Hi all,

Say I have a model expressed something like this:

```
y ~ 1 + me(x_mu, x_se)
```

Where the predictor X is observed with known measurement error, so the data comprise a point estimate `x_mu`

and a standard error `x_se`

for each observation.

This model appears to work just fine for my application, and from post-processing functions I can see that the estimates of the latent X corresponding to the observed data, are saved in the model object.

If I then want to use `fitted()`

or `predict()`

, for example to visualize the effects, I am required to specify new values of `x_se`

; this makes sense for the expected value of a new observation, but I’d like to show the relationship with the ‘true’ X. Is it possible to pass in values of the noise-free latent variable X to these functions instead? Can anyone suggest a preferred workflow for this? I haven’t been able to locate an example.

Cheers!