Dear list,
I'm using geoRglm with the log link function and a number of covariates. I'm having convergence and mixing problems with my MCM algorithms and my supervisors and I have come up with a few suggestions for what the problem could be (I'm a novice geostatistician and R-user). I was wondering if someone could perhaps answer a couple of general questions about one theory? Firstly, if the covariance model specified in model.glm.control does not fit the data adequately is this likely to result in convergence/mixing problems? And secondly, what impact could this have on the model results? My supervisor suggested trying to use only the short lags in model fitting as the tail of my variogram does not fit well to the specified covariance function - is this possible in geoRglm? Could I use only lags up to 30km and discard lags from 30 to 60km (max lag is about 60km in my dataset)? My data shows a 'hole-effect' so the variogram rises, and then drops again, but I have been using the spherical covariance function in my model fitting which fits my data up to the peak, but not after. So far I have been unable to find a better fitting line for my data using lines.variomodel. I'm not sure if this is useful or not, but I have included my code below (nicola1 is the dataset and MARKET is a covariate). Any advice or suggestions would be appreciated, Many thanks, Nicola varA<-variog(nicola1, trend=~MARKET, max.dist=30,uvec=15) plot(varA) lines.variomodel(cov.model="sph", cov.pars = c(1.2, 20), nug=0.8, max.dist=30) #Set a random seed set.seed(548) #Set the prior control PriA<-prior.glm.control(beta.prior="flat", sigmasq.prior="uniform", phi.prior="fixed", phi =20, tausq.rel=1) #Set the MCMC control McA<-mcmc.control(S.scale=0.03, phi.scale=0.36, burn.in=100000, thin=50, n.iter=100000) #Set the model control ModA<-model.glm.control(trend.d=trend.spatial(~MARKET, nicola1), cov.model="spherical", lambda=0) #Set the output control OutA<-output.glm.control(sim.posterior=TRUE, keep.mcmc.sim=TRUE, quantile=TRUE, inference=TRUE) #Run the model with inputs (prior control, MCMC control, trend specification, model control, and output control) testA<-binom.krige.bayes(nicola1, prior=PriA, model=ModA, mcmc.input=McA, output=OutA) save(testA, file="test1")
The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.
_______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
