occu {unmarked} | R Documentation |
This function fits the single season occupancy model of MacKenzie et al (2002).
occu(formula, data, knownOcc=numeric(0), starts, method="BFGS", se=TRUE, engine=c("C", "R"), ...)
formula |
Double right-hand side formula describing covariates of detection and occupancy in that order. |
data |
An |
knownOcc |
Vector of sites that are known to be occupied. These should be supplied as row numbers of the y matrix, eg, c(3,8) if sites 3 and 8 were known to be occupied a priori. |
starts |
Vector of parameter starting values. |
method |
Optimization method used by |
se |
Logical specifying whether or not to compute standard errors. |
engine |
Either "C" or "R" to use fast C++ code or native R code during the optimization. |
... |
Additional arguments to optim, such as lower and upper bounds |
See unmarkedFrame
and unmarkedFrameOccu
for a
description of how to supply data to the data
argument.
occu
fits the standard occupancy model based on zero-inflated
binomial models (MacKenzie et al. 2006, Royle and Dorazio
2008). The occupancy state process (z_i) of site i is
modeled as
z_i ~ Bernoulli(psi_i)
The observation process is modeled as
y_ij | z_i ~ Bernoulli(z_i * p_ij)
Covariates of psi_i and p_ij are modeled
using the logit link according to the formula
argument. The formula is a double right-hand sided formula
like ~ detform ~ occform
where detform
is a formula for the detection process and occform
is a
formula for the partially observed occupancy state. See formula for details on constructing model formulae
in R.
unmarkedFitOccu object describing the model fit.
Ian Fiske
MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. Andrew Royle, and C. A. Langtimm. 2002. Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One. Ecology 83: 2248-2255.
MacKenzie, D. I. et al. 2006. Occupancy Estimation and Modeling. Amsterdam: Academic Press.
Royle, J. A. and R. Dorazio. 2008. Hierarchical Modeling and Inference in Ecology. Academic Press.
unmarked
, unmarkedFrameOccu
,
modSel
, parboot
data(frogs) pferUMF <- unmarkedFrameOccu(pfer.bin) plot(pferUMF, panels=4) # add some fake covariates for illustration siteCovs(pferUMF) <- data.frame(sitevar1 = rnorm(numSites(pferUMF))) # observation covariates are in site-major, observation-minor order obsCovs(pferUMF) <- data.frame(obsvar1 = rnorm(numSites(pferUMF) * obsNum(pferUMF))) (fm <- occu(~ obsvar1 ~ 1, pferUMF)) confint(fm, type='det', method = 'normal') confint(fm, type='det', method = 'profile') # estimate detection effect at obsvars=0.5 (lc <- linearComb(fm['det'],c(1,0.5))) # transform this to probability (0 to 1) scale and get confidence limits (btlc <- backTransform(lc)) confint(btlc, level = 0.9) # Empirical Bayes estimates of proportion of sites occupied re <- ranef(fm) sum(bup(re, stat="mode"))