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 didn’t 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) 444378×1 DataFrame │ Row │ fe_B&A │ │ │ Float64 │ ├────────┼────────────────────────┤ │ 1 │ -1.47252 │ │ 2 │ -1.47252 │ ⋮ │ 444376 │ -0.610356 │ │ 444377 │ -0.610356 │ │ 444378 │ -0.610356 │
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!