pcount: Fit the N-mixture point count model...

In ianfiske/unmarked: Models for Data from Unmarked Animals

Description

Fit the N-mixture point count model

Usage

pcount(formula, data, K, mixture=c("P", "NB"), starts, method="BFGS",
    control=list(), se=TRUE)

Arguments

formula	Double right-hand side formula describing covariates of detection and abundance, in that order
data	an unmarkedFramePCount object supplying data to the model.
K	Integer upper index of integration for N-mixture.
mixture	character specifying mixture: either "P" or "NB".
starts	vector of starting values
method	Optimization method used by optim.
control	Other arguments passed to optim.
se	logical specifying whether or not to compute standard errors.

Details

This function fits binomial-Poisson mixture model for spatially replicated point count data.

See unmarkedFrame for a description of how to supply by creating and unmarkedFrame.

This function fits the latent N-mixture model for point count data (Royle 2004, Kéry et al 2005).

The latent abundance distribution, f(N | theta) can be set as either a Poisson or a negative binomial random variable, depending on the setting of the mixture argument. mixture = "P" or mixture = "NB" select the Poisson or negative binomial distribution respectively. The mean of N_i is lambda_i. If N_i ~ NB, then an additional parameter, alpha, describes dispersion (lower alpha implies higher variance).

The detection process is modeled as binomial: y_ij ~ Binomial(N_i, p_ij).

Covariates of lamdba_i use the log link and covariates of p_ij use the logit link.

Value

unmarkedFit object describing the model fit.

Author(s)

Ian Fiske [e-mail]

References

Royle, J. A. (2004) N-Mixture Models for Estimating Population Size from Spatially Replicated Counts. Biometrics 60, pp. 108–105.

Kéry, M., Royle, J. A., and Schmid, H. (2005) Modeling Avaian Abundance from Replicated Counts Using Binomial Mixture Models. Ecological Applications 15(4), pp. 1450–1461.

Examples

data(mallard)
mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs = mallard.site,
obsCovs = mallard.obs)
(fm.mallard <- pcount(~ ivel+ date + I(date^2) ~ length + elev + forest, mallardUMF))
(fm.mallard.nb <- pcount(~ date + I(date^2) ~ length + elev, mixture = "NB", mallardUMF))

ianfiske/unmarked documentation built on May 18, 2019, 1:28 a.m.