Hi Mithilesh,
It's not so much a limitation on the number of observations, but you are
asking a lot of Amelia here. If there are 28 categorical variables each
with more than 10 categories (and you have marked them so), then you adding
roughly 280 variables to the imputation model which is quite a few. But
that shouldn't be too bad, given the size of your data. It seems more
likely to be the extremely high missingness rate. You might try using the
ridge prior ("empri" argument in the amelia function). See section 4.7.1 of
vignette for more information about this setting:
https://cran.r-project.org/web/packages/Amelia/vignettes/amelia.pdf
Cheers,
Matt
~~~~~~~~~~~
Matthew Blackwell
Assistant Professor of Government
Harvard University
url:
http://www.mattblackwell.org
On Fri, Jan 29, 2016 at 10:54 PM, Mithilesh Kumar <mithileshk.in(a)gmail.com>
wrote:
I have 761,592 obs for 31 variables on users
behaviours towards online
ads. Out of 31 variables, 28 are categorical. Many cat. variables have more
than 10 categories. I am using Amelia for missing data imputation.
It's taking very long time. Are there other ways to do it fast? What's the
Amelia limits on number of observations ?
Is there any R-package which perform better on large dataset for missing
data imputation?
I checked for complete cases, there are only 172 complete cases which is
very insignificant as compare to total dataset.
--
Mithilesh Kumar
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