goccu.RdFit multi-scale occupancy models as described in Nichols et al. (2008) to repeated presence-absence data collected using the robust design. This model allows for inference about occupancy, availability, and detection probability.
goccu(psiformula, phiformula, pformula, data, linkPsi = c("logit", "cloglog"),
starts, method = "BFGS", se = TRUE, ...)Right-hand sided formula describing occupancy covariates
Right-hand sided formula describing availability covariates
Right-hand sided formula for detection probability covariates
An object of class unmarkedFrameGOccu or unmarkedMultFrame
Link function for the occupancy model. Options are
"logit" for the standard occupancy model or "cloglog"
for the complimentary log-log link, which relates occupancy
to site-level abundance.
Starting values
Optimization method used by optim
Logical. Should standard errors be calculated?
Additional arguments to optim, such as lower and upper
bounds
Primary periods could represent spatial or temporal sampling replicates. For example, you could have several spatial sub-units within each site, where each sub-unit was then sampled repeatedly. This is a frequent design for eDNA studies. Or, you could have multiple primary periods of sampling at each site (conducted at different times within a season), each of which contains several secondary sampling periods. In both cases the robust design structure can be used to estimate an availability probability in addition to detection probability. See Kery and Royle (2015) 10.10 for more details.
An object of class unmarkedFitGOccu
Kery, M., & Royle, J. A. (2015). Applied hierarchical modeling in ecology: Volume 1: Prelude and static models. Elsevier Science.
Nichols, J. D., Bailey, L. L., O'Connell Jr, A. F., Talancy, N. W., Campbell Grant, E. H., Gilbert, A. T., Annand E. M., Husband, T. P., & Hines, J. E. (2008). Multi-scale occupancy estimation and modelling using multiple detection methods. Journal of Applied Ecology, 45(5), 1321-1329.
set.seed(123)
M <- 100
T <- 5
J <- 4
psi <- 0.5
phi <- 0.3
p <- 0.4
z <- rbinom(M, 1, psi)
zmat <- matrix(z, nrow=M, ncol=T)
zz <- rbinom(M*T, 1, zmat*phi)
zz <- matrix(zz, nrow=M, ncol=T)
zzmat <- zz[,rep(1:T, each=J)]
y <- rbinom(M*T*J, 1, zzmat*p)
y <- matrix(y, M, J*T)
umf <- unmarkedMultFrame(y=y, numPrimary=T)
if (FALSE) { # \dontrun{
mod <- goccu(psiformula = ~1, phiformula = ~1, pformula = ~1, umf)
plogis(coef(mod))
} # }