unmarked (version 0.11-0)

occuRN: Fit the occupancy model of Royle and Nichols (2003)

Description

Fit the occupancy model of Royle and Nichols (2003)

Usage

occuRN(formula, data, K=25, starts, method="BFGS", se=TRUE, ...)

Arguments

formula

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

data

Object of class unmarkedFrameOccu supplying data to the model.

K

the upper summation index used to numerically integrate out the latent abundance. This should be set high enough so that it does not affect the parameter estimates. Computation time will increase with K.

starts

initial values for the optimization.

method

Optimization method used by optim.

se

logical specifying whether or not to compute standard errors.

...

Additional arguments to optim, such as lower and upper bounds

Value

Details

This function fits the latent abundance mixture model described in Royle and Nichols (2003).

The latent abundance of site $i$ is modelled as Poisson:

$$N_i \sim Poisson(\lambda_i)$$

The detection of a single individual in site $i$ during sample $j$ is modelled as Bernoulli:

$$w_{ij} \sim Bernoulli(r_{ij})$$.

Thus, the detection probability for a single site is linked to the detection probability for an individual by

$$p_{ij} = 1 - (1 - r_{ij}) ^ {N_i}$$

Covariates of $lambda_i$ are modelled with the log link and covariates of $r_ij$ are modelled with the logit link.

References

Royle, J. A. and Nichols, J. D. (2003) Estimating Abundance from Repeated Presence-Absence Data or Point Counts. Ecology, 84(3) pp. 777--790.

Examples

## Not run: 
# 
# data(birds)
# woodthrushUMF <- unmarkedFrameOccu(woodthrush.bin)
# # survey occasion-specific detection probabilities
# (fm.wood.rn <- occuRN(~ obsNum ~ 1, woodthrushUMF))
# 
# # Empirical Bayes estimates of abundance at each site
# re <- ranef(fm.wood.rn)
# plot(re)
# 
# 
# ## End(Not run)