- 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!