dppapproxkernel

Object of class <code>"detpointprocfamily"</code>.

model

Numeric specifying how the model truncation is
 performed. See Details section of <code><a rd-options="" href="/link/simulate.detpointprocfamily?package=spatstat.core&version=2.3-1" data-mini-rdoc="spatstat.core::simulate.detpointprocfamily">simulate.detpointprocfamily</a></code>.

trunc

Optional window -- undocumented at the moment.

Returns an approximation to the kernel of a determinantal
 point process, as a function of one argument \(x\).

Functionality for data analysis and modelling of
spatial data, mainly spatial point patterns,
in the 'spatstat' family of packages.
(Excludes analysis of spatial data on a linear network,
which is covered by the separate package 'spatstat.linnet'.)
Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods.
A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above.
Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

Adrian Baddeley

spatstat.core

Core Functionality of the 'spatstat' Family

Rolf Turner

Ege Rubak

Kasper Klitgaard Berthelsen

Achmad Choiruddin

Jean-Francois Coeurjolly

Ottmar Cronie

Tilman Davies

Chiara Fend

Julian Gilbey

Yongtao Guan

Ute Hahn

Kassel Hingee

Abdollah Jalilian

Marie-Colette van Lieshout

Greg McSwiggan

Tuomas Rajala

Suman Rakshit

Dominic Schuhmacher

Rasmus Plenge Waagepetersen

Hangsheng Wang

dppapproxkernel function

Numeric specifying how the model truncation is
 performed. See Details section of <code><a rd-options='' href='simulate.detpointprocfamily'>simulate.detpointprocfamily</a></code>.

Approximate Determinantal Point Process Kernel — dppapproxkernel

Approximate Determinantal Point Process Kernel

dppapproxkernel: Approximate Determinantal Point Process Kernel

Description

Usage

Arguments

Value