Hi everyone!

In my regression model (`Y ~ A:B`

), a numeric variable (`A`

) interacts with a categorical variable (`B`

). Since the categorical variable has a lot of unique levels, fitting the model using `GLM.jl`

package consumes a lot of RAM. I used the `FixedEffectModels.jl`

package and it looks much better! However, I have difficulties in extracting model coefficients such as **name of the effects, estimates, stderr, p_value, t_value**, etc. and also with **residuals** and **predict**. I read the documentation here and here, but I didnโt see the functions I needed

When building the model, I selected the option `save = true`

, which saves the residuals and fixed effects estimates. When I extract `fe(model)`

, I get a table like this:

```
julia> fe(model)
444378ร1 DataFrame
โ Row โ fe_B&A โ
โ โ Float64 โ
โโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโค
โ 1 โ -1.47252 โ
โ 2 โ -1.47252 โ
โฎ
โ 444376 โ -0.610356 โ
โ 444377 โ -0.610356 โ
โ 444378 โ -0.610356 โ
```

How can I also get **names of the effects to match them with the fixed effects estimates**? (because, I see only row numbers)

Also, how can I get other coefficients (stderr, p_value, t_value, etc)?

Thanks a lot for your help!