7 Mar
2003
7 Mar
'03
5:08 p.m.
I think the problem is the number of variables. you have a lot of
parameters (they increase fast in the number of variables, although less
fast for the fully observed ones). you can try increasing the number of
simulations a lot, but you'll probably need to increase the RAM the
program uses too. I'd think about whether you really need all the
variables.
Gary King
: Gary King, King(a)Harvard.Edu http://GKing.Harvard.Edu :
: Center for Basic Research Direct (617) 495-2027 :
: in the Social Sciences Assistant (617) 495-9271 :
: 34 Kirkland Street, Rm. 2 HU-MIT DC (617) 495-4734 :
: Harvard U, Cambridge, MA 02138 eFax (928) 832-7022 :
On Thu, 6 Mar 2003, Matthew Vile wrote:
Looking for some suggestions here -
I've got NES data (2000 Presidential election to be exact) merged with
crime rate data (county level) from the FBI. I'm imputing 48
variables (10 fully observed) of which two are missing @ 50% (n =
1768). I have already transformed all the variables to be as close to
normal as possible a'la the suggestions in Honaker, etal (2001), I'm
using a ridge prior of 6, and I've increased the _AMsn global to 100.
Despite all this Amelia still crashes during stage 3 (Importance
Sampling) and reports the error message :
There are insufficient valid draws from the approximating
distribution so the program will end. This error may occur
for a number of reasons including severe missingness and
data which badly violates the assumptions of the imputation
model. The user should be able correct the problem by
increasing the draws from the approximating distribution
(see _AMsn global); by transforming the variables to more
closely meet the model's distributional assumptions;
by using or increasing the strength of the prior; by using
the t-distribution for the approximating distribution (see
_AMst global); and/or adjusting the _AMsfac global.
Press any key to exit
Currently active call: RNDISMP [154]
Now, I don't think the problem is the severe missingness because I was
able to get Amelia to run on this data before I merged the crime data
into the set (note: this was before I learned that scales, etc. require
both the scale and it's components to be entered - thus the old dataset
only @ 20 variables in it.)
My question is, is it worth my time to keep increasing the _AMsn global
(how high should you go?) or should I just bite the bullet and start
playing with the _AMst and _AMsfac globals? If I change these, how
much am I compromising the robustness of my imputation model?
Matthew Vile,
University of New Orleans