Hi, I have a small question about how to specify parameters.

The model is

```
data {
int<lower=1> N; // Number of subjects
int<lower=1> obs; // Number of longitudinal measurements
int subject[obs]; // subject ID
vector[obs] Y;
vector<lower=0>[obs] time;
matrix[obs,3] NCS; // predictor matrix
}
transformed data {
matrix[obs,3] Q_ast;
matrix[3,3] R_ast;
matrix[3,3] R_ast_inverse;
// thin and scale the QR decomposition
Q_ast = qr_thin_Q(NCS) * sqrt(obs - 1);
R_ast = qr_thin_R(NCS) / sqrt(obs - 1);
R_ast_inverse = inverse(R_ast);
}
parameters {
real<lower=0> sigma_e;
vector<lower=0>[2] sigma;
vector[N] u_raw[2];
vector[3] beta;
real<lower=0> alpha;
}
transformed parameters {
vector[N] u[2];
vector[obs] theta;
for (j in 1:2) {
u[j] = sigma[j] * u_raw[j];
}
theta = alpha + Q_ast * beta + u[1][subject] + u[2][subject] .* time;
}
model {
Y ~ normal(theta,sigma_e);
for (j in 1:2) u_raw[j] ~ std_normal();
sigma_e ~ normal(0,10);
sigma ~ normal(0,10);
beta ~ normal(0,10);
alpha ~ normal(0,10);
}
```

the warning is

```
Exception: vector[multi] indexing: accessing element out of range. ... at line 32
```

I think the problem is the random effect `u[subject]`

.

The data is like

```
standata1 <- list(N=length(unique(dat$id)),obs=nrow(dat),subject=dat$id,Y=dat[,4],time=dat$month,NCS=NCS)
```

Because this is only a subset of the whole dataset. (Only the risk set)

For example, the `N`

could be 15, which means there are 15 unique subjects in this dataset.

But the serial number of subjects could be 30 or 45, since there are 50 subjects in total in the whole dataset. (`subject`

represents the serial number of subject of each observation)

So the term `u[subject]`

is reporting errors: accessing element out of range. (My guess)

How can I specify `u`

, making it corresponding with `unique(dat$id)`

?