-----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: Wednesday, November 03, 2010 6:07 PM
To: Andrew Robinson
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] sandwich variance estimation using glmer?
Hi Andrew,
Unfortunately I do not have access to SAS, so that is simply not an option
for me, though I do welcome your clarification. If this is a sensitive
topic perhaps I will abstain from mentioning it in future, but to me it was
a simple observation and not a value statement requiring qualification.
Perhaps I should put this another way: can anyone confirm that this
functionality does NOT exist or is NOT presently being worked on somewhere
within the R sphere?
Tyler
On Wed, Nov 3, 2010 at 5:49 PM, Andrew Robinson <
A.Robinson at ms.unimelb.edu.au> wrote:
Hi Tyler,
I think that there's something that you're missing.
R is not motivated by comparisons with SAS or any package. So, your
impression that R was ahead of SAS or behind SAS is mistaken, or at
least, it's your impression, so you are responsible for it. R
responds exactly to the community's needs because the community
supports it. If the functionality that you want isn't there, it's
because noone else has wanted it badly enough to
a) code it up, or
b) pay someone else to code it up.
If you want that function, and you know that SAS has it, then use SAS.
If you want to use that function in R, then see the above two points.
Good luck,
Andrew
On Wed, Nov 03, 2010 at 04:04:23PM -0400, Tyler Dean Rudolph wrote:
Indeed, in this case the correlation structure of the random effects is
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
currently possible with GEEs, that approach does not ask the same
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
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?
estimator is typically used when the likelihood is misspecified, but
still want standard errors that account for correlations among units
a cluster.
Since this is what lmer standard errors already account for, is there
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
correlations), and in this case it makes sense to use the sandwich
(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
the
sandwich package and gee implementations but to my knowledge these
not
yet compatible with mixed model objects.
Apparently these are already implemented in SAS....
Tyler
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