Standard behavioural and time effect models via unconditional (complete) likelihood approach
Description
Comparative point and interval estimates for the population size N obtained fitting many alternative behavioural and time effect capturerecapture models. AIC index is reported for each alternative model.
Usage
1 2 
Arguments
data 
can be one of the following:

last.column.count 
a logical. In the default case 
neval 
a positive integer. 
by.incr 
a positive integer. 
which.mod 
a character. 
sort 
character. 
Details
The available models are: M_0, M_b, M_t, M_{c_1}, M_{c_1b}, M_{c_2}, M_{c_2b}, M_{mc},M_{mc_{int}}, M_{mc_{count}} and M_{mc_{count.int}}.
This function LBRecap.all
can be computing intensive for high values of neval
.
Value
A dataframe with one row corresponding to each model and the following columns:
model: model considered
npar: number of parameters
AIC: Akaike's information criterion
Nhat: estimate of population size
Ninf: lower 95 \% confidence limit
Nsup: upper 95 \% confidence limit
Author(s)
Danilo Alunni Fegatelli and Luca Tardella
References
Alunni Fegatelli D. (2013) New methods for capturerecapture modelling with behavioural response and individual heterogeneity.
Alunni Fegatelli D., Tardella L. (2012) Improved inference on capture recapture models with behavioural effects. Statistical Methods & Applications Applications Volume 22, Issue 1, pp 4566 10.1007/s1026001202214
Farcomeni A. (2011) Recapture models under equality constraints for the conditional capture probabilities. Biometrika 98(1):237–242
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 capturerecapture experiments, Biometrics 61(4), 10101017
See Also
LBRecap
,
Examples
1 2 3 4 5  ## Not run:
data(greatcopper)
LBRecap.all(greatcopper)
## End(Not run)

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