x1<-c(1.1,2.3,3.4,4.5,5.4)
x2<-c(-2.3,4.5,3.6,6.8,12.7)
y<-c(1.2,1.4,2.3,3.2,1.2)
As<-data.frame(x1,x2,y)

library(INLA)

##  1 predictor

formula1<-y~1+x1     # formula for model 

res1<-inla(formula1,family="gaussian",data=As,
control.compute=list(dic=TRUE,cpo=TRUE))   			#fitting model 

summary(res1)      #   displays the summary of the fit

sum1<-res1$summary.fixed   # storing the regression estimates 

res1$dic   #  displaying the DIC results 

## 2 predictors

formula2<-y~1+x1+x2
res2<-inla(formula2,family="gaussian",data=As,
control.compute=list(dic=TRUE,cpo=TRUE))   

## random effect (one predictor and individual level random effect) 

ind<-seq(1:5)
formula3<-y~1+x1+f(ind,model='iid')

ind2<-c(1,1,1,2,2)

## random slope model 
Formula5<-y~1+x1+f(ind,x2,model="iid")

## smoothed RW model on predictor 

Formula6<-y~1+x1+f(x2,model="rw1")