phiCI

object of class "SDALGCP" from the the call to function <a rd-options="" href="/link/SDALGCPMCML?package=SDALGCP&version=0.4.0" data-mini-rdoc="SDALGCP::SDALGCPMCML">SDALGCPMCML</a>

the coverage probability, default is 0.95

coverage

logical, to plot the deviance curve. default is TRUE

plot

This function computes the confidence interval of phi

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

phiCI function

object of class "SDALGCP" from the the call to function <a rd-options='' href='SDALGCPMCML'>SDALGCPMCML</a>

phiCI: Plot of the deviance to derive the confidence interval of of the scale parameter, phi

Description

Usage

Arguments

Value

Details

See Also