Here’s my code. My Rhat values are NA for the Lu parameters. According to an other post, it seems to come form the fact that the sampled values are constant troughout the draws. Someone suggested to use the package posterior (https://github.com/stan-dev/posterior) to fix issue, however, I’m not sure how it is supposed to do so. Anyone else has a solution or can explain how I could use posterior. Thanks!

```
data {
int<lower=1> N; //number of observations
real M[N]; //reaction times
real X[N]; //predictor (days of sleep deprivation)
// grouping factor
int<lower=1> J; //number of subjects
int<lower=1,upper=J> Subject[N]; //subject id
}
parameters {
vector[2] beta; // fixed-effects parameters
real<lower=0> sigma_e; // residual std
vector<lower=0>[2] sigma_u; // random effects standard deviations
//declare L_u to be the Choleski factor of a 2x2 correlation matrix
cholesky_factor_corr[2] L_u;
matrix[2,J] z_u; // random effect matrix
}
transformed parameters {
// this transform random effects so that they have the correlation
// matrix specified by the correlation matrix above
matrix[2,J] u;
u = diag_pre_multiply(sigma_u, L_u) * z_u;
}
model {
real mu; // conditional mean of the dependent variable
//priors
L_u ~ lkj_corr_cholesky(2); // LKJ prior for the correlation matrix
to_vector(z_u) ~ normal(0,5);
sigma_e ~ normal(0, 5); // prior for residual standard deviation
beta[1] ~ normal(0, 0.5); // prior for fixed-effect intercept
beta[2] ~ normal(0, 2); // prior for fixed-effect slope
//likelihood
for (i in 1:N){
mu = beta[1] + u[1,Subject[i]] + beta[2]*X[i] + u[2,Subject[i]]*X[i];
M[i] ~ normal(mu, sigma_e);
}
}
```

This is my stan model. However, when I try to run it using my data, it does not work.

```
Warning messages:
1: The largest R-hat is NA, indicating chains have not mixed.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#r-hat
2: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#bulk-ess
3: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
http://mc-stan.org/misc/warnings.html#tail-ess
```

Here the ‘data’ in R:

```
data=list(N = nrow(MyData),
X = MyData$x,
M = MyData$m,
J = length(unique(MyData$id)),
Subject = as.numeric(factor(MyData$id,
labels = 1:length(unique(MyData$id)))))
```

Here is the model in R:

```
fit = stan(file = 'mediationHM.stan', data=data, iter=4000, chains =4).
```

What am I doing wrong?