[paper] writing

[DominiqueMakowski]

Let's get this bad boi out.

@mattansb @bwiernik what would you write about the two backends, emmeans and marginaleffects, their main differences etc.

Do they both rely on the delta method?

[DominiqueMakowski]

@strengejacke I think I'll need your help for the marginal section, the different types of marginalization etc. it's quite blurry in my head to be honest ^^

[DominiqueMakowski]

@easystats/core-team remaining bits:

• add details about types of marginalization
• Drawbacks/benefits of both backends
• Polishing

[mattansb]

emmeans relies on the delta method for non-linear transformations when those are done as part of the estimation.

@DominiqueMakowski where do you want my input, and what should it include? I must admit I have 0 familiarity with modelbased - I use marginaleffects, emmeans and ggeffects directly for plotting and estimation, so I have no idea what would be relevant here.

[DominiqueMakowski]

I must admit I have 0 familiarity

Your torment over you can make the switch now 😛

Mostly if you could take a look at the technical details section that talks about the backends

[strengejacke]

I must admit I have 0 familiarity with modelbased

modelbased currently covers classical predict() (non-focal held constant at representative values) or estimates marginal means (non-focal averaged/weighted), the latter using emmeans resp. marginaleffects via estimate_means(). estimate_means() should return identical results for both emmeans and marginaleffects backends, i.e. currently we support marginaleffects by mimicking the emmeans-approach of marginalzation.

[DominiqueMakowski]

@strengejacke I think here we could mention our 2 main types of marginalization (essentially copypasta our nice docs on that)

[strengejacke]

I can start working/helping on this paper by mid of February, or earlier if it's just minor stuff.

[DominiqueMakowski]

@mattansb any other interesting facts to mention here?

[strengejacke]

Reference for marginaleffects would be https://www.jstatsoft.org/article/view/v111i09

Arel-Bundock, V., Greifer, N., & Heiss, A. (2024). How to Interpret Statistical Models Using marginaleffects for R and Python. Journal of Statistical Software, 111(9), 1–32. https://doi.org/10.18637/jss.v111.i09

[mattansb]

Oh there's a lot that I can add.

I think the biggest difference would be:

• emmeans used a reference grid (that has by default a "complete" combination of all predictors) to generate expected values, by default conditioning on the mean of numeric values and marginalizing over categorical/binary variables, using a linear function of the model's coefficients (and v-cov matrix to get SEs) to give "predictions at the mean" (predictions for an average observation).
• marginaleffects uses unit level predictions to generate two counterfactual values - the difference of which is then taken (with SEs computed using the delta method), and averaged across all units. By default, the original model frame is used.

Of course, emmeans can also the delta method and can build complex reference grids (that aren't actually "grid" like), and marginaleffects can also generate linear perditions at the mean.

Using the delta method is more computationally costly than using a linear combination (though marginaleffects is very efficient). Using linear combinations with orthogonal "grids" also often means that results from emmeans directly correspond to a models coefficients (which is a benefit for those who are used to looking at regressions tables to understand their models - this can be shown with an example).

...

Would you like me to add all of this in? Just some of this?

[DominiqueMakowski]

Would you like me to add all of this in? Just some of this?

Anything you think is relevant, but I think it'll be good to be quite thorough and detailed here as the inner workings of marginal stuff are not often clearly explained so having details is good!

[mattansb]

Alright - I'll give it some time tomorrow.