| Bayesian SCR | R Documentation |
Functions to estimate density from mark-recapture data using MCMC methods and JAGS.
Bsecr0(capthist, buffer = 100, start = NULL, nAug = NA, maxSig = 2*buffer,
chains=3, draws=1e4, burnin=0, thin=1, adapt=1000,
priorOnly=FALSE, parallel=NULL, seed=NULL, ...)
capthist |
a |
buffer |
scalar mask buffer radius (default 100 m) |
start |
an optional object of class |
nAug |
number of individuals in the augmented population; if NA, a suitable default is chosen based on the object passed to |
maxSig |
maximum value for the scale parameter of the detection function: the prior is Uniform(0, maxSig). |
chains |
the number of Markov chains to run. |
draws |
the total number of values to return. The number of values calculated per chain is |
burnin |
the number of values to discard at the beginning of each chain. |
thin |
the thinning rate. If set to n > 1, n values are calculated for each value returned. |
adapt |
the number of iterations to run in the JAGS adaptive phase. |
priorOnly |
if TRUE, the function produces random draws from the appropriate prior distributions, with a warning. |
seed |
set a seed for the random number generators. |
parallel |
if TRUE or NULL and sufficient cores are available, the MCMC chains are run in parallel; if TRUE and insufficient cores are available, a warning is given. |
... |
other arguments to pass to the function. |
Bsecr0 implements an intercept-only model (D ~ 1, g0 ~ 1, sigma ~ 1).
Returns an object of class Bwiqid, data frame with one column for each parameter, ie. D, lam0 and sigma.
There are print, plot, and window methods for Bwiqid.
Mike Meredith
Borchers & Efford (2008) Spatially explicit maximum likelihood methods for capture-recapture studies Biometrics 64, 377-385
Royle & Dorazio (2008) Hierarchical Modeling and Inference in Ecology. Academic Press
The function secr.fit in package secr.
if(requireNamespace("secr") && requireNamespace("rjags")) {
# The stoats data set in 'secr'
data(stoatDNA, package="secr")
# This takes ca 10 mins on a multicore machine:
Bout <- Bsecr0(stoatCH, buffer=1000, chains=2) # 2 chains for testing
summary(Bout)
plot(Bout)
# look at diagnostic plots to see if D is constrained by nAug:
diagPlot(Bout) # Upper values of D doesn't look constrained.
plotACs(Bout, 1:20) # Plot the ACs for captured animals
}
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