简单多元线性模型的Rstan代码

我正在尝试使用Rstan来拟合Christensen，Johnson，Branscum和Hanson的贝叶斯思想与数据分析：科学家和统计学家入门的示例模型。作者使用WinBUGS，因此需要进行一些修改。数据在这里，WinBUGS代码被复制在这篇文章的底部。这是一个非常简单的模型，但是我是一个完整的初学者，我无法弄清楚如何解决我遇到的错误。我的Stan代码如下：

data {
  int N_subjects;
  int N_items;
  matrix[N_subjects,N_items] y;
}

parameters {
  vector[N_items] mu;
  real<lower=0> sigma;
  real<lower=-1,upper=1> rho;
}

transformed parameters {
  cov_matrix[N_items] Sigma;
  for (j in 1:N_items)
    for (k in 1:N_items)
      Sigma[j,k] <- pow(sigma,2)*pow(rho,step(abs(j-k)-0.5));
}

model {
  sigma ~ uniform(0,100);
  rho ~ uniform(0,1);
  mu ~ multi_normal(0,100);
  for (i in 1:N_subjects)
    y[i] ~ multi_normal(mu,Sigma);
}

解析器将引发以下错误：

Error in stanc(file = file, model_code = model_code, model_name = model_name,  : 
  failed to parse Stan model 'model' with error message:
SYNTAX ERROR, MESSAGE(S) FROM PARSER:
no matches for function name="multi_normal_log"
    arg 0 type=vector
    arg 1 type=int
    arg 2 type=int
available function signatures for multi_normal_log:
0.  multi_normal_log(vector, vector, matrix) : real
1.  multi_normal_log(vector, row vector, matrix) : real
2.  multi_normal_log(row vector, vector, matrix) : real
3.  multi_normal_log(row vector, row vector, matrix) : real
4.  multi_normal_log(vector, vector[1], matrix) : real
5.  multi_normal_log(vector, row vector[1], matrix) : real
6.  multi_normal_log(row vector, vector[1], matrix) : real
7.  multi_normal_log(row vector, row vector[1], matrix) : real
8.  multi_normal_log(vector[1], vector, matrix) : real
9.  multi_normal_log(vector[1], row vector, matrix) : real
10.  multi_normal_log(row vector[1], vector, matrix) : real
11.  multi_normal_log(row vector[1], row vector, matrix) : real
12.  multi_normal_log(vector[1], vector

（我认为）我知道解析器告诉我我正在尝试将不合适的数据类型传递给模型块中的multi_normal函数，但我无法弄清楚它的起源。我怀疑我在定义协方差矩阵时做错了事，但似乎有多个参数具有错误的数据类型...

WinBUGS代码我正在对Stan代码进行建模：

model{
for(i in 1:30){
for(j in 1:6){
logy[i,j] <- log(y[i,j])
}
}
for(i in 1:30){logy[i,1:6]~dmnorm(m[1:6],precision[1:6,1:6])}
for(j in 1:6){
for(k in 1:6){
covariance[j,k] <- sigma2*pow(rho, step(abs(j-k)-0.5))
}
}
for(i in 1:6){ m[i] <- mu }
precision[1:6,1:6] <- inverse(covariance[1:6,1:6])
sigma ~ dunif(0,100)
mu ~ dnorm(0,0.001)
L <- -1/(6-1)
rho ~ dunif(L,1)
sigma2 <- sigma*sigma
tau <- 1/sigma2
}