Olá pessoal estou fazendo uma modelagem geoestatística pelo INLA, mas estou com dúvidas quanto às estimativas para os intervalos de credibilidade maiores, p.ex. 95%, para esta situação os valores estimados fogem do campo amostral que é de 0,3 a 0,7. Alguém sabe onde posso configurar para que as estimativas fiquem nesse intervalo. Segue o código: ## Criando domain IEBdomain <- inla.nonconvex.hull(as.matrix(dados[,1:2]), -0.03, -0.05, resolution=c(100,100)) ## Crando mesh IEBmesh <- inla.mesh.2d(boundary=IEBdomain, max.edge=c(35,35), cutoff=35, offset=c(-0.5, -0.5)) plot(IEBmesh, asp=1, main='') ## spde matern 0.5 = exponetial IEBspde <- inla.spde2.matern(mesh=IEBmesh,alpha=2) mesh.index <- inla.spde.make.index(name = "i", n.spde = IEBspde$n.spde) ## Matriz projetora estimativa A.est <- inla.spde.make.A(IEBmesh, loc=as.matrix(dados[,1:2])) ## Matriz de covariaveis selecionadas pelo AIC, estatistica frequentista covars <- dados[,c(1:4,6:23)] stk.est <- inla.stack(data=list(y=dados$IEB_ANO), A=list(A.est,1), tag="est", effects=list(c(mesh.index,list(Intercept=1)), list(covars))) stk.val <- inla.stack(data=list(y=NA), A=list(A.est,1), tag="est", effects=list(c(mesh.index,list(Intercept=1)), list(covars))) ## Matriz projetora predicao A.pred = inla.spde.make.A(IEBmesh) stk.pred = inla.stack(data = list(y = NA), A = list(A.pred),tag = "pred", effects=list(c(mesh.index,list(Intercept=1)))) str(stk.pred) stk.all <- inla.stack(stk.est, stk.val,stk.pred) ## Testar qual variável tem menor DIC names(covars) f.IEB <- y ~ -1 + Intercept + Dens.dren + f(i, model=IEBspde) names(inla.models()$likelihood) r.IEB <-inla(f.IEB,family="beta", control.compute=list(dic=TRUE),quantiles=c(0.025,0.1,0.5, 0.975), data=inla.stack.data(stk.all,spde=IEBspde), control.predictor=list(A=inla.stack.A(stk.all),compute=TRUE)) names(r.IEB) r.IEB$dic$dic r.IEB$summary.fixed r.IEB$summary.hyper[1,] r.IEB$summary.hyper[-1,] result <- inla.spde2.result(r.IEB, "i", IEBspde) names(result) str(r.IEB$marginals.hyperpar) ## Posterior mean inla.emarginal(function(x) x, result$marginals.variance.nominal[[1]]) inla.emarginal(function(x) x, result$marginals.range.nominal[[1]]) ## Quantis inla.qmarginal(c(0.025,0.5,0.975), result$marginals.variance.nominal[[1]]) inla.qmarginal(c(0.025,0.5,0.975), result$marginals.range.nominal[[1]]) par(mfrow=c(2,3), mar=c(3,3.5,0,0), mgp=c(1.5, .5, 0), las=0) plot(r.IEB$marginals.fix[[1]], type='l', xlab=expression(beta[0]), ylab='Density') plot(r.IEB$marginals.fix[[2]], type='l', xlab=expression(beta[1]),ylab= 'Density') plot(r.IEB$marginals.hy[[1]], type='l', xlab=expression(phi),ylab='Density') plot.default(inla.tmarginal(function(x) 1/exp(x), r.IEB$marginals.hy[[3]]), type='l', xlab=expression(kappa), ylab='Density') plot.default(result$marginals.variance.nominal[[1]], type='l', xlab=expression(sigma[x]^2), ylab='Density') plot.default(result$marginals.range.nominal[[1]], type='l', xlab='Practical range', ylab='Density') index.pred <- inla.stack.index(stk.all, "pred")$data names(r.IEB$summary.linear.predictor) linpred.mean <- r.IEB$summary.linear.predictor[index.pred,"mean"] linpred.2.5 <- r.IEB$summary.linear.predictor[index.pred,"0.025quant"] linpred.10 <- r.IEB$summary.linear.predictor[index.pred,"0.1quant"] linpred.50 <- r.IEB$summary.linear.predictor[index.pred,"0.5quant"] linpred.97.5 <- r.IEB$summary.linear.predictor[index.pred,"0.975quant"] (nxy <- round(c(diff(c(200,800)), diff(c(6700,7200))))) proj <- inla.mesh.projector(IEBmesh, xlim=c(200,800), ylim=c(6700,7200), dims=nxy) lp.mean.grid <- inla.mesh.project(proj, linpred.mean) lp.2.5.grid <- inla.mesh.project(proj, linpred.2.5) lp.10.grid <- inla.mesh.project(proj, linpred.10) lp.50.grid <- inla.mesh.project(proj, linpred.50) lp.97.5.grid <- inla.mesh.project(proj, linpred.97.5) par(mfrow=c(2,3), mar=c(3,3.5,0,0), mgp=c(1.5, .5, 0), las=0) image(lp.2.5.grid) image(lp.10.grid) image(lp.mean.grid) image(lp.97.5.grid) Abraço -- *Wagner Wolff, **PhD* "*Luiz de Queiroz**" College of Agriculture,* University of São Paulo Pádua Dias avenue11 | 13418-900| Piracicaba-SP| Brazil Phone: +55 19 982385582 <+55%2019%2098238-5582> http://orcid.org/0000-0003-3426-308X https://github.com/wwolff7 http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4463141A1