estimating VAR coefficient using Metropolis algorithm MCMC in R

I am trying to estimate the slope coefficients for a VAR using a Metropolis algorithm. I was expecting that my output would have the same number of columns as my the X variable which has 20 columns, however, it has only 3 columns. I know I am doing something terribly wrong with the code, (that I found on this platform), but I am not sure what I should do. The code is as follows:

  #example data
  data<- matrix(rnorm(3600,10,1),ncol=20)

  Y=data[,c(1:20)]
  Y <- as.matrix(Y, ncol=20)
  p=4
  T=nrow(Y)
  X <- Y[p:(T-1),]

  set.seed(101)

  #for the loglikelihood
  logl <- function(m,X, Y) {
  ly =sum(dnorm(X,m[1]+m[2]*X, (m[3])^2,log=TRUE))
  }


  #the proposal function
  proposalfunction <- function(param,s){
  return(rnorm(3, mean = param, sd= s))
  }

  #the metropolis step
  Metropolis_MCMC <- function(startvalue, iterations,X, Y,s){
  i=1
  chain = array(dim = c(iterations+1,3))
  chain[i,] = startvalue
   while (i <= iterations){
    proposal = proposalfunction(chain[i,],s)
    probab = exp(logl(proposal,X = X, Y = Y) - logl(chain[i,],X = X, Y = Y))
    if(!is.na(probab)){
     if (runif(1) <= min(1,probab)){
       chain[i+1,] = proposal
     }else{
       chain[i+1,] = chain[i,]
     }
     i=i+1
    }else{
      cat('\r !')
   }
  }
  acceptance = round((1-mean(duplicated(chain)))*100,1)
  print(acceptance)
  return(chain)
 }


r <- Metropolis_MCMC(startvalue= c(0,.9,1), iterations = 36000, X, Y, s=.00085)