How to built Gibbs sampler of Mixture Bayesian regression in R?

Question

I am working on a Gibbs sampler of three parameters and we know the full conditional distribution of three parameters.

Answer 1

Note: This is just a comment but to quote a long code, I put it here.

for (ite in 2:NSim){
#Full conditional for pi

 pi[ite]=rbeta(1, sum(delta[ite-1,])+0.5, sum(1-delta[ite-1,])+0.5)

 #Full conditional for delta
 for(j in 1:4){
  p1=pi[ite]*exp(-beta[ite-1,j]^2/(20))
  p0=((1-pi[ite])*10^3)*exp(-500*beta[ite-1,j]^2)
  cat('\n',ite,j,(p1/(p0+p1)))
  delta[ite,j]=rbinom(1, 1,prob=(p1/(p0+p1)))
  }

The error says that maybe $p1$ and $p0$ are NAs. My experience when checking this type of error is that. Instead of for loop, just give $ite=2$ and $j=1$. Compute p1 and p0 as your formulas. Check carefully if they are NAs or not. If the codes are symmetric with regard to $iter$ and $j$, and if you can correct the error for this case, it will pass this.