summary.SDALGCPST

an object of class "SDALGCPST" obtained as result of a call to <code><a rd-options="" href="/link/SDALGCPMCML?package=SDALGCP&version=0.4.0" data-mini-rdoc="SDALGCP::SDALGCPMCML">SDALGCPMCML</a></code> .

object

further arguments passed to or from other methods.

<code>summary</code> method for the class "SDALGCPST" that computes the standard errors and p-values of SDALGCPST.

Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset. The details of the methods are provided in Johnson et al (2019) <doi:10.1002/sim.8339>.

Olatunji Johnson

SDALGCP

Spatially Discrete Approximation to Log-Gaussian Cox Processes
for Aggregated Disease Count Data

Emanuele Giorgi

Peter Diggle

summary.SDALGCPST function

an object of class "SDALGCPST" obtained as result of a call to <code><a rd-options='' href='SDALGCPMCML'>SDALGCPMCML</a></code> .

summary.SDALGCPST: Summarizing the parameter estimates of SDALGCP model

Description

Usage

Arguments

Value