[R-sig-ME] sandwich variance estimation using glmer?

[David Atkins]

Tyler--

This question about "robust" (usually, heteroscedastically consistent) 
SE has come up before with respect to lme4 and lmer.  For example, see 
the following thread:

http://tolstoy.newcastle.edu.au/R/e3/help/07/10/0754.html

I have a vague memory of seeing a post from Achim Zeileis (the author of 
the "sandwich" package) mentioning why it might be challenging to extend 
his methods to lmer, but I couldn't (quickly) find it.

[BTW, you might be cautious about comparisons to SAS... R is a wonderful 
resource, but not universally guaranteed to do everything SAS can, and 
sometimes for good reasons.  Moreover, it is the result of an 
incredible, collective generosity of time and expertise (ie, nobody is 
making money here...).]

Hope that helps.

cheers, Dave

Tyler wrote:

Indeed, in this case the correlation structure of the random effects is not
fully appreciated or known, in which case the standard errors are likely
underestimated.  The use of sandwich estimators should render variance
estimates, and therefore inference, somewhat more realistic.  While this is
currently possible with GEEs, that approach does not ask the same question
as a GLMM (i.e. marginal or "population" estimates vs. conditional or
"subject-specific" estimates).

I used to think R updates were ahead of SAS upgrades in terms of new
approaches but apparently that is often not the case.  Does anyone have the
know-how required to implement this in R, or is there something I'm still
missing?

Best,
Tyler


On Wed, Nov 3, 2010 at 9:12 AM, Dimitris Rizopoulos <
d.rizopoulos at erasmusmc.nl> wrote:

 > On 11/3/2010 1:57 PM, Doran, Harold wrote:
 >
 >> Out of curiosity, why would you want a sandwich estimator from lmer? 
That
 >> estimator is typically used when the likelihood is misspecified, but you
 >> still want standard errors that account for correlations among units 
within
 >> a cluster.
 >>
 >> Since this is what lmer standard errors already account for, is there a
 >> need for the sandwich?
 >>
 >
 > well, it is possible that the random-effects structure that you have
 > specified is not the correct one (i.e., in order to fully account for the
 > correlations), and in this case it makes sense to use the sandwich 
estimator
 > (of course, the sandwich estimator has its own problems, but this is
 > probably another discussion...)
 >
 > Best,
 > Dimitris
 >
 >
 >
 >  -----Original Message-----
 >>> From: r-sig-mixed-models-bounces at r-project.org [mailto:
 >>> r-sig-mixed-models-
 >>> bounces at r-project.org] On Behalf Of Tyler Dean Rudolph
 >>> Sent: Tuesday, November 02, 2010 4:41 PM
 >>> To: r-sig-mixed-models at r-project.org
 >>> Subject: [R-sig-ME] sandwich variance estimation using glmer?
 >>>
 >>> Are there any current functionalities in R that permit estimation of
 >>> robust
 >>> sandwich variances based on lmer (mixed model) objects??  I'm aware of
 >>> the
 >>> sandwich package and gee implementations but to my knowledge these are
 >>> not
 >>> yet compatible with mixed model objects.
 >>>
 >>> Apparently these are already implemented in SAS....
 >>>
 >>> Tyler
 >>>
 >>>        [[alternative HTML version deleted]]
 >>>
 >>> _______________________________________________
 >>> R-sig-mixed-models at r-project.org mailing list
 >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
 >>>
 >>
 >> _______________________________________________
 >> R-sig-mixed-models at r-project.org mailing list
 >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
 >>
 >>
 > --
 > Dimitris Rizopoulos
 > Assistant Professor
 > Department of Biostatistics
 > Erasmus University Medical Center
 >
 > Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
 > Tel: +31/(0)10/7043478
 > Fax: +31/(0)10/7043014
 > Web: http://www.erasmusmc.nl/biostatistiek/
 >

	[[alternative HTML version deleted]]


-- 
Dave Atkins, PhD
Research Associate Professor
Department of Psychiatry and Behavioral Science
University of Washington
datkins at u.washington.edu

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