SCcongen90<-list(obs=c(0,7,1,5,1,1,5,16,0,17,4,0,0,1,1,7,1,3,0,0,8,2,13,7,0,8,0,3,2,4,1,11,0,1,2,3,3,8,6,14,3,11,6,0,1,5),
expe=c(1.0807,6.3775,0.622,6.6854,0.9142,1.0744,5.6518,8.1682,0.5749,18.0989,2.174,1.6619,1.9321,1.6148,1.6713,
3.0819,1.7562,4.9952,0.9362,1.2001,6.1293,2.5604,15.8589,2.9437,1.0399,7.276,0.9739,2.064,2.7206,2.8275,
0.9425,8.828,0.3644,1.775,1.5111,1.5111,2.5321,4.5836,3.9647,15.0264,0.732,10.8292,5.9848,1.4357,1.9949,6.9807),
pov=c(13.6,13.8,32.3,11,24.2,19.9,12.3,13.3,17,15.4,14.1,15.7,18,24.3,21.8,20.2,24.9,12,17.4,18.1,18.7,17.5,10.6,
13.8,22.8,13.7,21,12.6,14,14,26.9,9.4,17.8,23.1,23.1,14.4,10.8,22.1,10.1,13.6,15.9,11.2,18.3,14,26.4,10.6),
inc=c(36.786,38.534,20.727,37.205,24.3,27.607,45.822,40.161,32.247,38.458,33.232,31.715,29.505,25.896,
28.919,30.776,25.552,42.886,34.297,29.96,34.009,35.008,41.658,34.109,27.65,34.654,27.117,39.04,33.698,
32.32,25.144,45.14,29.805,25.008,25.993,32.231,36.912,28.624,37.054,39.587,31.324,37.092,31.948,30.801,23.748,44.619))
region<-seq(1:46)

library(INLA)
####geobugs2inla(adj, num, graph.file="SC.graph")   ## assumes that adj and num are already available
prior.iid = c(1,0.01)
prior.besag = c(1,0.001)
initial.iid = 4
initial.besag = 3

formula1.bym = obs ~ f(region, model = "bym", graph.file = "SC.graph",
                     param = c(prior.iid, prior.besag),
                     initial = c(initial.iid, initial.besag))
result1 = inla(formula1.bym,family="poisson",
data=SCcongen90,control.compute=list(dic=TRUE,cpo=TRUE,graph=TRUE),E=expe)

sum<-result1$summary.random
RE1<-sum$region[1:46,2]   # uncorrelated RE
RE2<-sum$region[47:92,2]   #correlated RE
 


 
library(foreign)
library(sp)
library(maptools)
source("fillmap.R")

geobugsSC<-readSplus("SC_geobugsSPlus.txt")   # SC counties 46#
plot(geobugsSC)
maintitle1<-paste("uncorrelated heterogeneity")
maintitle2<-paste("correlated heterogeneity")

fillmap(geobugsSC,maintitle1,RE1,n.col=6)
x11()
fillmap(geobugsSC,maintitle2,RE2,n.col=6)
################################################################################################

## means and SDs and CIs in sum object

sum

##   DICs and CPOs

result1$dic
result1$cpo