Description Usage Arguments Details Value Author(s) References See Also Examples
Unconditional (complete) likelihood 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 | 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. |
z.cut |
numeric vector. |
meaningful.mat.subjective |
|
meaningful.mat.new.value.subjective |
|
td.cov |
data frame or matrix with k columns and t rows with each column corresponding to a time-dependent 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 log-likelihood 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. (2016), Flexible behavioral capture–recapture modeling. Biometrics, 72(1):125-135. doi:10.1111/biom.12417
Alunni Fegatelli D. (2013) New methods for capture-recapture modelling with behavioural response and individual heterogeneity. http://hdl.handle.net/11573/918752
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
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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