Description Usage Arguments Details Value Author(s) References See Also Examples
Bayesian inference for a large class of discrete-time capture-recapture models under closed population with special emphasis on behavioural effect modelling including also the meaningful behavioral covariate approach proposed in Alunni Fegatelli (2013) [PhD thesis]. Many of the standard classical models such as M_0, M_b, M_{c_1}, M_t or M_{bt} can be regarded as particular instances of the aforementioned approach. Other flexible alternatives can be fitted through a careful choice of a meaningful behavioural covariate and a possible partition of its admissible range.
1 2 3 4 5 6 7 8 | BBRecap (data,last.column.count=FALSE, neval = 1000, by.incr = 1,
mbc.function = c("standard","markov","counts","integer","counts.integer"),
mod = c("linear.logistic", "M0", "Mb", "Mc", "Mcb", "Mt", "Msubjective.cut",
"Msubjective"), nsim = 5000, burnin = round(nsim/10),
nsim.ML = 1000, burnin.ML = round(nsim.ML/10), num.t = 50,
markov.ord=NULL, prior.N = c("Rissanen","Uniform","one.over.N","one.over.N2"),
meaningful.mat.subjective = NULL, meaningful.mat.new.value.subjective = NULL,
z.cut=NULL, output = c("base", "complete", "complete.ML"))
|
data |
can be one of the following:
M is the number of units captured at least once and t is the number of capture occasions. |
last.column.count |
a logical. In the default case |
neval |
a positive integer. |
by.incr |
a positive integer. |
mbc.function |
a character string with possible entries (see Alunni Fegatelli (2013) for further details)
|
mod |
a character. |
nsim |
a positive integer. |
burnin |
a positive integer. |
nsim.ML |
a positive integer. Whenever MCMC is needed |
burnin.ML |
a positive integer. Whenever MCMC is needed |
num.t |
a positive integer. Whenever MCMC is needed |
markov.ord |
a positive integer. |
prior.N |
a character. |
meaningful.mat.subjective |
|
meaningful.mat.new.value.subjective |
|
z.cut |
numeric vector. |
output |
a character. |
Independent uniform distributions are considered as default prior for the nuisance parameters. If model="linear.logistic"
or model="Msubjective"
and output="complete.ML"
the marginal likelihood estimation is performed through the power posteriors method suggested in Friel and Pettit (2008). In that case the BBRecap
procedure is computing intensive for high values of neval
and nsim
.
Model |
model considered |
Prior |
prior distribution for N |
N.hat.mean |
posterior mean for N |
N.hat.median |
posterior median for N |
N.hat.mode |
posterior mode for N |
N.hat.RMSE |
minimizer of a specific loss function connected with the Relative Mean Square Error |
HPD.N |
95 \% highest posterior density interval estimate for N |
log.marginal.likelihood |
log marginal likelihood |
N.range |
values of N considered |
posterior.N |
values of the posterior distribution for each N considered |
z.matrix |
meaningful behavioural covariate matrix for the observed data |
vec.cut |
cut point used to set up meaningful partitions the set of the partial capture histories according to the value of the value of the meaningful behavioural covariate |
N.vec |
simulated values from the posterior marginal distribution of N |
mean.a0 |
posterior mean of the parameter a0 |
hpd.a0 |
highest posterior density interval estimate of the parameter a0 |
a0.vec |
simulated values from the posterior marginal distribution of a0 |
mean.a1 |
posterior mean of the parameter a1 |
hpd.a1 |
highest posterior density interval estimate of the parameter a1 |
a1.vec |
simulated values from the posterior marginal distribution of a1 |
Danilo Alunni Fegatelli and Luca Tardella
Otis D. L., Burnham K. P., White G. C, Anderson D. R. (1978) Statistical Inference From Capture Data on Closed Animal Populations, Wildlife Monographs.
Yang H.C., Chao A. (2005) Modeling animals behavioral response by Markov chain models for capture-recapture experiments, Biometrics 61(4), 1010-1017
N. Friel and A. N. Pettitt. Marginal likelihood estimation via power posteriors. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(3):589, 607–2008
Farcomeni A. (2011) Recapture models under equality constraints for the conditional capture probabilities. Biometrika 98(1):237–242
Alunni Fegatelli, D. and Tardella, L. (2012) Improved inference on capture recapture models with behavioural effects. Statistical Methods & Applications Applications Volume 22, Issue 1, pp 45-66 10.1007/s10260-012-0221-4
Alunni Fegatelli D. (2013) New methods for capture-recapture modelling with behavioural response and individual heterogeneity. http://hdl.handle.net/11573/918752
1 2 3 4 5 6 7 | ## Not run:
data(greatcopper)
mod.Mb=BBRecap(greatcopper,mod="Mb")
str(mod.Mb)
## End(Not run)
|
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