detect (version 0.2-2)

databu: Simulated example for abundance model

Description

Simulated example for abundance model, see code below.

Usage

data(databu)

source

Simulated example.

Details

This simulated example corresponds to the Binomial - ZIP model implemented in the function svabu.

References

Solymos, P., Lele, S. R and Bayne, E. 2011. Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error. Environmetrics (in press) Solymos, P., Lele, S. R and Bayne, E. 2011. Abundance estimation in the presence of zero inflation and detection error using single visit data. Alberta Biodiversity Monitoring Institute, Alberta, Canada. Technical Report No. ABMI-20061, August 24, 2011. Available at: http://www.abmi.ca/FileDownloadServlet?filename=20061_ABMI_2011-08-24_Single_Visit_Abundance.pdf&dir=REPORTS_UPLOAD{http://www.abmi.ca}

Examples

data(databu)
str(databu)
## simulation
n <- 1000
set.seed(1234)
x1 <- runif(n,0,1)
x2 <- rnorm(n,0,1)
x3 <- runif(n,-1,1)
x4 <- runif(n,-1,1)
x5 <- rbinom(n,1,0.6)
x6 <- rbinom(n,1,0.4)
x7 <- rnorm(n,0,1)
X <- model.matrix(~ x1 + x5)
Z <- model.matrix(~ x2 + x5)
Q <- model.matrix(~ x7)
beta <- c(2,-0.8,0.5)
theta <- c(1, 2, -0.5)
phi <- 0.3
p <- drop(binomial("logit")$linkinv(Z %*% theta))
lambda <- drop(exp(X %*% beta))
A <- rbinom(n, 1, 1-phi)
N <- rpois(n, lambda * A)
Y <- rbinom(n, N, p)
databu <- data.frame(N=N, Y=Y, x1, x2, x3, x4, x5, x6, p=p, lambda=lambda, A, phi)