[R] lm: RME vs. ML

[Ravi Varadhan]

"worrying about df (ml vs reml) is just a silly obsession of statisticians (of which I'm one)"

I too have often wondered about the importance of such tertiary issues.  My half-baked understanding is that the main "practical" difference between ML vs REML is with regards to ease of computing the estimates, i.e. the REML estimates can be computed much more easily than ML.  However, I am wide open to be enlightened about other practically important differences.

Best,
Ravi.

____________________________________________________________________

Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University

Ph. (410) 502-2619
email: rvaradhan at jhmi.edu


----- Original Message -----
From: Bert Gunter <gunter.berton at gene.com>
Date: Tuesday, December 8, 2009 11:38 am
Subject: Re: [R] lm: RME vs. ML
To: 'John Sorkin' <jsorkin at grecc.umaryland.edu>, r-help-bounces at r-project.org, JLucke at ria.buffalo.edu
Cc: r-help at r-project.org


> A contrarian point of view:
> 
> If you have so little data (relative to the number of parameters to be
> estimated, especially NONLINEAR parameters like covariance estimates)that
> the ml vs reml bias could be large, then there's so little information
> anyway that such bias is the least of your problems (identifiability
> probably is a major issue-- mis-shapen confidence regions).
> 
> Ergo, worrying about df (ml vs reml) is just a silly obsession of
> statisticians (of which I'm one).
> 
> Criticisms, public or private, welcome of course. 
> 
> This is my view only and should not be considered a stain on my 
> employer --
> other than its misfortune in employing me.
> 
> 
> Bert Gunter
> Genentech Nonclinical Biostatistics
> 
> 
> -----Original Message-----
> From: r-help-bounces at r-project.org [ On
> Behalf Of John Sorkin
> Sent: Tuesday, December 08, 2009 7:12 AM
> To: r-help-bounces at r-project.org; JLucke at ria.buffalo.edu
> Cc: r-help at r-project.org
> Subject: Re: [R] lm: RME vs. ML
> 
> Your question is well taken. I did not give any criteria because I realized
> there might be different answers based upon different criteria. Certainly
> one fundamental criteria would be that the estimates are BLUE, but 
> this is
> not the only criteria one might be used.
> John 
> -----Original Message-----
> From: <JLucke at ria.buffalo.edu>
> To: John Sorkin <jsorkin at grecc.umaryland.edu>
> Cc:  <r-help at r-project.org>
> To:  <r-help-bounces at r-project.org>
> 
> Sent: 12/8/2009 9:39:28 AM
> Subject: Re: [R] lm: RME vs. ML
> 
> You need to give your criteria for "preferable".  For normal-linear 
> models, REML estimates of variances are unbiased, whereas ML estimates 
> are 
> downwardly biased.  My intuition is that the ML-induced bias would be 
> 
> worse in small samples. I don't know about other distributions. 
> Likewise I 
> don't know about MSE or other criterion for preference.
> 
> 
> 
> 
> 
> 
> "John Sorkin" <jsorkin at grecc.umaryland.edu> 
> Sent by: r-help-bounces at r-project.org
> 12/07/2009 09:24 PM
> 
> To
> <r-help at r-project.org>
> cc
> 
> Subject
> [R] lm: RME vs. ML
> 
> 
> 
> 
> 
> 
> windows XP
> R 2.10
> 
> As pointed out by Prof. Venables and Ripley (MASS 4th edition, p275), 
> the 
> results obtained from lme using method="ML" and method="REML" are 
> often 
> different, especially for small datasets. Is there any way to 
> determine 
> which method is preferable for a given set of data?
> Thanks,
> john
> 
> 
> John David Sorkin M.D., Ph.D.
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
> 
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