MatClust.lppm: Fit a Matern cluster point process to a point pattern dataset...

In myllym/GET: Global Envelopes

Fit a Matern cluster point process to a point pattern dataset on a linear network

Description

Fit a Matern cluster point process to a point pattern dataset on a linear network. This function is provided in GET to support hotspots.MatClustlpp. See the hotspots vignette available by starting R, typing library("GET") and vignette("GET").

Usage

MatClust.lppm(
  PP,
  formula,
  subwin = NULL,
  valpha,
  vR,
  data,
  nsim = 10,
  ncores = 1L
)

Arguments

PP	Input, a point pattern object (ppp) of spatstat.
formula	An R formula to estimate the first order model. This formula can contain objects of full size. PP should be on the left side of the formula.
subwin	A part of the observation window of PP to be used for estimating the second order structure. NULL means that the full point pattern is used. Typically this is feasible (not too time consuming).
valpha	A vector of parameter values for the parameter alpha of the Matern cluster process.
vR	A vector of parameter values for the parameter R of the Matern cluster process.
data	Data from where the formula takes objects. Must be acceptable by the function lppm of spatstat.linnet.
nsim	The number of simulated Matern cluster point patterns for evaluating the K-function for any alpha and R values.
ncores	Number of cores used for computations. Default to 1. If NULL, all available cores are used.

Details

The function MatClust.lppm, can be used to estimate the Matern cluster point pattern with inhomogeneous cluster centers on linear network. This function provides the same outputs as the pois.lppm and further estimated parameters alpha and R. The secondorder provides again the diagnostics for checking if the clustered model is appropriate. The sample K-function must be close to the K-function of the estimated model (green line). If it is not the case the searching grid for parameters alpha and R that is input in the function must be manipulated to get the a closer result. If the estimated model is adequate one can proceed to the hotspot detection with the use of the function hotspots.MatClustlpp. Remark here, that for the estimation of the second order structure a smaller data can be used than for the estimation of the first order structure in order to save the computation time, since the second order is a local characteristics. This smaller window can be specified by subwin. Then the full point pattern will be used for estimation of first order intensity and the pattern in subwindow will be used for estimating second order characteristic. The input parameters are the same as in pois.lppm. Furthermore, valpha, i.e., vector of proposed alphas which should be considered in the optimization, vR, i.e., vector of proposed values for R which should be considered in the optimization, must be provided. The user can also specify how many cores should be used in the computation by parameter ncores.

myllym/GET documentation built on July 3, 2025, 8:48 p.m.