unmarked (version 0.8-5)

multinomPois: Multinomial-Poisson Mixtures

Description

Fit the multinomial-Poisson abundance mixture model.

Usage

multinomPois(formula, data, starts, method = "BFGS", control = list(), 
	se = TRUE)

Arguments

formula

double right-hand side formula for detection and abundance covariates, in that order.

data

unmarkedFrame supplying data.

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.

Value

• unmarkedFit object describing the model fit.

Details

This function takes advantage of the closed form of the integrated likelihood when a latent Poisson distribution is assumed for abundance at each site and a multinomial distribution is taken for the observation state. Many common sampling methods can be framed in this context. For example, double-observer point counts, removal sampling, and distance sampling can all be analyzed with this function by specifying the proper multinomial cell probablilities. This is done with by supplying the appropriate function (piFun) argument. removalPiFun and doublePiFun are supplied as example cell probability functions.

References

Royle, J. A., Dawson, D., & Bates, S. (2004). Modeling abundance effects in distance sampling. Ecology, 85(6), 1591-1597. Royle, J. A. (2004). Generalized estimators of avian abundance from count survey data. Animal Biodiversity and Conservation, 27(1), 375-386.

Royle, J. A., & Dorazio, R. M. (2006). Hierarchical Models of Animal Abundance and Occurrence. Journal Of Agricultural Biological And Environmental Statistics, 11(3), 249.

See Also

piFuns

Examples

data(ovendata)
ovenFrame <- unmarkedFrameMPois(ovendata.list$data,
siteCovs=as.data.frame(scale(ovendata.list$covariates[,-1])), type = "removal")
(fm1 <- multinomPois(~ 1 ~ ufp + trba, ovenFrame))
(fm2 <- multinomPois(~ 1 ~ ufp, ovenFrame))
(fm3 <- multinomPois(~ 1 ~ trba, ovenFrame))
(fm4 <- multinomPois(~ 1 ~ 1, ovenFrame))
fmList <- fitList(fits=list(Global=fm1, ufp=fm2, trba=fm3,Null=fm4))

# Model selection
modSel(fmList, nullmod="Null")