spatialparsEst: spatialparsEst function
In bentaylor1/lgcp: Log-Gaussian Cox Process

Description Usage Arguments Details Value References See Also

Having estimated either the pair correlation or K functions using respectively ginhomAverage or KinhomAverage, the spatial parameters sigma and phi can be estimated. This function provides a visual tool for this estimation procedure.

1 2	spatialparsEst(gk, sigma.range, phi.range, spatial.covmodel, covpars = c(), guess = FALSE)

`gk`	an R object; output from the function KinhomAverage or ginhomAverage
`sigma.range`	range of sigma values to consider
`phi.range`	range of phi values to consider
`spatial.covmodel`	correlation type see ?CovarianceFct
`covpars`	vector of additional parameters for certain classes of covariance function (eg Matern), these must be supplied in the order given in ?CovarianceFct
`guess`	logical. Perform an initial guess at paramters? Alternative (the default) sets initial values in the middle of sigma.range and phi.range. NOTE: automatic parameter estimation can be can be unreliable.

To get a good choice of parameters, it is likely that the routine will have to be called several times in order to refine the choice of sigma.range and phi.range.

rpanel function to help choose sigma nad phi by eye

Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2013). Journal of Statistical Software, 52(4), 1-40. URL http://www.jstatsoft.org/v52/i04/
Baddeley AJ, Moller J, Waagepetersen R (2000). Non-and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54, 329-350.
Brix A, Diggle PJ (2001). Spatiotemporal Prediction for log-Gaussian Cox processes. Journal of the Royal Statistical Society, Series B, 63(4), 823-841.
Diggle P, Rowlingson B, Su T (2005). Point Process Methodology for On-line Spatio-temporal Disease Surveillance. Environmetrics, 16(5), 423-434.

ginhomAverage, KinhomAverage, thetaEst, lambdaEst, muEst

bentaylor1/lgcp documentation built on May 12, 2019, 2:09 p.m.