thetaEst: thetaEst function

In lgcp: Log-Gaussian Cox Process

thetaEst function

Description

A tool to visually estimate the temporal correlation parameter theta; note that sigma and phi must have first been estiamted.

Usage

thetaEst(
  xyt,
  spatial.intensity = NULL,
  temporal.intensity = NULL,
  sigma,
  phi,
  theta.range = c(0, 10),
  N = 100,
  spatial.covmodel = "exponential",
  covpars = c()
)

Arguments

xyt	object of class stppp
spatial.intensity	A spatial at risk object OR a bivariate density estimate of lambda, an object of class im (produced from density.ppp for example),
temporal.intensity	either an object of class temporalAtRisk, or one that can be coerced into that form. If NULL (default), this is estimated from the data, seee ?muEst
sigma	estimate of parameter sigma
phi	estimate of parameter phi
theta.range	range of theta values to consider
N	number of integration points in computation of C(v,beta) (see Brix and Diggle 2003, corrigendum to Brix and Diggle 2001)
spatial.covmodel	spatial covariance model
covpars	additional covariance parameters

Value

An r panel tool for visual estimation of temporal parameter theta NOTE if lambdaEst has been invoked to estimate lambda, then the returned density should be passed to thetaEst as the argument spatial.intensity

References

1. 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/

2. Brix A, Diggle PJ (2001). Spatiotemporal Prediction for log-Gaussian Cox processes. Journal of the Royal Statistical Society, Series B, 63(4), 823-841.

3. Diggle P, Rowlingson B, Su T (2005). Point Process Methodology for On-line Spatio-temporal Disease Surveillance. Environmetrics, 16(5), 423-434.

See Also

ginhomAverage, KinhomAverage, spatialparsEst, lambdaEst, muEst

lgcp documentation built on Oct. 3, 2023, 5:08 p.m.