Hi Mark, 

We changed the infrastructure of Amelia in 1.7 to improve the speed considerably, but there may be consequences in terms of tolerance for highly correlated data. You might want to check to see if there are variables in the data with correlation above .95 or use the "findLinearCombos" function from the "caret" package to see if there are linear combinations hidden somewhere in the data. Unfortunately, a lot has changed from 1.6.1 to 1.7.1, so it's hard to exactly pin down your issues, but it seems very likely due to the structure of the data. 

Hope that helps!

Cheers,
matt.

~~~~~~~~~~~
Matthew Blackwell
Assistant Professor of Political Science
University of Rochester
url: http://www.mattblackwell.org


On Fri, Apr 5, 2013 at 12:27 AM, Mark Seeto <markseeto@gmail.com> wrote:
Dear Amelia mailing list,

I have some code that runs in Amelia 1.6.1 (with R 2.15.0) without any
problems. When I run that same code in Amelia 1.7.1 (with R 3.0.0), a
few imputations might finish without any error messages, but many
finish with the message "The resulting variance matrix was not
invertible.   Please check your data for highly collinear variables".
I understand that this suggests that there may be a problem with the
data, but I don't understand why there would be a problem in version
1.7.1 but not in 1.6.1.

Also, when I'm using Amelia 1.7.1 in R 3.0.0, R will sometimes crash
during the imputation process. I'm running R in emacs in Windows, and
I'll sometimes get a pop-up window saying "R for Windows terminal
front-end has stopped working", with the following message appearing
in emacs:

"error: chol(): failed to converge

This application has requested the Runtime to terminate it in an unusual way.
Please contact the application's support team for more information.
terminate called after throwing an instance of 'std::runtime_error'
  what():

Process R exited abnormally with code 3 at Fri Apr 05 15:04:58 2013"

Amelia 1.7.1 worked without any problems when I tried it on some small
made-up data, but not on my real data. I understand that it's almost
impossible to diagnose problems without a reproducible example, but I
was wondering whether others have had similar problems.

Thanks for any comments you can give.

Mark Seeto
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