loccit {spatstat.local} | R Documentation |
Locally Fitted Cluster or Cox Point Process Model
Description
Fits a Neyman-Scott cluster process or Cox point process model using a locally-weighted composite likelihood.
Usage
loccit(X, trend = ~1,
clusters = c("Thomas", "MatClust", "Cauchy", "VarGamma", "LGCP"),
covariates = NULL,
...,
diagnostics = FALSE,
taylor = FALSE,
sigma = NULL, f = 1/4,
clustargs = list(), control = list(),
rmax,
covfunargs=NULL, use.gam=FALSE, nd=NULL, eps=NULL,
niter=3,
fftopt = list(),
verbose = TRUE)
Arguments
X |
Point pattern. |
trend |
Formula (without a left hand side) specifying the form of the logarithm of the intensity. |
clusters |
Character string determining the cluster model. Partially matched. |
covariates |
The values of any spatial covariates (other than the Cartesian coordinates) required by the model. A named list of pixel images, functions, windows or numeric constants. |
diagnostics |
Whether to perform auxiliary calculations in addition to the local estimates of the model parameters. |
... |
Additional arguments passed to
|
taylor |
Logical value indicating whether to fit the model
exactly at each spatial location ( |
sigma |
Standard deviation of Gaussian kernel for local likelihood. |
f |
Argument passed to |
clustargs |
List of additional parameters for the cluster model,
passed to the function |
control |
List of control arguments passed to the generic optimisation
function |
rmax |
Maximum distance between pairs of points that will contribute to the composite likelihood. |
covfunargs , use.gam , nd , eps |
Arguments passed to |
niter |
Number of iterations in algorithm if |
fftopt |
Developer use only. |
verbose |
Logical. If |
Details
This function fits a Cox or cluster process model to point pattern data locally, using the local Palm likelihood technique (Baddeley, 2016, section 8).
It can be used in the same way as kppm
and effectively performs local fitting of the same model.
Value
An object of class "loccit"
.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
References
Baddeley, A. (2017) Local composite likelihood for spatial point patterns. Spatial Statistics 22, 261–295. DOI: 10.1016/j.spasta.2017.03.001
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.
See Also
Examples
X <- redwood[owin(c(0,1), c(-1,-1/2))]
fit <- loccit(X, ~1, "Thomas", nd=5, control=list(maxit=20))
fit