Hello,
I'm using Amelia II to impute missing data in a longitudinal setting. I'm
running into similar warnings that others have noticed regarding a variable
being perfectly collinear with another variable. I have 65 variables and 5
are deemed to be perfectly collinear. The most logical thing to do is to
remove the 5 variables and continue with the imputation process, which I do
and the model converges fine. However, I'm wondering if it makes sense to
add these 5 variables back in *after *the imputation process (these 5
variables contain no missing data). I realize that it is ideal to have all
the variables included in the original imputation model to best estimate
the missing values. However, at first glance, it doesn't seem harmful to
add back in variables that are collinear. Adding back in collinear
features might seem weird, but I'll be analyzing the data with penalized
regression and would like to keep all of the original data in the model.
I'd appreciate any feedback!
Thanks!
