Description Usage Arguments Details Value Warning Note Author(s) References See Also Examples
siteocc
will fit a patch occupancy model to histories of observations and can account for an imperfect probability of detection.
1 |
psi |
an object of class |
p |
an object of class |
histories |
matrix of encounter histories. One row per site, one column per visit. This argument should be of size nsites x nvisits. A NA may be used in the |
start |
vector of starting values passed to the |
lower |
vector of lower bounds passed to the |
... |
other arguments passed to the |
The log-likelihood is maximized using the nlminb
function. If the user decides to provide starting values, then they will need to specify values for each PSI covariate as well as each P covariate, including intercepts. The starting values should be listed in the order of the specified covariates (i.e., the PSI intercept starting value first, the PSI coefficient(s) starting value(s) next, then the P intercept starting value, and finally the P Coefficient(s) starting value(s)). See below for an example of format. If specifying a Beta-mixture model, then the starting values are 0.5 for all of the covariates including the intercept. Otherwise, 0 is used for the starting value of each covariate.
The same format used to specify starting values should also be used to specify the lower bounds for the lower
argument. The default lower bounds for a Beta-mixture are -Inf for all PSI covariates and 0 for both of the beta-binomial parameters. If you specify a lower bound of less than 0 for the beta-binomial parameters, your model will most likely not converge. This is because both parameters must be strictly greater than 0. If a Beta-mixture is not specified, then -Inf will be the lower bound for all covariates.
Unless otherwise specified, the default values of the nlminb
function are used.
The example datasets are detailed in pages 116-122 of MacKenzie et al. (2006) and also included with the program PRESENCE.
loglik |
Optimized log-likelihood. |
convergence |
An integer code. 0 indicates successful convergence. See the Value section of |
convergence.message |
A character string giving any additional information returned by the optimizer, or NULL. See the Value section of |
call |
The matched call. |
naive.psi.est |
Naive Estimate of Occupancy. |
nsites |
Number of Sites. |
nvisits |
Number of Visits. |
psi.coefs |
Esimate(s) of psi coefficient(s). |
p.coefs |
Estimate(s) of p coefficient(s) or the shape parameters if a Beta-Binomial mixture model was specified. |
se.psi.coefs |
Standard Error of the psi coefficient(s). |
se.p.coefs |
Standard Error of the p coefficient(s). |
hessian |
Hessian matrix used to compute the standard error of the psi and p coefficient(s). |
psi.ests |
Psi Estimates corresponding to each site. |
p.ests |
Matrix of P Estimates corresponding to each site and visit. |
aic |
Akaike's information criterion. |
bic |
Bayesian information criterion. |
Be sure to check for convergence. Some tips if you are having trouble getting your models to converge:
1. Choose different starting values.
2. Make sure your covariates are on similar scales.
3. Check for high correlations among covariates.
Currently, this function does not fit visit-specific P-covariates.
Fawn Hornsby, Ryan Nielson, and Trent McDonald www.west-inc.com
Maintainer: Fawn Hornsby fhornsby@west-inc.com
Royle, J.A., 2006. Site occupancy models with heterogeneous detection probabilities. Biometrics 62:97-102.
MacKenzie, D.I., Nichols, J.D., Lachman, G. B., Droege, S., Royle, J. A., and Langtimm, C. A., 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83:2248-2255.
MacKenzie, D.I., Nichols, J.D., Royle, J.A., and Pollock, K.H. (2006), Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Academic Press, Burlington, MA.
weta.data
print.pom
print.mixed.pom
F.2nd.deriv
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(weta.data)
# INTERCEPT-ONLY MODEL
fit1 <- siteocc(~1, ~1, histories=weta.data$detection.histories, start=c(1,1))
# BETA-BINOMIAL MIXTURE MODEL
fit2 <- siteocc(~1, ~Beta.mixture, histories=weta.data$detection.histories,
lower=c(log(0.01),0.0001,0.0001))
# MODEL TESTING BROWSED AND OBSERVER EFFECTS
fit3 <- siteocc(~weta.data$siteCovar$Browsed, ~weta.data$Obs1 + weta.data$Obs2,
histories=weta.data$detection.histories, start=c(0,2,0,-1,0), control=list(iter.max=50))
# MODEL WHICH ALSO FITS A SITE COVARIATE TO THE PROBABILITY OF DETECTION
numvisits=5
p.Browse <- matrix(rep(weta.data$siteCovar$Browsed, numvisits), ncol=numvisits)
fit4 <- siteocc(~1, ~p.Browse + weta.data$Obs1 + weta.data$Obs2,
histories=weta.data$detection.histories)
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