Unconditional likelihood inference for a general model framework based on the capture probabilities conditioned on each possible partial capture history. As suggested in Alunni Fegatelli and Tardella (2012) the conditional approach originally proposed in Farcomeni (2011) [saturated reparameterization] is reviewed in terms of partitions into equivalence classes of conditional probabilities. In this function the user can directly provide the model as a partition.
1 2 3  LBRecap.custom.part (data,last.column.count=FALSE, partition, neval = 1000,
by.incr = 1, output = c("base", "complete"))

data 
can be one of the following:

last.column.count 
a logical. In the default case 
partition 
list. 
neval 
a positive integer. 
by.incr 
a positive integer. 
output 
character. 
The unconditional likelihood is evaluated by means of glm/glmer
for each value of the N
parameter and it is then maximized.
(if output="complete"
) the function LBRecap
returns a list of:
N.hatunconditional maximum likelihood estimate for N
CIinterval estimate for N
pH.hatpoint estimate of nuisance parameters (conditional probabilities)
AICAkaike information criterion.
L.FailureLikelihood Failure condition
N.rangesequence of N values considered
log.likvalues of the loglikelihood distribution for each N considered
partitionslist of subsets of partial capture histories corresponding to equivalence classes
Danilo Alunni Fegatelli and Luca Tardella
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 4566 10.1007/s1026001202214
Farcomeni A. (2011) Recapture models under equality constraints for the conditional capture probabilities. Biometrika 98(1):237–242
partition.ch
,
BBRecap.custom.part
,
LBRecap
1 2 3 4  data(greatcopper)
partition.Mc1=partition.ch(quant.binary,t=ncol(greatcopper),breaks=c(0,0.5,1))
mod.Mc1=LBRecap.custom.part(greatcopper,partition=partition.Mc1)
str(mod.Mc1)

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