fit.void: Fitting a model to a void point process

In b-steve/nspp: Fitting point process models via the Palm likelihood

Description

Estimates parameters for a void point process by maximising the Palm likelihood. This approach was first proposed by Tanaka et al. (2008) for two-dimensional Thomas processes. Generalisation to d-dimensional void processes was made by Jones-Todd (2017).

Usage

fit.void(points, lims, R, edge.correction = "pbc", start = NULL, bounds,
  trace = FALSE)

Arguments

points	A matrix containing locations of observed points, where each row corresponds to a point and each column corresponds to a dimension.
lims	A matrix with two columns, corresponding to the upper and lower limits of each dimension, respectively.
R	Truncation distance for the difference process.
edge.correction	The method used for the correction of edge effects. Either "pbc" for periodic boundary conditions, or "buffer" for a buffer-zone correction.
start	A named vector of starting values for the model parameters.
bounds	A list with named components. Each component should be a vector of length two, giving the upper and lower bounds for the named parameter.
trace	Logical; if TRUE, parameter values are printed to the screen for each iteration of the optimisation procedure.

Details

Parameters to estimate are as follows:

• Dc, the baseline density of observed points.

• Dp, the density of unobserved parents that cause voids.

• tau, the radius of the deletion process centred at each parent.

Value

An R6 reference class object. Extraction of the information held within is best handled by functions coef.nspp, confint.nspp, summary.nspp, and plot.nspp.

References

Tanaka, U., Ogata, Y., and Stoyan, D. (2008) Parameter estimation and model selection for Neyman-Scott point processes. Biometrical Journal, 50: 43–57.

Jones-Todd, C. M. (2017) Modelling complex dependencies inherent in spatial and spatio-temporal point pattern data. PhD thesis, University of St Andrews.

b-steve/nspp documentation built on May 11, 2019, 5:20 p.m.