Unfortunately this is missing diff_se, which would then give you the same information as when comparing two models. The difference is computed to the model with highest log predictive density (elpd_loo). You can still use this see the order, and then you can compare two models at time to check diff_se’s. Before seeing other diff_se’s my guess is that there is uncertainty about the difference between loom2Va and loom2Vai, but these models provide clearly better predictive performance than others.
Changing this output to be more clear is on our (with @jonah) todo list.
That makes sense - thank you for your response, avehtari.
I have just analysed them against each other and it seems like you have some good intuition regarding the two bigger models being better than the rest, but similar to each other. May I ask you which way you would prefer this reported?
One way could be to plot all the models against the null model (loom0V) with 2 SEs as error bars:
In this case, however, it seems that there is barely any difference between 3 of the models.
When I compare them pairwise, one can see that the 2 complex models are at least 2 SE’s better than the best model with one predictor, but this may look more confusing.
Do you have any preference or would you choose a completely different way of reporting this?
Edit: While writing this, I was thinking that one could also do as the table in my original post and compare all models against the best model. I think I would prefer that myself: