Gelman & Hill (2006) say:

In Bugs, missing outcomes in a regression can be handled easily by

simply including the data vector, NA’s and all. Bugs explicitly

models the outcome variable, and so it is trivial to use this model

to, in eﬀect, impute missing values at each iteration.

This sounds like an easy way to use JAGS to do prediction. But do the observations with the missing outcomes also affect parameter estimates? If so, is there an easy way to keep these observations in the dataset that JAGS sees, but to not have them affect the parameter estimates? I was thinking about the cut function, but that's only available in BUGS, not JAGS.

## Best Answer

Yes, it is really easy to use in BUGS or JAGS! It is actually a pleasure to use it!

But do the observations with the missing outcomes also affect parameter estimates?Of course not. The parameters are only affected by the observed outcomes. The missing outcomes (NAs) will not affect anything, actually it is the other way: the missing outcomes will be derived from the parameters. Note that the missing outcomes will have its posterior distribution also. Then it is very easy to compute some derived quantities e.g. like a sum over indices of the outcome, and these derived quantities not only are handled for missing values, but also immediatelly have their posterior distribution. That's what is so sexy on BUGS & JAGS!

Have fun!