- 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
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
Y values used to fit the model, but have not been successful in using either
conditional_effects(), even when trying to pass the
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
Y coordinates conditioning on a specific value of
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?