Error: Attempt to redefine node in linear regression

I have fitted following simple linear regression Bayesian model using rjags. I was able to run the model by specifying all the predictors separately(like for a lm object). Now I want to learn how to specify the predictors by introducing them as a matrix instead of specifying them separately.

So I ran the following code, but it gave some errors.

I used tobbaco data set in rrr package to provide a reproducible example.

library(rrr)
require(dplyr)
library(rjags)
tobacco <- as_data_frame(tobacco)
N1 = length(tobacco$Y1.BurnRate)
x1 = model.matrix(Y1.BurnRate~X2.PercentChlorine+X3.PercentPotassium ,data = tobacco)
        
bayes_model_mul1=
 "model {
      for(i in 1:N1){
        Y1.BurnRate[i]~dnorm(mu1[i],tau1)
        
        for(j in 1:3){
          mu1[i]=beta1[j]*x1[i,j]  
        }
      }
      
      for (l in 1:3) { beta1[l] ~dnorm(0, 0.001) }        
      tau1 ~ dgamma(.01,.01)
      sigma_tau1 = 1/tau1 
        
    }"
        
        
model3 <- jags.model(textConnection(bayes_model_mul1), 
                     data = list(Y1.BurnRate=tobacco$Y1.BurnRate, x1=x1, N1=N1),
                     n.chains=1)

After I run model3 , I got following error.

Error in jags.model(textConnection(bayes_model_mul1), data = list(Y1.BurnRate = tobacco$Y1.BurnRate, :
RUNTIME ERROR:
Compilation error on line 6.
Attempt to redefine node mu1[1]

Can anyone help me figure this out ? Does this due to introducing predictors as a matrix ?

There are a few ways to do this, here are two:

1. Use matrix multiplication outside of the likelihood loop

m1 =
 "model {
     mu1 = x1 %*% beta1 # ---> this
     for(i in 1:N1){
       Y1.BurnRate[i] ~ dnorm(mu1[i], tau1)
    }

    for (l in 1:3) { beta1[l] ~ dnorm(0, 0.001) }
    tau1 ~ dgamma(.01,.01)
    sigma_tau1 = 1/tau1 
 }"

2. Use inprod to multiply the parameters with the design matrix

m2 = 
  "model {
    for(i in 1:N1){
      mu1[i] = inprod(beta1, x1[i,]) #----> this
      Y1.BurnRate[i] ~ dnorm(mu1[i], tau1)
    }

    for (l in 1:3) { beta1[l] ~ dnorm(0, 0.001) }
    tau1 ~ dgamma(.01,.01)
    sigma_tau1 = 1/tau1 
 }"

You received an error with for(j in 1:3){ mu1[i] = beta1[j]* x1[i,j] } as every time you loop though the parameter index j you overwrite mu1[i]. It also doesn't sum up the individual terms. You may be able to index mu1 with j as well and then sum but untested ...