter (e.g., left-side), and type 2 encounters (e.g., right-side). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "non-simultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

data.type="never" indicates both type 1 and type 2 encounters are never observed for the same individual within a sampling occasion, and observed encounter histories therefore include only type 1 or type 2 encounters (e.g., only left- and right-sided photographs were collected). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), and type 2 encounters (2). See bobcat. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 3 encounters (3).

data.type="sometimes" indicates both type 1 and type 2 encounters are sometimes observed (e.g., both-sided photographs are sometimes obtained, but not necessarily for all individuals). Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4). Type 3 encounters can only be observed when an individual has at least one type 4 encounter. Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), type 3 encounters (3), and type 4 encounters (4).

data.type="always" indicates both type 1 and type 2 encounters are always observed, but some encounter histories may still include only type 1 or type 2 encounters. Observed encounter histories can consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4). Latent encounter histories consist of non-detections (0), type 1 encounters (1), type 2 encounters (2), and type 4 encounters (4).

Model for detection probability as a function of distance from activity centers. Must be "half-normal" (of the form \exp{(-d^2 / (2*\sigma^2))}, where d is distance) or "exponential" (of the form \exp{(-d / \lambda)}).

A list of length 3 composed of objects named trapCoords, studyArea, and centers:

trapCoords is a matrix of dimension ntraps x (2 + noccas) indicating the Cartesian coordinates and operating occasions for the traps, where rows correspond to trap, the first column the x-coordinate (“x”), and the second column the y-coordinate (“y”). The last noccas columns indicate whether or not the trap was operating on each of the occasions, where ‘1’ indciates the trap was operating and ‘0’ indicates the trap was not operating.

studyArea is a 3-column matrix defining 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 grid cells must have the same resolution. Note that rows should be ordered in raster cell order (raster cell numbers start at 1 in the upper left corner, and increase from left to right, and then from top to bottom).

centers is a N-vector indicating the grid cell (i.e., the row of studyArea) that contains the true (latent) activity centers for each individual in the population.

If spatialInputs=NULL (the default), then all traps are assumed to be operating on all occasions, the study area is assumed to be composed of ncells grid cells, grid cells within buffer of the trap array are assumed to be available habitat, and the activity centers are randomly assigned to grid cells of available habitat.

A scaler indicating the buffer around the bounding box of trapCoords for defining the study area and available habitat when spatialInputs=NULL. Default is buffer=3*sqrt(sigma2_scr). Ignored unless spatialInputs=NULL.

The number of grid cells in the study area when studyArea=NULL. The square root of ncells must be a whole number. Default is ncells=1024. Ignored unless spatialInputs=NULL.

Upper bound for grid cell centroid x- and y-coordinates. Default is scalemax=10, which scales the x- and y-coordinates to be between 0 and 10. Ignored unless spatialInputs=NULL.

Logical indicating whether to plot the simulated trap coordinates, study area, and activity centers using plotSpatialData. Default is plot=TRUE

Matrix containing the observed encounter histories with rows corresponding to individuals and (ntraps*noccas) columns corresponding to traps and sampling occasions. The first noccas columns correspond to trap 1, the second noccas columns corresopond to trap 2, etc.

Matrix containing the true (latent) encounter histories with rows corresponding to individuals and (ntraps*noccas) columns corresponding to traps and sampling occasions. The first noccas columns correspond to trap 1, the second noccas columns corresopond to trap 2, etc.

List of length 2 with objects named trapCoords and studyArea:

trapCoords is a matrix of dimension ntraps x (2 + noccas) indicating the Cartesian coordinates and operating occasions for the traps, where rows correspond to trap, the first column the x-coordinate, and the second column the y-coordinate. The last noccas columns indicate whether or not the trap was operating on each of the occasions, where ‘1’ indciates the trap was operating and ‘0’ indicates the trap was not operating.

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 indicate the Cartesian x- and y-coordinate for the centroid of each grid cell, and the third column indicates whether the cell is available habitat (=1) or not (=0). All cells must have the same resolution.

N-vector indicating the grid cell (i.e., the row of spatialInputs$studyArea) that contains the true (latent) activity centers for each individual in the population.