palm (version 1.1.5)

sim.ns: Simulating points from a Neyman-Scott point process

Description

Generates points from a Neyman-Scott point process using parameters provided by the user.

Usage

sim.ns(
  pars,
  lims,
  disp = "gaussian",
  child.dist = "pois",
  parents = NULL,
  child.info = NULL
)

Value

A list. The first component gives the Cartesian coordinates of the generated points. The second component returns the parent locations. A third component may provide sibling information.

Arguments

pars

A named vector containing the values of the parameters of the process that generates the points.

lims

A matrix or list of matrices with two columns, corresponding to the upper and lower limits of each dimension, respectively. If a list, then each matrix provides the limits for the corresponding pattern in points.

disp

A character string indicating the distribution of children around their parents. Use "gaussian" for multivariate normal dispersion with standard deviation sigma, or "uniform" for uniform dispersion within distance tau of the parent.

child.dist

The distribution of the number of children generated by a randomly selected parent. For a Poisson distribution, use "pois"; for a binomial distribution, use "binomx", where "x" is replaced by the fixed value of the number of independent trials (e.g., "binom5" for a Binomial(5, p) distribution, and "binom50" for a Binomial(50, p) distribution); and "twocamera" for a child distribution appropriate for a two-camera aerial survey.

parents

An optional matrix containing locations of parents. If this is provided, then the parameter D is not required in pars. If this is not provided, then parents are generated from a homogeneous Poisson point process with intensity D.

child.info

A list of further information that is required about the distribution for the number of children generated by parents. See `Details'.

Details

For a list of possible parameter names, see fit.ns.

The "child.info" argument is required when child.dist is set to "twocamera". It must be a list that comprises (i) a component named w, providing the halfwidth of the detection zone; (ii) a component named b, providing the halfwidth of the survey area; (iii) a component named l, providing the time lag between cameras (in seconds); and (iv) a component named tau, providing the mean dive-cycle duration. See Stevenson, Borchers, and Fewster (2019) for details.

References

Stevenson, B. C., Borchers, D. L., and Fewster, R. M. (2019) Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. Biometrics, 75: 326--336.

Examples

## Simulating from a one-dimensional Thomas process.
data.thomas <- sim.ns(c(D = 10, lambda = 5, sigma = 0.025), lims = rbind(c(0, 1)))
## Simulating from a three-dimensional Matern process.
data.matern <- sim.ns(c(D = 10, lambda = 10, tau = 0.1), disp = "uniform",
                      lims = rbind(c(0, 1), c(0, 2), c(0, 3)))