Built-in models:Mediation analysis

!Model
A mediation model can be written as

{{%%

\[
m_i = a_0 + a_1 x_i + e_{1i}
\]
\[
y_i = b_0 + b_1 m_i + b_2 x_i + e_{2i}
\]

%%}}
The mediation effect or indirect effect is \(a_1b_1\).
!Code
{{
model{
for (i in 1:N){
m[i] ~ dnorm(mum[i], pre.s1)
mum[i] <- a0 + a1*x[i]

y[i] ~ dnorm(muy[i], pre.s2)
muy[i] <- b0 + b1*m[i] + b2*x[i]
}
a0 ~ dnorm(0, 1.0E-6)
a1 ~ dnorm(0, 1.0E-6)
b0 ~ dnorm(0, 1.0E-6)
b1 ~ dnorm(0, 1.0E-6)
b2 ~ dnorm(0, 1.0E-6)
pre.s1 ~ dgamma(.001, .001)
pre.s2 ~ dgamma(.001, .001)
ab <- a1*b1
}
}}