confint.SDALGCP

an object of class "SDALGCP" 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

a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.

parm

level

the number of decimal places for the result

additional argument(s) for methods.

Computes confidence intervals for one or more parameters in a fitted SDALGCP model from the object of class "SDALGCP", based on asymptotic normality.

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

confint.SDALGCP function

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

confint.SDALGCP: Confidence Intervals for SDALGCP Model Parameters

Description

Usage

Arguments

Value

See Also