eval=FALSE. If there's an issue, just use the cem package directly.
Gary
--
*Gary King* - Albert J. Weatherhead III University Professor - Director,
IQSS - Harvard University
<http://gking.harvard.edu/> - King(a)Harvard.edu -
@kinggary<http://twitter.com/kinggary>- 617-500-7570 - Asst 495-9271 -
Fax 812-8581
On Tue, Feb 28, 2012 at 7:48 AM, Adamakis, Sotirios (Customer Analytics &
Decision) <Sotirios.Adamakis(a)lloydstsb.co.uk> wrote:
Hi, I have the same problem. Which option turns off
the calculation of L1?
****
** **
Regards, ****
Sotiris****
** **
** **
** **
------------------------------
if you use CEM and turn off the calculation of L1, its very fast and can****
deal with very large data sets.****
** **
Gary****
--****
*Gary King* - Albert J. Weatherhead III University Professor - Director,****
IQSS - Harvard University****
GKing.Harvard.edu <http://gking.harvard.edu/> - King at
Harvard.edu
<https://lists.gking.harvard.edu/mailman/listinfo/matchit> -****
@kinggary<http://twitter.com/kinggary>- 617-500-7570 - Asst 495-9271 -****
Fax 812-8581****
** **
** **
** **
On Mon, Feb 27, 2012 at 12:14 PM, Donny Baum <donnybaum at
gmail.com
<https://lists.gking.harvard.edu/mailman/listinfo/matchit>> wrote:****
** **
* Has anyone come across problems of performance
using MatchIt with large*
* datasets? I am trying to perform nearest
neighbor matching with 10*
* subclassifications on a sample of 400,000
(50,000 treatment cases, 350,00*
* untreated) with about 25 covariates. I was able
to get one round of*
* successful results after about 8 hours of
waiting for R to produce the*
* output. Is this typical of using MatchIt with
large data? Is there any way*
* to increase the speed or otherwise work around
this?*
* *
* Any help would be great.*
* *
* Cheers,*
* *
* Don Baum*
* *
* --*
* Don Baum*
* Ph.D. Candidate/ Graduate Assistant*
* Comparative and International Development
Education*
* Educational Policy and Administration*
* University of Minnesota*
* *
* *
* *
* -*
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