Apologies if this topic is in the wrong place.

I’ve been using a simple toy model to experiment with the rstan and loo packages (most current verison of each, as far as I’m aware), and I noticed what appears to be a discrepancy between the relative effective sample sizes I get from the summary method in rstan and the relative_eff function in loo. The toy model is as follows.

```
data {
int<lower=0> N;
vector[N] y;
}
parameters {
real mu;
real<lower=0> sigma;
}
model {
y ~ normal(mu, sigma);
mu ~ normal(-1, 1);
sigma ~ cauchy(0, 1);
}
generated quantities{
real lik[N];
for(i in 1:N){
lik[i] = exp(normal_lpdf(y[i] | mu, sigma));
}
}
```

The data is a small draw from rnorm. Note that I am directly generating the likelihoods by exponentiating the logs in the generated quantities block.

Creating the model and extracting the relative effective sample sizes from the stanfit method gives me one set of results:

```
stan_runs = stan(file = 'code/smallstantest.stan', data = list(y = y, N = N), verbose = TRUE, iter = 500, save_warmup=FALSE)
summary(stan_runs, pars = c('lik'))$summary[,'n_eff']/1000 #250 iterations * 4 chains = 1000 iterations
0.4850813 0.7350164 0.5353286 0.7060084 0.7548567 0.7030208 0.8220460 0.5953139 0.4300723 0.7909692
```

But using the provided function from loo gives me a slightly different (but somewhat similar) results:

```
relative_eff(extract(stan_runs, pars = c('lik'), permuted=FALSE))
0.4685559 0.7308909 0.5181340 0.7051706 0.7429508 0.7117508 0.7870215 0.5970975 0.4180794 0.7728746
```

What is the loo package doing differently? Looking through the source code didn’t reveal any obvious differences in methods. Is it some kind of warmup thing I’m missing? Perhaps something to do with permutation of chains? And to the extent that there is a difference, which is “correct”?

Any help is greatly appreciated.