Using new values of random intercepts to predict values

[DikobrazDante]

Hello!

Let's say we have a following prediction:

mod <- y ~ x + (1+x | Subject)

Is there a way to predict new values in y, using the predict() function not by specifying only new fixed effects by using newdata arguement, but also by specifying a single random intercept value for which the predictions should be calculated?

According to my logic it should look something like this:
predict(mod, newdata = data.frame(x = seq(1,10, length.out=100), Subject$'(Intercept)' = 2)

[bbolker]

Just trying to clarify: you want to substitute 2 for the conditional mode/BLUP for all subjects? Wouldn't this be equivalent to predict(mod, newdata= ..., re.form=NA) + 2? (re.form=NA sets all random effects to zero). If not, can you be more specific about what you want to do?