'[R-sig-Geo] How do I have to work with Poisson distribution with geoRglm (count data)?'

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List:       r-sig-geo
Subject:    [R-sig-Geo] How do I have to work with Poisson distribution with geoRglm (count data)?
From:       Jimmy Neutron <jimmyjmv () hotmail ! com>
Date:       2015-03-30 21:13:20
Message-ID: BLU182-W2408CB12EA0403AE232543C4F50 () phx ! gbl
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Dear comRades:
Because of my data, I have just realized that I have to work with Poisson, because I \
have 'count data' with geographic reference. Then, my my 'geodata' is such as:  \
xUTMkm   yUTMkm 'Count'1385 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?. When I worked with Binomial model (True, False), my \
script 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, I suppose that my Poisson model would be 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?. Is my Poisson model right?. 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 'Count' do I have in the survey. Thanks in advance for youR help. 		 	   		  

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<body class='hmmessage'><div dir='ltr'><div><div dir="ltr">Dear \
comRades:<div><br></div><div>Because of my data, I have just realized that I have to \
work with Poisson, because I have 'count data' with geographic reference. Then, my my \
'geodata' is such as:</div><div><br></div><div><div>&nbsp; &nbsp; &nbsp; &nbsp;xUTMkm \
&nbsp; yUTMkm 'Count'</div><div>1385 450.1202 1011.425 &nbsp; 0</div><div>1386 \
450.4273 1007.219 &nbsp; 0</div><div>1387 450.2584 1011.884 &nbsp; 0</div><div>1388 \
450.1696 1010.261 &nbsp; 0</div><div>1389 450.1718 1009.887 &nbsp; 0</div><div>1390 \
450.6981 1004.379 &nbsp; 0</div></div><div>...</div><div><br></div><div><span \
style="font-size:12pt;">I read the geoRglm structure. What does it mean that I have \
to make a empiric variogram?.</span></div><div><span \
style="font-size:12pt;"><br></span></div><div><span style="font-size:12pt;">When I \
worked with Binomial model </span>(True, False<span style="font-size: 12pt;">), my \
script was as following:</span></div><div><span \
style="font-size:12pt;">a2008.posCEROS.spmod&lt;-list(cov.pars=c(1,20),beta=1.0,cov.model="matern",nugget=0,kappa=0.35,family="</span>binomial<span \
style="font-size:12pt;">",link="logit")</span></div><div><span \
style="font-size:12pt;"><br></span></div><div><span style="font-size:12pt;">Then, I \
suppose that my Poisson model would be as following:</span></div><div><span \
style="font-size:12pt;">a2008.posCEROS.spmod&lt;-list(cov.pars=c(1,20),beta=1.0,cov.model="matern",nugget=0,kappa=0.35,family="</span>poisson<span \
style="font-size:12pt;">",link="logit")</span></div><div><br></div><div><span \
style="font-size: 12pt;">Do I have to calculate&nbsp;</span><span style="font-size: \
12pt;">nugget and kappa by some method (like likelihood) from my 'count data' or it \
is merely a theoretical model?. Is my&nbsp;</span><span style="font-size: \
12pt;">Poisson model</span><span style="font-size: \
12pt;">&nbsp;right?.</span></div><div><br></div><div>After that, I will generate MCMC \
simulations, such as:</div><div><br></div><div><div>a2008.posCEROS.mcmc&lt;-mcmc.control(S.scale=0.582, \
thin=10) #mcmc marc of change monte carlo, \
S.scale</div><div>a2008.posCEROS.tune&lt;-glsm.mcmc(a2008.posCEROSbin, \
model=a2008.posCEROS.spmod, \
mcmc.input=a2008.posCEROS.mcmc)</div></div><div><br></div><div>My goal is to predict \
how many 'Count' do I have in the survey.</div><div><br></div><div>Thanks in advance \
                for youR help.</div></div></div><style><!--
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