Imputation very slow
Posted: Thu Sep 26, 2013 11:20 am
Hi,
I am running a 2 level logistic model in MLwiN on a Macbook Air with Virtual Box. I have 5 level 1 variables and 3 level 2 variables. The outcome variable has minimal missing data. One of the level 1 variables is missing 10% of its data, the rest minimal levels. All level 2 variables have complete data.
I am attempting to use REALCOM to impute the missing data. I have set it up for a burn in of 1000, and 10,000 updates. It is however very slow. At the current rate I can see it taking 10+ hours, possibly many more.
Ideally, I would just like to verify that the missing data in the one variable exerts minimal bias on the results to justify using a full case approach, given that I have many models to run. Can you make any suggestions on how to deal with this?
Also, should the outcome variable be included in the response variables if it has missing data, or the auxiliary variables if it does not?
Many thanks
Shane
I am running a 2 level logistic model in MLwiN on a Macbook Air with Virtual Box. I have 5 level 1 variables and 3 level 2 variables. The outcome variable has minimal missing data. One of the level 1 variables is missing 10% of its data, the rest minimal levels. All level 2 variables have complete data.
I am attempting to use REALCOM to impute the missing data. I have set it up for a burn in of 1000, and 10,000 updates. It is however very slow. At the current rate I can see it taking 10+ hours, possibly many more.
Ideally, I would just like to verify that the missing data in the one variable exerts minimal bias on the results to justify using a full case approach, given that I have many models to run. Can you make any suggestions on how to deal with this?
Also, should the outcome variable be included in the response variables if it has missing data, or the auxiliary variables if it does not?
Many thanks
Shane