Evaluating tensor effect at specific predictor values in brms, ignoring all other effects

Interfaces brms
1
  • Operating System: Windows 10
  • brms Version: 2.13.0

I’ve fit a distributional model of the following form:

fit = brm(
  bf(response ~ A + t2(X, Y) + (1|G/F), 
      response ~ A + t2(X, Y) + (1|G/F)),
  data = model_data, 
  family = gaussian())

A is a factor with two levels, while X and Y are Cartesian coordinates that describe location within a circle.

I would like to evaluate the tensor term t2() only (i.e., with all other effects set to zero) at the X and Y values used to fit the model, but have not been successful in using either conditional_smooths() or conditional_effects(), even when trying to pass the X and Y data into int_conditions. Using conditional_smooths() results in a finely evaluated mesh across the entire marginal domains of the predictors (so I get a rectangular surface rather than circular), whereas the latter seems to only evaluate the predictor X and Y coordinates conditioning on a specific value of Y or X, respectively.

Using fitted() with the newdata argument set to the X, Y values gives the desired result for the expected predicted response, but in this case I’m interested in purely the effect of the tensor. Is there a way of using fitted() such that all other model terms are set to zero / ignored?

Thank you!

2

Sorry for taking so long respond.

I don’t think you can do this easily in the current version. You can ignore the varying intercept (1|G/F) via the re_formula argument, but you can’t similarly get rid of the A fixed effect and the intercept.

You can however use posterior_samples to extract the tensor parameters and then compute the tensor yourself.

Alternatively, what should work would be to use fitted or posterior_linpred to get the predictor without the varying intercept, then use posterior_samples to obtain the samples for the fixed effect and intercept and then subtract intercept_samples + b_A_samples * A from the predictor, leaving you with just the t2 term… This is a bit hacky, but if evaluating the tensor yourself is out of the question, it could help.

Does that make sense?

Best of luck with your model!

3

Thanks, @martinmodrak, we had the same thought - that’s exactly what I ended up doing :).

4

At some point i will replace the re_formula argument with a more general one that allows to select terms much more flexibly.