Hi Isaac, 

One thing you can do is include a subset expression that refers to a column in the data. This might reduce the possibility of there being a mismatch between the dimensions of the subset vector and the original data. Here is an example: https://gist.github.com/mattblackwell/7587294

library(Amelia)
data(africa)
a.out <- amelia(africa, ts = "year", cs = "country")

overimpute(a.out, "gdp_pc")
overimpute(a.out, "gdp_pc", subset = country == "Burkina Faso")
overimpute(a.out, "gdp_pc", subset = gdp_pc >=500)

Hope that helps! If you're still seeing an error message, please send it along. 

Cheers,
Matt






On Thu, Nov 21, 2013 at 1:04 PM, Isaac Petersen <dadrivr@gmail.com> wrote:
I'm trying to run overimputation as a diagnostic on a large data set with 20 multiple imputations from Amelia.  The overimputation diagnostic on one variable takes a long time to complete (more than 2 weeks).  Is there any way to expedite the overimputation procedure?  For example, would using the subset argument to select a subset of cases expedite the procedure?  If so, how would one use the subset argument?  I tried using a vector of TRUEs/FALSEs to indicate whether to keep a row, but I received an error.  Any suggestions for expediting overimpute and/or using the subset argument of overimpute() would be very helpful.

Thanks!
-Isaac

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