Unconditional (complete) likelihood inference for a large class of discretetime capturerecapture 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  LBRecap ( data,last.column.count = FALSE, neval = 1000, startadd=0, by.incr = 1,
mbc.function = c("standard","markov","counts","integer","counts.integer"),
mod = c("linear.logistic", "M0", "Mb", "Mc", "Mcb", "Mt", "Msubjective.cut",
"Msubjective"), heterogeneity=FALSE, markov.ord=NULL, z.cut=c(),
meaningful.mat.subjective = NULL, meaningful.mat.new.value.subjective = NULL,
td.cov = NULL, td.cov.formula ="", verbose = FALSE, graph = FALSE,
output = c( "base", "complete" ) )

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. 
startadd 
a positive integer. The likelihood evaluation is started from M+ 
by.incr 
a positive integer. 
mbc.function 
a character string with possible entries (see Alunni Fegatelli (2013) for further details)

mod 
a character. 
heterogeneity 
a logical. If 
markov.ord 
a positive integer. 
meaningful.mat.subjective 

meaningful.mat.new.value.subjective 

z.cut 
numeric vector. 
td.cov 
data frame or matrix with k columns and t rows with each column corresponding to a timedependent covariate to be used at each capture occasion for any captured/uncaptured unit 
td.cov.formula 
a character string to be used as additional component in the 
verbose 
a logical. If 
graph 
a logical. If 
output 
character. 
The LBRecap
procedure is computing intensive for high values of neval
.
(if output="complete"
) the function LBRecap
returns a list of:
Modelmodel considered.
N.hatunconditional maximum likelihood estimate for N
CIinterval estimate for N
AICAkaike information criterion.
L.FailureLikelihood Failure condition
N.rangevalues of N considered.
log.likvalues of the loglikelihood distribution for each N considered
z.matrixmeaningful behavioural covariate matrix for the observed data
vec.cutcut 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.
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
LBRecap.custom.part
,
LBRecap.all
,
BBRecap
1 2 3  data(greatcopper)
mod.Mb=LBRecap(greatcopper,mod="Mb")
str(mod.Mb)

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