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,

> Phil

You 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,

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