Rcapture (version 1.4-3)

closedp.Mtb: Closed Population Capture-Recapture Model Mtb

Description

This function fits model Mtb for closed populations in capture-recapture experiments.

Usage

closedp.Mtb(X, dfreq=FALSE, method = "BFGS", …)

# S3 method for closedp.Mtb print(x, …)

Arguments

X

The matrix of the observed capture histories (see Rcapture-package for a description of the accepted formats).

dfreq

A logical. By default FALSE, which means that X has one row per unit. If TRUE, it indicates that the matrix X contains frequencies in its last column.

method

The method to be used by optim. The default is "BFGS".

Further arguments to be passed to optim or print.default.

x

An object, produced by the closedp.Mtb function, to print.

Value

n

The number of captured units

t

The total number of capture occasions in the data matrix X.

results

A table containing, for the fitted model:

abundance

: the estimated population size,

stderr

: the standard error of the estimated population size,

deviance

: the model's deviance,

df

: the number of degrees of freedom,

AIC

: the Akaike's information criterion,

BIC

: the bayesian information criterion,

infoFit

: a numerical code giving information about error or warnings encountered when fitting the model (see Rcapture-package for details).

optim

The output produced by optim from fitting the model.

optim.warn

A vector of character strings. If the optim function generates one or more warnings when fitting the model, a copy of these warnings are stored in optim.warn. NULL if optim did not produce any warnings.

parMtb

Capture-recapture parameters estimates for model Mtb : the abundance N, \(p_1\) to \(p_t\), the probabilities of first capture for each capture occasion, and \(c_2\) to \(c_t\), the recapture probabilities for each capture occasion.

Details

The Mtb model is non-linear. It is fitted with the optim function instead of the glm function. Therefore, the abundance estimate can be unstable.

For the model to be identifiable, the parameters are constrained in the following way: \(logit(c_i)=logit(p_i)+b\) for i in \(2,\ldots,l\).

References

Baillargeon, S. and Rivest, L.P. (2007) Rcapture: Loglinear models for capture-recapture in R. Journal of Statistical Software, 19(5), http://www.jstatsoft.org/v19/i05.

See Also

closedp, closedpCI.t

Examples

Run this code
# NOT RUN {
# hare data set
closedp.Mtb(hare)

## Example producing an unstable estimate
# Fourth primary period of mvole data set
period4 <- mvole[, 16:20]
closedp.Mtb(period4)
# }

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