Dear Amelia group,

Suppose my data set has a variable v that I want to include as a
predictor variable in a regression model. Supoose that some
transformation of v, for example, sqrt(v) or log(50 - v), looks more
normally distributed than v does. However, to keep the interpretation
of the model simpler, I want to include v itself as a predictor
variable, not a transformation of v.

What I had been doing previously was to use the "sqrts" or "logs"
argument of amelia(), and then use v (not the transformed v) in the
model. Or if a different transformation was required, I would create
the transformed variable then impute (with v as an idvar) then
back-transform, and use the back-transformed v in the model.

Is this considered poor practice because I was using the transformed v
for imputation but using v itself in the regression model? If it is,
would I be better off simply imputing without using any transformation
of v, assuming that v is the variable I want to include in the
regression model?

Thanks for any advice, and thanks to the Amelia team for all their work.

Mark
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