Description Usage Arguments Details Value References See Also Examples
Estimate parameters of the colonization-extinction model, including covariate-dependent rates and detection process.
1 2 3 |
psiformula |
Right-hand sided formula for the initial probability of occupancy at each site. |
gammaformula |
Right-hand sided formula for colonization probability. |
epsilonformula |
Right-hand sided formula for extinction probability. |
pformula |
Right-hand sided formula for detection probability. |
data |
unmarkedMultFrame object that supplies the data (see |
starts |
optionally, initial values for parameters in the optimization. |
method |
Optimization method used by |
control |
Other arguments passed to |
se |
logical specifying whether or not to compute standard errors. |
This function fits the colonization-extinction model of
MacKenzie et al (2003). The colonization and extinction rates can be
modeled with covariates that vary yearly at each site using a logit
link. These covariates are supplied by special unmarkedMultFrame
yearlySiteCovs
slot. These parameters are specified using the
gammaformula
and epsilonformula
arguments. The initial
probability of occupancy is modeled by covariates specified in the
psiformula
.
The conditional detection rate can also be modeled as a function of
covariates that vary at the secondary sampling period (ie., repeat
visits). These covariates are specified by the first part of the
formula
argument and the data is supplied via the usual
obsCovs
slot.
The projected and smoothed trajectories (Weir et al 2009) can be
obtained from the smoothed.mean
and projected.mean
slots
(see examples).
unmarkedFitColExt object describing model fit.
MacKenzie, D.I. et al. (2002) Estimating Site Occupancy Rates When Detection Probabilities Are Less Than One. Ecology, 83(8), 2248-2255.
MacKenzie, D. I. et al. (2006) Occupancy Estimation and Modeling.Amsterdam: Academic Press.
Weir L. A., Fiske I. J., Royle J. (2009) Trends in Anuran Occupancy from Northeastern States of the North American Amphibian Monitoring Program. Herpetological Conservation and Biology. 4(3):389-402.
nonparboot
, unmarkedMultFrame
, and formatMult
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | data(frogs)
umf <- formatMult(masspcru)
obsCovs(umf) <- scale(obsCovs(umf))
## Use 1/4 of data just for run speed in example
umf <- umf[which((1:numSites(umf)) %% 4 == 0),]
## constant transition rates
(fm <- colext(psiformula = ~ 1,
gammaformula = ~ 1,
epsilonformula = ~ 1,
pformula = ~ JulianDate + I(JulianDate^2), umf, control = list(trace=1, maxit=1e4)))
## get the trajectory estimates
smoothed(fm)
projected(fm)
## Not run:
## Find bootstrap standard errors for smoothed trajectory
fm <- nonparboot(fm, B = 100) # This takes a while!
fm@smoothed.mean.bsse
## End(Not run)
## Not run:
## try yearly transition rates
yearlySiteCovs(umf) <- data.frame(year = factor(rep(1:7, numSites(umf))))
(fm.yearly <- colext(psiformula = ~ 1,
gammaformula = ~ year,
epsilonformula = ~ year,
pformula = ~ JulianDate + I(JulianDate^2), umf,
control = list(trace=1, maxit=1e4)))
## End(Not run)
|
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