sepstlppm

Fit a separable spatio-temporal Poisson process model on a linear network

Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers (Siino, Adelfio, and Mateu 2018<doi:10.1007/s00477-018-1579-0>; Siino et al. 2018<doi:10.1002/env.2463>; Adelfio et al. 2020<doi:10.1007/s00477-019-01748-1>; D’Angelo, Adelfio, and Mateu 2021<doi:10.1016/j.spasta.2021.100534>; D’Angelo, Adelfio, and Mateu 2022<doi:10.1007/s00362-022-01338-4>; D’Angelo, Adelfio, and Mateu 2023<doi:10.1016/j.csda.2022.107679>). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks.

Nicoletta D'Angelo

stopp

Spatio-Temporal Point Pattern Methods, Model Fitting,
Diagnostics, Simulation, Local Tests

Giada Adelfio

sepstlppm function

<dl><dt>x</dt>
<dd>A <code>stlpm</code> object</dd>
<dt>spaceformula</dt>
<dd>A formula for the spatial component. See lppm for details</dd>
<dt>timeformula</dt>
<dd>A formula for the temporal component. It fits a log-linear model with the <a href="/link/glm?package=stopp&version=0.2.3" data-mini-rdoc="stopp::glm">glm</a> function</dd></dl>

Arguments

Fit a separable spatio-temporal Poisson process model on a linear network — sepstlppm

<dl>

<dt>x</dt>
<dd>A <code>stlpm</code> object</dd>


<dt>spaceformula</dt>
<dd>A formula for the spatial component. See lppm for details</dd>


<dt>timeformula</dt>
<dd>A formula for the temporal component. It fits a log-linear model with the <a href='https://rdrr.io/r/stats/glm.html'>glm</a> function</dd>

</dl>

sepstlppm: Fit a separable spatio-temporal Poisson process model on a linear network

Description

Usage

Value

Arguments

Examples