Also you can try increasing the workspace memory, set in gsrun.cfg
max_workspace = 32.0
try changing this to something modern, like 256
Ken
Gary King wrote:
On Thu, 9 Jun 2005, fdmplimo(a)usp.br wrote:
Dear professor King
My name is Fernando Limongi. I work with Argelina Figueiredo taht
recently talked with you about applying your solution to the
ecological problem to Brazilian electoral data. I am using eziwin to
get the estimates of split or join vote on Brazilain elections. The
program runs only up to 3,000 or 4,000 cases. When I try with more
cases, the program does not run. In the software documentation there
is no reference to limitations due to the number of cases. Is there
such a limitation? If yes, Is there something that can be done to
circunvent this limitation?
I am trying to run the data on EI but Gauss is not very popular in
Brazil, so no one has a copy of the program. I tried to adapt your
routine with some others softwares but always fial for the missinf of
some of the programes invoked. With regard EI, Gauss is the only
software that runs it?
Thank you very much for your time
Fernando Limongi
Political Science Department at USP
You probably have just a hardware limitation; if you used a machine with
more RAM, odds are it will work. But there are workarounds. The key is
that the basic model only estimates 5 parameters in order to get you
precinct-level estimates for all n precincts. The extended model won't
usually have many more parameters.
That means you are using a lot of data to estimate those parameters and
you could use less without much cost. There are several options. The
easiest is to set _EselRnd to select (randomly) some fraction of the
data in the estimation stage; you'll still get estimates for all
quantities of interest at the precinct level. The other option would be
to run the whole analysis separate for different subsets (such as
regions) of your data. That too would work and would add some
flexibility, allowing the model to fit the data better.
Gary
---
Gary King Institute for Quantitative Social Science
Harvard University, 34 Kirkland St, Cambridge, MA 02138
http://GKing.Harvard.Edu, King(a)Harvard.Edu
Direct 617-495-2027, Assistant 495-9271, eFax 812-8581
-
EI mailing list served by Harvard-MIT Data Center
Subscribe/Unsubscribe:
http://lists.hmdc.harvard.edu/?info=ei