model { #likelihood for (i in 1:n) { y[i] ~ dnorm(mu[i], tau) mu[i] <- beta0 + beta1 * x1[i] + beta2 * x2[i] } #priors tau ~ dgamma(0.01, 0.01) beta0 ~ dnorm(0.0,0.001) beta1 ~ dnorm(0.0,0.001) beta2 ~ dnorm(0.0,0.001) #sigma s2<-1/tau s<-sqrt(s2) #samplevar sy2<-pow(sd(y[]),2) #bayes R2 R2B<- 1 - s2/sy2 #expected y typical.y<-beta0+beta1*mean(x1[])+beta2*mean(x2[]) }