data(Germany) g = system.file("demodata/germany.graph", package="INLA") source(system.file("demodata/Bym-map.R", package="INLA")) summary(Germany) ## just make a duplicated column Germany = cbind(Germany,region.struct=Germany$region) # standard BYM model (without covariates) formula1 = Y ~ f(region.struct,model="besag",graph.file=g) + f(region,model="iid") result1 = inla(formula1,family="poisson",data=Germany,E=E) # with linear covariate formula2 = Y ~ f(region.struct,model="besag",graph.file=g) + f(region,model="iid") + x result2 = inla(formula2,family="poisson",data=Germany,E=E) # with smooth covariate formula3 = Y ~ f(region.struct,model="besag",graph.file=g) + f(region,model="iid") + f(x, model="rw2") result3 = inla(formula3,family="poisson",data=Germany,E=E) inla.pause() dev.new() par(mfrow=c(2,2)) Bym.map(result1$summary.random$region.struct$mean) Bym.map(result2$summary.random$region.struct$mean) Bym.map(result3$summary.random$region.struct$mean) ## An alternative to above is to use the combined model BYM, which ## contains both the "iid" and the "besag" model. This makes it ## possible to get the marginals of "idd" + "besag" which otherwise is ## only possible using linear-combinations. Here, I just repeat the ## first example. I also add some prior paramters and initial values ## as they are in the order ("idd", "besag"). Further, the internal ## representation is ("iid"+"besag", "besag"), which implies that the ## first 'n' elements of the mean vector (of length '2*n') is the sum ## and the remaining 'n' elements is the spatial term. If the ## constr=TRUE, the only the "besag" part satisfy the sum-to-zero ## constraint. prior.iid = c(1,0.01) prior.besag = c(1,0.001) initial.iid = 4 initial.besag = 3 formula1.bym = Y ~ f(region, model = "bym", graph.file = g, param = c(prior.iid, prior.besag), initial = c(initial.iid, initial.besag)) result1.bym = inla(formula1.bym,family="poisson",data=Germany,E=E) |

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