21 Nov
2013
21 Nov
'13
1:47 p.m.
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(a)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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