19 Aug
2014
19 Aug
'14
3:31 p.m.
Hi Daniel, several answers:
1. you can use the graphics features to look at the data.
2. for many models, R^2 or something analogous to it isn't what you want.
in fact, the goal of many advanced procedures is to degrade the fit
(relative to say OLS) in order to gain more efficiency (e.g., WLS).
3. we will soon have a generic model misspecification test that will work
for almost all the models in Zelig called "GIM" based on comparing robust
and classical standard errors. See this paper: http://j.mp/InK5jU
Gary
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*Gary King* - Albert J. Weatherhead III University Professor - Director,
IQSS <http://iq.harvard.edu/>- Harvard University
GaryKing.org - King(a)Harvard.edu - @KingGary <https://twitter.com/kinggary> -
617-500-7570 - fax 812-8581 - Assistant <king-assist(a)iq.harvard.edu>du>:
495-9271
On Fri, Aug 15, 2014 at 1:56 PM, Daniel Eirikson <daniel.eirikson(a)gmail.com>
wrote:
Dear list members,
I am running a logistic regression model in R using multiply imputed data
created using Amelia II, which I am then analyzing using Zelig. I would
like to be able to report some measures of goodness-of-fit (e.g. likelihood
ratio, pseudo R-squared, Hosmer-Lemeshow) and was wondering what options I
have when using Zelig to analyze my data, as there are none that are
provided in the default output. I know some packages that provide measures
of goodness-of-fit, such as pscl, only work on glm objects, not MI objects
created when using Amelia and Zelig.
Are there any measures of goodness-of-fit that can be extracted from the
zelig() object, and if so how? And if not, does anyone know if there are
there any other packages that can run these tests on multiply imputed
datasets?
Thanks in advance for your help!
Best,
Daniel
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