databu | R Documentation |
Simulated example for abundance model, see code below.
data(databu)
A data frame with 1000 observations on the following 11 variables.
N
true counts
Y
observed counts
x1
random variables used as covariates
x2
random variables used as covariates
x3
random variables used as covariates
x4
random variables used as covariates
x5
random variables used as covariates
x6
random variables used as covariates
p
probability of detection
lambda
mean of the linear predictor
A
occupancy
phi
zero inflation probabilities
This simulated example corresponds to the Binomial - ZIP model implemented in the function svabu
.
Simulated example.
Solymos, P., Lele, S. R. and Bayne, E. 2012. Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error. Environmetrics, 23, 197–205. <doi:10.1002/env.1149>
data(databu)
str(databu)
## Not run:
## 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)
## End(Not run)
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