model{
#specify the multilevel model, z and d are the number of first-level units and second-level units, respectively.
for(j in 1:z){
beta3[j] ~ dnorm(0,1.0E-6)
beta1[j] ~ dnorm(0,1.0E-6)
for(i in 1:d){
#specify the first level model: m[j,i]=beta3[j]+e2[j,i]
m[j,i] ~ dnorm(beta3[j], prec.m)
#specify the first level model: y[j,i]=beta1[j]+beta2*m_1[j,i]+e1[j,i]
y[j,i] ~ dnorm(mean.y[j,i], prec.y)
mean.y[j,i]<- beta1[j]+beta2*m_1[j,i]
}
#specify the second level model: beta3[j]=beta3+alpha*x[j]+e4[j]
beta3[j] ~ dnorm(mean.beta3[j], prec.beta3)
#mean.beta3[j]<-beta3+alpha*x[j]
mean.beta3[j]<-beta3[j]+alpha*x[j]
#specify the second level model: beta1[j]=beta1+tau.p*x[j]+beta*m_2[j]+e5[j]
beta1[j] ~ dnorm(mean.beta1[j], prec.beta1)
#mean.beta1[j]<-beta1+tau.p*x[j]+beta*m_2[j]
mean.beta1[j]<-beta1[j]+tau.p*x[j]+beta*m_2[j]
}
#prior distribution of parameters.
beta2 ~ dnorm(0,1.0E-6)
#beta1 ~ dnorm(0,1.0E-6)
tau.p ~ dnorm(0,1.0E-6)
beta ~ dnorm(0,1.0E-6)
#beta3 ~ dnorm(0,1.0E-6)
alpha ~ dnorm(0,1.0E-6)
prec.y ~ dgamma(0.001,0.001)
prec.m ~ dgamma(0.001,0.001)
prec.beta1 ~ dgamma(0.001,0.001)
prec.beta3 ~ dgamma(0.001,0.001)
#define the effects as function of parameters
theta <- alpha*beta
}