`Model.predict()` generates unexpected out-of-sample predictions for a mixed effects model

Hello,

first of all, thanks for the great work on this project, I’ve been using bambi a lot and it has been super helpful!

I’m currently facing a (potential) issue when trying to make out-of-sample predictions for a logistic regression model built with the following formula:

y ~ x1 + x2 + x3 + (0 + x2|x1) + (0 + x3|x1)

where x1 and x2 are categorical variables with two dimensions respectively and x3 is a continuous variable.

The out-of-sample data I’m trying to make predictions for looks like this (exemplary):

x1	x2	x3
0	0	0
0	0	0.5
0	0	1
0	0	1.5
1	0	0
1	0	0.5
1	0	1
1	0	1.5

There was no error when running model.predict(iData, data=out_of_sample_data, kind='mean'), however the spaghetti plot I generated from the posterior predictions looked off for when x1==1, the variance was much bigger than I expected. (I noticed this because I had manually made a plot displaying the 0.5 decision boundary for x3, i.e. the mean value and hdi intervals of x3 where there is a 50% probability of a positive outcome and that didn’t match what I saw in the spaghetti plot.)

I then had a look at the code and noticed that the Z matrix generated in the predict method in models.py looked different from what I expected. Here’s the code bit I’m referring to (last line):

        if self._design.group:
            if in_sample:
                Z = self._design.group.design_matrix
            else:
                Z = self._design.group._evaluate_new_data(data).design_matrix

What I got for Z was the following:

1	0	0	0	0	0
1	0	0	0	0.5	0
1	0	0	0	1	0
1	0	0	0	1.5	0
0	0	1	0	0	0
0	0	1	0	0	0.5
0	0	1	0	0	1
0	0	1	0	0	1.5

…but what I was expecting (after trying to make sense of it) was this:

1	0	0	0	0	0
1	0	0	0	0.5	0
1	0	0	0	1	0
1	0	0	0	1.5	0
0	1	0	0	0	0
0	1	0	0	0	0.5
0	1	0	0	0	1
0	1	0	0	0	1.5

so basically the second and third column swapped. I then added the following line:

Z[:, [1, 2]] = Z[:, [2, 1]]

to achieve that and the spaghetti plot I then generated matched my expectation.

It would be great if someone had a look at this and fixed it properly (if it really is an issue and not me making a mistake), I hope it was clear enough and if not, let me know!

[LeonieMei]

Yes, it seems to be working now 😃

[LeonieMei]

Yes, I also made a plot for a model with basically the same specification but instead of two categories in the group variable I had four and it also worked in that case 👍