svocc {detect} R Documentation

## ZI Binomial model with single visit

### Description

ZI Binomial model with single visit

### Usage

svocc(formula, data, link.sta = "cloglog", link.det = "logit",
penalized = FALSE, method = c("optim", "dc"), inits,
model = TRUE, x = FALSE, ...)
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, ...)


### Arguments

 formula formula of the form y ~ x | z, where y is a vector of observations, x is the set of covariates for the occurrence model, z is the set of covariates for the detection model 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

### Details

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.

### Value

An object of class 'svocc'.

### Author(s)

Peter Solymos and Monica Moreno

### References

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>

### Examples

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)