Dear R users. I'm trying to fit a generalised linear spatial mode using the geoRglm package. To do so, I'm preparing my data (geodata) as follow: geoData9093 = as.geodata(data9093, coords.col= 17:18, data.col=15,* covar.col=16*) where covar.col is a factor variable (years in this case 90-91-92-93)). Then I run the model as follow: / model.5 = list(cov.pars=c(1,1), cov.model='exponential', beta=1, family="poisson") mcmc.5 = mcmc.control(S.scale = 0.25, n.iter = 30000, burn.in=50000, thin 100) #trial error outmcmc.5 = glsm.mcmc(geoData9093, model= model.5, mcmc.input = mcmc.5) mcmcobj.5 = prepare.likfit.glsm(outmcmc.5) lik.5 = likfit.glsm(mcmcobj.5, ini.phi = 0.3, fix.nugget.rel = F)/ And the summary of lik.5 is: likfit.glsm: estimated model parameters: beta sigmasq phi tausq.rel "1.2781" "0.5193" "0.0977" "0.0069" likfit.glsm : maximised log-likelihood = 43.62 I'm fairly new to geostatistics, but I thought using a factor variable as covariable would give me 4 intercepts (beta) as I have 4 levels in my covar. But looking at the summary, we see that I only have 1 beta which is equal to 1.28. I guess I made mistakes in specifying the model description, but I can't find where. Any advices would be welcome. With regards, Phil -- View this message in context: http://r.789695.n4.nabble.com/geoRglm-with-factor-variable-as-covariable-tp4645067.html Sent from the R help mailing list archive at Nabble.com.

On 10/4/2012 10:39 PM, Filoche wrote:> Dear R users. > > I'm trying to fit a generalised linear spatial mode using the geoRglm > package. To do so, I'm preparing my data (geodata) as follow: > > geoData9093 = as.geodata(data9093, coords.col= 17:18, data.col=15,* > covar.col=16*) > > where covar.col is a factor variable (years in this case 90-91-92-93)). > > Then I run the model as follow: > / > model.5 = list(cov.pars=c(1,1), cov.model='exponential', beta=1, > family="poisson") > mcmc.5 = mcmc.control(S.scale = 0.25, n.iter = 30000, burn.in=50000, thin > 100) #trial error > outmcmc.5 = glsm.mcmc(geoData9093, model= model.5, mcmc.input = mcmc.5) > mcmcobj.5 = prepare.likfit.glsm(outmcmc.5) > lik.5 = likfit.glsm(mcmcobj.5, ini.phi = 0.3, fix.nugget.rel = F)/ > > And the summary of lik.5 is: > > likfit.glsm: estimated model parameters: > beta sigmasq phi tausq.rel > "1.2781" "0.5193" "0.0977" "0.0069" > > likfit.glsm : maximised log-likelihood = 43.62 > > I'm fairly new to geostatistics, but I thought using a factor variable as > covariable would give me 4 intercepts (beta) as I have 4 levels in my covar. > But looking at the summary, we see that I only have 1 beta which is equal to > 1.28. I guess I made mistakes in specifying the model description, but I > can't find where. Any advices would be welcome. > > With regards, > PhilYou may have covariates in your data but your model (model.5) is set up as a model without covariates. You put beta=1, thus, the model is a constant. HTH Ruben -- Ruben H. Roa-Ureta, Ph. D. Senior Scientist Marine Studies Section, Center for Environment and Water, Research Institute, King Fahd University of Petroleum and Minerals, KFUPM Box 1927, Dhahran 31261, Saudi Arabia Office Phone : 966-3-860-7850 Cellular Phone : 966-5-61151014 Save a tree. Don't print this e-mail unless it's really necessary

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