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
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.