simHDSg {AHMbook} R Documentation

Simulate data under HDS protocol with groups

Description

Simulates hierarchical distance sampling (HDS) data for groups under either a line or a point transect protocol and using a half-normal detection function (Buckland et al. 2001).

At each site, it works with a strip of width B*2 (for line transects) or a circle of radius B (for point transects).

The state process is simulated by first drawing a covariate value, "habitat", for each site from a Normal(0, 1) distribution. This is used in a log-linear regression with arguments beta0 and beta1 to calculate the expected number of groups in each strip or circle. Group size is simulated by first drawing from a Poisson distribution with parameter lambda.group then adding 1.

For line transects, the distance from the line is drawn from a Uniform(0, B) distribution. For point transects, the distance from the point is simulated from B*sqrt(Uniform(0,1)), which ensures a uniform distribution over the circle.

The group size is used in a log-linear regression with arguments alpha0 and alpha1 to calculate the scale parameter, sigma, of the half-normal detection function. Detections are simulated as Bernoulli trials with probability of success decreasing with distance from the line or point.

Usage

simHDSg(type = c("line", "point"), nsites = 100, lambda.group = 0.75,
alpha0 = 0, alpha1 = 0.5,
beta0 = 1, beta1 = 0.5, B = 4, discard0 = TRUE, show.plot = TRUE)

Arguments

 type The type of distance transect, either "line" or "point". nsites Number of sites (spatial replication) lambda.group Poisson mean of group size alpha0 intercept of log-linear model relating sigma of the half-normal detection function to group size alpha1 slope of log-linear model relating sigma of the half-normal detection function to group size beta0 intercept of log-linear model relating the Poisson mean of the number of groups per unit area to habitat beta1 slope of log-linear model relating the Poisson mean of the number of groups per unit area to habitat B strip half width or the radius of the circle discard0 whether to discard or keep the data from sites with nobody detected show.plot choose whether to show plots or not. Set to FALSE when using function in simulations.

Value

A list with the values of the arguments entered and the following additional elements:

 data simulated distance sampling data: a matrix with a row for each group detected and 6 columns: site ID, status (1 if captured), x and y coordinates (NA for line transects), distance from the line or point, group size; if discard0 = FALSE, sites with no detections will appear in the matrix with NAs in columns 2 to 6. habitat simulated habitat covariate N simulated number of groups at each site N.true for point counts, the simulated number of groups within the circle sampled groupsize group size for each of the groups observed

Author(s)

Marc Kéry & Andy Royle

References

Buckland, S.T., et al (2001) Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, Oxford, UK.

Kéry, M. & Royle, J.A. (2016) Applied Hierarchical Modeling in Ecology AHM1 - 9.2.1.

Examples

# Run with the default arguments and look at the structure of the output:
set.seed(123)
tmp <- simHDSg()
str(tmp)