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
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