datocc: Simulated example for occupancy model

In detect: Analyzing Wildlife Data with Detection Error

Description

Simulated example for occupancy model, see code below.

Usage

data(datocc)

Format

A data frame with 1000 observations on the following 6 variables.

Y

true occupancy

W

observations

x1

random variables used as covariates

x2

random variables used as covariates

x3

random variables used as covariates

x4

random variables used as covariates

p.occ

probability of occurrence

p.det

probability of detection

Details

This simulated example corresponds to the ZI Binomial model implemented in the function svocc.

Source

Simulated example.

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.

Examples

data(datocc)
str(datocc)
## Not run: 
## simulation
n <- 1000
set.seed(1234)
x1 <- runif(n, -1, 1)
x2 <- as.factor(rbinom(n, 1, 0.5))
x3 <- rnorm(n)
x4 <- rnorm(n)
beta <- c(0.6, 0.5)
theta <- c(0.4, -0.5, 0.3)
X <- model.matrix(~ x1)
Z <- model.matrix(~ x1 + x3)
mu <- drop(X %*% beta)
nu <- drop(Z %*% theta)
p.occ <- binomial("cloglog")$linkinv(mu)
p.det <- binomial("logit")$linkinv(nu)
Y <- rbinom(n, 1, p.occ)
W <- rbinom(n, 1, Y * p.det)
datocc <- data.frame(Y, W, x1, x2, x3, x4, p.occ, p.det)

## End(Not run)

Example output

Loading required package: Formula
Loading required package: stats4
Loading required package: pbapply
detect 0.4-2 	 2018-08-29
'data.frame':	1000 obs. of  8 variables:
 $ Y    : num  1 1 1 1 1 1 0 0 0 1 ...
 $ W    : num  1 0 0 0 1 1 0 0 0 1 ...
 $ x1   : num  -0.773 0.245 0.219 0.247 0.722 ...
 $ x2   : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 1 2 2 ...
 $ x3   : num  -1.205 0.301 -1.539 0.635 0.703 ...
 $ x4   : num  -0.9738 -0.0996 -0.1107 1.1922 -1.6559 ...
 $ p.occ: num  0.71 0.872 0.869 0.873 0.927 ...
 $ p.det: num  0.605 0.591 0.457 0.615 0.562 ...

detect documentation built on May 2, 2019, 4:50 p.m.