gpcount {unmarked} | R Documentation

## Generalized binomial N-mixture model for repeated count data

### Description

Fit the model of Chandler et al. (2011) to repeated count data collected using the robust design. This model allows for inference about population size, availability, and detection probability.

### Usage

    gpcount(lambdaformula, phiformula, pformula, data,
    mixture = c("P", "NB"), K, starts, method = "BFGS", se = TRUE,
    engine = c("C", "R"), ...)

### Arguments

`lambdaformula` | Right-hand sided formula describing covariates of abundance.
`phiformula` | Right-hand sided formula describing availability covariates
`pformula` | Right-hand sided formula for detection probability covariates
`data` | An object of class unmarkedFrameGPC
`mixture` | Either "P" or "NB" for Poisson and negative binomial distributions
`K` | The maximum possible value of M, the super-population size.
`starts` | Starting values
`method` | Optimization method used by `optim`
`se` | Logical. Should standard errors be calculated?
`engine` | Either "C" or "R" for the C++ or R versions of the likelihood. The C++ code is faster, but harder to debug.
`...` | Additional arguments to `optim`, such as lower and upper bounds

### Details

The latent transect-level super-population abundance distribution f(M | theta) can be set as either a Poisson or a negative binomial random variable, depending on the setting of the `mixture` argument. The expected value of M_i is lambda_i. If M_i ~ NB, then an additional parameter, alpha, describes dispersion (lower alpha implies higher variance).

The number of individuals available for detection at time j is a modeled as binomial: N(i,j) ~ Binomial(M(i), phi(i,j)).

The detection process is also modeled as binomial: y(i,k,j) ~ Binomial(N(i,t), p(i,k,j)).

Parameters lambda, phi and p can be modeled as linear functions of covariates using the log, logit and logit links respectively.

### Value

An object of class unmarkedFitGPC

### Note

In the case where availability for detection is due to random temporary emigration, population density at time j, D(i,j), can be estimated by N(i,j)/plotArea.

This model is also applicable to sampling designs in which the local population size is closed during the J repeated counts, and availability is related to factors such as the probability of vocalizing. In this case, density can be estimated by M(i)/plotArea.

If availability is a function of both temporary emigration and other processess such as song rate, then density cannot be directly estimated, but inference about the super-population size, M(i), is possible.

Three types of covariates can be supplied, site-level, site-by-year-level, and observation-level. These must be formatted correctly when organizing the data with `unmarkedFrameGPC`

### Author(s)

Richard Chandler rbchan@uga.edu

### References

Royle, J. A. 2004. N-Mixture models for estimating population size from spatially replicated counts. Biometrics 60:108–105.

Chandler, R. B., J. A. Royle, and D. I. King. 2011. Inference about density and temporary emigration in unmarked populations. Ecology 92:1429-1435.

### See Also

`gmultmix`, `gdistsamp`, `unmarkedFrameGPC`

### Examples

    set.seed(54)

    nSites <- 20
    nVisits <- 4
    nReps <- 3

    lambda <- 5
    phi <- 0.7
    p <- 0.5

    M <- rpois(nSites, lambda) # super-population size

    N <- matrix(NA, nSites, nVisits)
    y <- array(NA, c(nSites, nReps, nVisits))
    for(i in 1:nVisits) {
        N[,i] <- rbinom(nSites, M, phi) # population available during vist j
    }
    colMeans(N)

    for(i in 1:nSites) {
        for(j in 1:nVisits) {
            y[i,,j] <- rbinom(nReps, N[i,j], p)
        }
    }

    ym <- matrix(y, nSites)
    ym[1,] <- NA
    ym[2, 1:nReps] <- NA
    ym[3, (nReps+1):(nReps+nReps)] <- NA
    umf <- unmarkedFrameGPC(y=ym, numPrimary=nVisits)

    ## Not run:
    fmu <- gpcount(~1, ~1, ~1, umf, K=40, control=list(trace=TRUE, REPORT=1))

    backTransform(fmu, type="lambda")
    backTransform(fmu, type="phi")
    backTransform(fmu, type="det")

    ## End(Not run)

[Package unmarked version 0.12-3 Index]