[R-sig-Geo] How to work with Poisson distribution (count data)?

[Jimmy Neutron]

Dear comRades:
I realized that I have to work with Poisson, because I have 'count data' with geographic reference. Then, it is 'geodata', such as:
       xUTMkm   yUTMkm DyF1385 450.1202 1011.425   01386 450.4273 1007.219   01387 450.2584 1011.884   01388 450.1696 1010.261   01389 450.1718 1009.887   01390 450.6981 1004.379   0...
I read the geoRglm structure. What does it mean that I have to make a empiric variogram?.
What I used when I worked with Binomial model was as following:a2008.posCEROS.spmod<-list(cov.pars=c(1,20),beta=1.0,cov.model="matern",nugget=0,kappa=0.35,family="binomial",link="logit")
Then, is it right to write my Poisson model as following?:a2008.posCEROS.spmod<-list(cov.pars=c(1,20),beta=1.0,cov.model="matern",nugget=0,kappa=0.35,family="poisson",link="logit")
Do I have to calculate nugget and kappa by some method (like likelihood) from my 'count data' or it is merely a theoretical model?.
After that, I will generate MCMC simulations, such as:
a2008.posCEROS.mcmc<-mcmc.control(S.scale=0.582, thin=10) #mcmc marc of change monte carlo, S.scalea2008.posCEROS.tune<-glsm.mcmc(a2008.posCEROSbin, model=a2008.posCEROS.spmod, mcmc.input=a2008.posCEROS.mcmc)
My goal is to predict how many DyF do I have in the survey.
Thanks in advance for youR help. 		 	   		  
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