Description Usage Arguments Details Value Author(s) References See Also Examples
This function fits CormackJollySeber (CJS) open population models for survival probability (φ) and capture probability (p) from capturemarkrecapture data consisting of multiple noninvasive marks. 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 9 10 11  multimarkCJS(Enc.Mat, data.type = "never", covs = data.frame(),
mms = NULL, mod.p = ~1, mod.phi = ~1, mod.delta = ~type,
parms = c("pbeta", "phibeta", "delta"), 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, maxnumbasis = 1, pbeta0 = 0, pSigma0 = 1,
phibeta0 = 0, phiSigma0 = 1, l0p = 1, d0p = 0.01, l0phi = 1,
d0phi = 0.01, a0delta = 1, a0alpha = 1, b0alpha = 1, a0psi = 1,
b0psi = 1, initial.values = NULL, known = integer(),
link = "probit", printlog = FALSE, ...)

Enc.Mat 
A matrix of observed encounter histories with rows corresponding to individuals and columns corresponding to sampling occasions (ignored unless 
data.type 
Specifies the encounter history data type. All data types include nondetections (type 0 encounter), type 1 encounter (e.g., leftside), and type 2 encounters (e.g., rightside). When both type 1 and type 2 encounters occur for the same individual within a sampling occasion, these can either be "nonsimultaneous" (type 3 encounter) or "simultaneous" (type 4 encounter). Three data types are currently permitted:

covs 
A data frame of temporal covariates for detection probabilities (ignored unless 
mms 
An optional object of class 
mod.p 
Model formula for detection probability (p). For example, 
mod.phi 
Model formula for survival probability (φ). For example, 
mod.delta 
Model formula for conditional probabilities of type 1 (delta_1) and type 2 (delta_2) encounters, given detection. Currently only 
parms 
A character vector giving the names of the parameters and latent variables to monitor. Possible parameters are probitscale 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 burnin 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. 
maxnumbasis 
Maximum number of basis vectors to use when proposing latent history frequency updates. Default is 
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 
a0delta 
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 (1delta_1delta_2). If 
a0alpha 
Specifies "shape1" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when 
b0alpha 
Specifies "shape2" parameter for [alpha] ~ Beta(a0alpha, b0alpha) prior. Only applicable when 
a0psi 
Specifies "shape1" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is 
b0psi 
Specifies "shape2" parameter for [psi] ~ Beta(a0psi,b0psi) prior. Default is 
initial.values 
Optional list of 
known 
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 
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 multimarkCJS
(or multimarkClosed
) 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 multimarkCJS
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
Bonner, S. J., and Holmberg J. 2013. Markrecapture with multiple, noninvasive marks. Biometrics 69: 766775.
McClintock, B. T., Conn, P. B., Alonso, R. S., and Crooks, K. R. 2013. Integrated modeling of bilateral photoidentification data in markrecapture analyses. Ecology 94: 14641471.
McClintock, B. T., Bailey, L. L., Dreher, B. P., and Link, W. A. 2014. Probit models for capturerecapture data subject to imperfect detection, individual heterogeneity and misidentification. The Annals of Applied Statistics 8: 24612484.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # This example is 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()
#Fit default open population model
sim.dot < multimarkCJS(data$Enc.Mat)
#Posterior summary for monitored parameters
summary(sim.dot$mcmc)
plot(sim.dot$mcmc)
#' #Fit ``age'' model with 2 age classes (e.g., juvenile and adult) for survival
#using 'parameters' and 'right' arguments from RMark::make.design.data
sim.age < multimarkCJS(data$Enc.Mat,mod.phi=~age,
parameters=list(Phi=list(age.bins=c(0,1,4))),right=FALSE)
summary(getprobsCJS(sim.age))

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