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> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch [SMTP:r-help-bounces at stat.math.ethz.ch] On Behalf Of ernesto > Sent: Friday, January 13, 2006 9:25 AM > To: Mailing List R > Subject: [R] help with gepRglm::likfit.glsm > > Hi, > > I'm exploring likfit.glsm and I need some help. I have to say that I'm > not an MCMC expert ... > > I did a first run of likfit.glsm with S.scale=0.002 and it worked > whithout problems but there was strong autocorrelation and the chain > convergenc...

Hello, I used glsm.mcmc and likfit.glsm to create model. Now I want to predict at different locations, but I can't get glsm.krige to work. I keep getting the error that trend.d and trend.l must have similar specifications. I have tried numerous ways to include the covariates in the glsm.krige model, and I keep ge...

...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...

...pos.lf0$beta)/(1+exp(OP2003Seppos.lf0$beta))) #P non spatial #[1] 0.9717596 (OP2003Seppos.lf.p<-exp(OP2003Seppos.lf$beta)/(1+exp(OP2003Seppos.lf$beta))) #P spatial #[1] 0.9717596 It must what have an important influence at kriging, because it shows as following: OP2003Sepposbin.krig<-glsm.krige(OP2003Seppos.tune,loc=OP2003Seppospro.pred.grid,bor=OP2003Sepposbor) #glsm.krige: Prediction for a generalised linear spatial model #There are 50 or mode advices (use warnings() to see them) #> warnings() #Warning messages: #1: In asympvar(kpl.result$predict, messages = FALSE) ... : # va...

...oRglm package to run a poisson spatial glm on a dataset with several covariates. When I run without covariates I have no problems. control1.data.geo <- mcmc.control(S.scale=0.2, thin = 1) model1.data.geo <- list(cov.pars = c(1,1), beta=c(1), family="poisson") test1.model1 <- glsm.mcmc(data.geo, model=model1.data.geo, mcmc.input=control1.data.geo) iter. numb. 1000 : Acc.-rate = 0.634 MCMC performed: n.iter. = 1000 ; thinning = 1 ; burn.in = 0 When I try to run model tuning with more than one variable specified in the model using trend spatial I get an issue with...

Dear R users: I want to model mosquito count data based on landcover attributes and meteorological variables using a Poisson GLSM in the geoRglm package. I have monthly mosquito counts over more than 20 years with repeated observations from individual trap sites over time. I have used as.geodata() to successfully read my dataset into the geodata format utilized by geoR and geoRglm, including specification of the realis...

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...03Seppos.lf0$beta)/(1+exp(OP2003Seppos.lf0$beta ))) #P non spatial #[1] 0.9717596 (OP2003Seppos.lf.p<-exp(OP2003Seppos.lf$beta)/(1+exp(OP2003Seppos.lf$beta))) #P spatial #[1] 0.9717596 It must what have an important influence at kriging, because it shows as following: OP2003Sepposbin.krig<-glsm.krige(OP2003Seppos.tune,loc=OP2003Seppospro.pred. grid,bor=OP2003Sepposbor) #glsm.krige: Prediction for a generalised linear spatial model #There are 50 or mode advices (use warnings() to see them) #> warnings() #Warning messages: #1: In asympvar(kpl.result$predict, messages = FALSE) ... : # v...