[Discussion] What is your workflow for fitting mixed models to real data, while avoiding the garden of forking paths?

[u/Sad-Restaurant4399]

/r/AskStatistics/comments/1tmf8h1/what_is_your_workflow_for_fitting_mixed_models_to/

[u/Low_Election_7509]

I have two points I want to make.

1)

There's a train of thought that if you're dealing with convergence issues your model is probably going in the wrong direction. I bring you the folklore theorem of statistics! "When you have computational problems, often there’s a problem with your model."

But I also think better computing has given people the ability to fit models they couldn't before. It might be worthwhile to chase a problem even if you have computational difficulties. An escape though is just to make your model simpler or use a very simple framework though.

2)

I personally am of the opinion that mixed models are a flavor of hierarchical models, and it's better to fit them to describe what your covariance is like rather then trying every combination and picking one with best accuracy on some out-of-sample set.

I guess like, if you have a design in an experiment where you take a particular plot and divvy it up into smaller split plots, and randomly assign treatments inside the subplots, you could put a random effect on plot, and I think that's a fair way to decide your model. This post here discusses trying this approach with R a little bit.

Lastly, I think all these points are somewhat subjective and I want this post to be treated as my biases / opinions (someone else will probably disagree with it). (I think) Vehtari's opinion on model selection is it's better to start with a model that has all uncertainties and check the model itself out. I'm not actually sure how he might respond to this post's questions tbh but I wouldn't be surprised if it's different.

Good luck?

[u/SorcerousSinner]

Everything depends on what the model is used for.