svocc | R Documentation |
ZI Binomial model with single visit
svocc(formula, data, link.sta = "cloglog", link.det = "logit", penalized = FALSE, method = c("optim", "dc"), inits, model = TRUE, x = FALSE, ...) svocc.fit(Y, X, Z, link.sta = "cloglog", link.det = "logit", penalized = FALSE, auc = FALSE, method = c("optim", "dc"), inits, ...) extractMLE(object, ...) svocc.step(object, model, trace = 1, steps = 1000, criter = c("AIC", "BIC", "cAUC"), test = FALSE, k = 2, control, ...)
formula |
formula of the form |
Y, X, Z |
vector of observation, design matrix for occurrence model, and design matrix for detection model |
data |
data |
link.sta, link.det |
link function for the occurrence (true state) and detection model |
penalized |
logical, if penalized likelihood estimate should be computed |
method |
optimization or data cloning to be used as optimization |
inits |
initial values |
model |
a logical value indicating whether model frame should be included as a component of the returned value, or true state or detection model |
x |
logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value |
auc |
logical, if AUC should be calculated |
object |
a fitted model object |
trace |
info returned during the procedure |
steps |
max number of steps |
criter |
criterion to be minimized (cAUC=1-AUC) |
test |
logical, if decrease in deviance should be tested |
k |
penalty to be used with AIC |
control |
controls for optimization, if missing taken from object |
... |
other arguments passed to the functions |
See Examples.
The right hand side of the formula must contain at least one continuous (i.e. non discrete/categorical) covariate. This is the necessary condition for the single-visit method to be valid and parameters to be identifiable. See References for more detailed description.
An object of class 'svocc'.
Peter Solymos and Monica Moreno
Lele, S.R., Moreno, M. and Bayne, E. 2011. Dealing with detection error in site occupancy surveys: What can we do with a single survey? Journal of Plant Ecology, 5(1), 22–31. <doi:10.1093/jpe/rtr042>
Moreno, M. and Lele, S. R. 2010. Improved estimation of site occupancy using penalized likelihood. Ecology, 91, 341–346. <doi:10.1890/09-1073.1>
Solymos, P., Lele, S. R. 2016. Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods in Ecology and Evolution, 7, 196–205. <doi:10.1111/2041-210X.12432>
data(datocc) ## MLE m00 <- svocc(W ~ x1 | x1 + x3, datocc) ## PMLE m01 <- svocc(W ~ x1 | x1 + x3, datocc, penalized=TRUE) ## print m00 ## summary summary(m00) ## coefficients coef(m00) ## state (occupancy) model estimates coef(m00, "sta") ## detection model estimates coef(m00, "det") ## compare estimates cbind(truth=c(0.6, 0.5, 0.4, -0.5, 0.3), mle=coef(m00), pmle=coef(m01)) ## AIC, BIC AIC(m00) BIC(m00) ## log-likelihood logLik(m00) ## variance-covariance matrix vcov(m00) vcov(m00, model="sta") vcov(m00, model="det") ## confidence intervals confint(m00) confint(m00, model="sta") confint(m00, model="det") ## fitted values ## (conditional probability of occurrence given detection history: ## if W=1, fitted=1, ## if W=0, fitted=(phi*(1-delta)) / ((1-delta) + phi * (1-delta)) summary(fitted(m00)) ## estimated probabilities: (phi*(1-delta)) / ((1-delta) + phi * (1-delta)) summary(m00$estimated.probabilities) ## probability of occurrence (phi) summary(m00$occurrence.probabilities) ## probability of detection (delta) summary(m00$detection.probabilities) ## Not run: ## model selection m02 <- svocc(W ~ x1 | x3 + x4, datocc) m03 <- drop1(m02, model="det") ## dropping one term at a time, resulting change in AIC m03 ## updating the model m04 <- update(m02, . ~ . | . - x4) m04 ## automatic model selection ## part of the model (sta/det) must be specified m05 <- svocc.step(m02, model="det") summary(m05) ## nonparametric bootstrap m06 <- bootstrap(m01, B=25) attr(m06, "bootstrap") extractBOOT(m06) summary(m06, type="mle") summary(m06, type="pmle") ## no SEs! PMLE!!! summary(m06, type="boot") ## vcov #vcov(m06, type="mle") ## this does not work with PMLE vcov(m06, type="boot") ## this works ## confint confint(m06, type="boot") ## quantile based ## parametric bootstrap ## sthis is how observations are simulated head(simulate(m01, 5)) m07 <- bootstrap(m01, B=25, type="param") extractBOOT(m07) summary(m07) data(oven) ovenc <- oven ovenc[, c(4:8,10:11)][] <- lapply(ovenc[, c(4:8,10:11)], scale) ovenc$count01 <- ifelse(ovenc$count > 0, 1, 0) moven <- svocc(count01 ~ pforest | julian + timeday, ovenc) summary(moven) drop1(moven, model="det") moven2 <- update(moven, . ~ . | . - timeday) summary(moven) BIC(moven, moven2) AUC(moven, moven2) rocplot(moven) rocplot(moven2, col=2, add=TRUE) ## End(Not run)
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