Hi, I am trying to build a model to understand neural spikes of bats as function of their position (I have bunch of Xs and Ys in each timeframe, and spike-count for that timeframe).

I am using Poisson GAM (which is a fancy GLM), to predict spike counts as function of bats positions and the library pygam (GitHub - dswah/pyGAM: [HELP REQUESTED] Generalized Additive Models in Python).

The goal of the model as I mentioned is to make sense of the data, namely to understand the relation between each feature and it affect on the output.

Therefore I am plotting Partial Dependence Plots, but I am not sure on how to interpret the y-axis of these plots. When I was looking deeper into the code, it seems like the PDPs are not being calculated with the inverse of the log link function (which is what I would expect), therefore the y-axis corresponds somehow to the log of the number of spikesâ€¦ it is not really intutitve. I tried to manually pass it through e^x but the results are still not interpretable to me.

- How should I calculate the new confidence intervals after I pass the values through e^x?
- Something which is clearly missing is the removal of the effect of other features! I am not sure how to do that.

Thanks ahead!