Obtaining predictions from a latent predictor variable (measurement error model)

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.