Functions to estimate density from markrecapture data using MCMC methods and JAGS.
1 2 3 
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 
chains 
the number of Markov chains to run. 
sample 
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 insufficent cores are available, a warning is given. 
Bsecr0
implements an interceptonly 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 capturerecapture studies Biometrics 64, 377385
Royle & Dorazio (2008) Hierarchical modeling and inference in ecology. Academic Press
The function secr.fit
in package secr
.
1 2 3 4 5 6 7 8 9 10  # The stoats data set in 'secr'
require(secr)
data(stoatDNA)
# This takes ca 10 mins on a multicore machine:
Bout < Bsecr0(stoatCH, buffer=1000)
Bout
plot(Bout)
# look at diagnostic plots to see if D is constrained by nAug:
tracePlot(Bout)
densityPlot(Bout) # Upper values of D doesn't look constrained.

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