Hi everyone,

I fitted a stan_glmer model to my data and wanted to use loo to compare models with or w/o a fixed effect of interest. This is my full model:

```
post_rep_full <- stan_glmer(Resp13 ~ (Distance * Context ) +
(Distance * Context || Subject),
data = myData, family = binomial(),
iter=4000, chains=6,
prior_intercept = normal(0,7.5),
prior = normal(0,2.5), QR = TRUE) # i removed the int term. the model does it.
```

and then I dropped my strongest fixed effect (this fixed effect had a coefficient of 0.56 [95% HDI = [0.44-0.68]]), keeping the random-effects structure untouched:

```
post_rep_no_dist <-stan_glmer(Resp13 ~ (Context + Distance:Context ) +
(Distance * Context || Subject),
data = myData, family = binomial(),
iter=4000, chains=6,
prior_intercept = normal(0,7.5),
prior = normal(0,2.5), QR = TRUE)
```

Then, I calculated loo for each model

```
loo_full<- loo(post_rep_full)
loo_no_distance = loo(post_rep_no_dist)
```

and comparing the models, I got

```
elpd_diff se
-0.4 1.1
```

suggesting (if I understand correctly) that we are indifferent between the models, with an unreliable preference to the full model. How come that the full model isnâ€™t preferred in this case? I received a similar result when I dropped another fixed effect with a coefficient around zero.

this is what happens when I drop each single fixed effect and compare all models. All the values seem very similar, I donâ€™t know whyâ€¦

```
looic se_looic elpd_loo se_elpd_loo
post_rep_full 20498.0 117.7 -10249.0 58.8
post_rep_no_dist 20498.7 117.8 -10249.3 58.9
post_rep_no_context 20498.9 117.6 -10249.4 58.8
post_rep_no_interaction 20499.3 117.7 -10249.7 58.8
p_loo se_p_loo
post_rep_full 71.7 0.8
post_rep_no_dist 73.1 0.8
post_rep_no_context 72.2 0.8
post_rep_no_interaction 71.8 0.8
```