dppkernel

Returns the kernel of a determinantal point process model as a
 function of one argument <code>x</code>.

models

spatial

Functionality for parametric statistical modelling and inference for 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'.)
Supports parametric modelling, formal statistical inference, and model validation.
Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Adrian Baddeley

spatstat.model

Parametric Statistical Modelling and Inference for the
'spatstat' Family

Rolf Turner

Ege Rubak

Kasper Klitgaard Berthelsen

Achmad Choiruddin

Jean-Francois Coeurjolly

Ottmar Cronie

Tilman Davies

Julian Gilbey

Yongtao Guan

Ute Hahn

Martin Hazelton

Kassel Hingee

Abdollah Jalilian

Frederic Lavancier

Marie-Colette van Lieshout

Bethany Macdonald

Greg McSwiggan

Tuomas Rajala

Suman Rakshit

Dominic Schuhmacher

Rasmus Plenge Waagepetersen

Hangsheng Wang

dppkernel function

<dl> <dt>model</dt>
<dd>Model of class <code>"detpointprocfamily"</code>.</dd> <dt>...</dt>
<dd>Arguments passed to <code>dppapproxkernel</code> if the exact kernel is unknown</dd></dl>

Arguments

Adrian Baddeley <a href="/link/Adrian.Baddeley%40curtin.edu.au?package=spatstat.model&version=3.3-5" data-mini-rdoc="spatstat.model::Adrian.Baddeley@curtin.edu.au">Adrian.Baddeley@curtin.edu.au</a> 
 
Rolf Turner <a href="/link/rolfturner%40posteo.net?package=spatstat.model&version=3.3-5" data-mini-rdoc="spatstat.model::rolfturner@posteo.net">rolfturner@posteo.net</a> 
and Ege Rubak <a href="/link/rubak%40math.aau.dk?package=spatstat.model&version=3.3-5" data-mini-rdoc="spatstat.model::rubak@math.aau.dk">rubak@math.aau.dk</a>

Author

Extract Kernel from Determinantal Point Process Model Object — dppkernel

<dl>

 <dt>model</dt>
<dd>Model of class <code>"detpointprocfamily"</code>.</dd>

 <dt>...</dt>
<dd>Arguments passed to <code>dppapproxkernel</code> if the exact kernel is unknown</dd>

</dl>

Adrian Baddeley <a href='mailto:Adrian.Baddeley@curtin.edu.au'>Adrian.Baddeley@curtin.edu.au</a> 
 
Rolf Turner <a href='mailto:rolfturner@posteo.net'>rolfturner@posteo.net</a> 
and Ege Rubak <a href='mailto:rubak@math.aau.dk'>rubak@math.aau.dk</a>

Extract Kernel from Determinantal Point Process Model Object

dppkernel: Extract Kernel from Determinantal Point Process Model Object

Description

Usage

Value

Arguments

Author

Examples