08-10-2015 10:39 AM
I am new to multiple imputation and I am trying to impute data in two different variables.
In my dataset I have 10 variables, 8 of them are complete and 2 contains missing data. Of these two, variable1 contains continous data and variable2 categorical (0/1).
About 20% of data for variable1 (continous data) is missing. Values in this variable(variable1) ranges from 1 to 70.
When I am imputing I recieve negative some values in variable1. I am probably doing something wrong in the settings fro PROC MI or can it be negative if all values are positive?
08-10-2015 11:28 AM
The MCMC algorithm, which is the default method, assumes multivariate normality, which is why you are getting negative values. Look at the documentation for the TRANSFORM statement and hopefully you can choose a transformation that transforms your data to MVN.
08-10-2015 01:08 PM
You want to be sure that your imputation model is compatible with your analysis model. If you log X in the imputation model but don't log X in your analysis model, your regression estimates will be biased. If you don't plan to log X in analysis, it is typically better not to log X in your imputation model, even if that means some impute values are out of bounds.
For details see this paper: https://arxiv.org/abs/1707.05360