pcount {unmarked} | R Documentation |
Fit the N-mixture point count model
pcount(formula, data, K, mixture=c("P", "NB"), starts, method="BFGS", control=list(), se=TRUE)
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. |
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, Kery and Royle 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.
unmarkedFit object describing the model fit.
Ian Fiske ianfiske@gmail.com
Royle, J. A. (2004) N-Mixture Models for Estimating Population Size from Spatially Replicated Counts. Biometrics 60, pp. 108–105.
Kery, M. and Royle, J. A. (2005) Modeling Avaian Abundance from Replicated Counts Using Binomial Mixture Models. Ecological Applications 15(4), pp. 1450–1461.
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))