ML vs. REML to find a parsimonious mixed model
Hi Christoph, No, I didn't. And I'm still very interested in what other mixed model experts/experienced mixed model users think about it. At the moment I tend to use REML for this purpose. Best, Maarten On Mon, Apr 23, 2018 at 4:20 PM, Christoph Huber <
christoph.huber-huber at univie.ac.at> wrote:
Hi Maarten, Did you get any responses yet? I was facing the same problem and went for REML eventually. But it still seems to me that this question does not (yet) have a definite answer. Best, Christoph Am 16.04.2018 um 12:00 schrieb r-sig-mixed-models-request at r-project.org: Send R-sig-mixed-models mailing list submissions to r-sig-mixed-models at r-project.org To subscribe or unsubscribe via the World Wide Web, visit
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models or, via email, send a message with subject or body 'help' to r-sig-mixed-models-request at r-project.org You can reach the person managing the list at r-sig-mixed-models-owner at r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-mixed-models digest..." Today's Topics: 1. ML vs. REML to find a parsimonious mixed model (Maarten Jung) ---------------------------------------------------------------------- Message: 1 Date: Sun, 15 Apr 2018 13:00:08 +0200 From: Maarten Jung <Maarten.Jung at mailbox.tu-dresden.de> To: Help Mixed Models <r-sig-mixed-models at r-project.org> Subject: [R-sig-ME] ML vs. REML to find a parsimonious mixed model Message-ID: <CAHr4Dycsa1wmOXKKmDuGzrQi8pxgXq55iQxjEoEzFvyYNmvUvA at mail.gmail.com> Content-Type: text/plain; charset="utf-8" I want to use LRTs via anova() on fitted linear mixed models (merMod objects) to find a parsimonious mixed model containing only variance components supported by the data (e.g. Matuschek et al. 2017 [1], Bates et al. 2015 [2]). In this situation my focus is *only on the reduction of the random effects part* of the models. The aforementioned papers use ML instead of REML estimation within this process. Douglas Bates seems to prefer ML model comparison due to the skewed nature of the distribution of variance estimators [3] and the user Wolfgang states that "the ML estimator usually has lower mean-squared error (MSE) than the REML estimator" [4]. However, literally every textbook I know suggests using REML estimation when comparing mixed models that differ only in their random effect parts. What would you suggest in this particular situation? ML or REML? Best regards, Maarten [1] https://arxiv.org/abs/1511.01864 [2] https://arxiv.org/abs/1506.04967 [3] https://stat.ethz.ch/pipermail/r-sig-mixed-models/2015q3/023750.html [4] https://stats.stackexchange.com/a/48770 [[alternative HTML version deleted]] ------------------------------ Subject: Digest Footer _______________________________________________ R-sig-mixed-models mailing list R-sig-mixed-models at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models ------------------------------ End of R-sig-mixed-models Digest, Vol 136, Issue 26 *************************************************** ? Dr. Christoph Huber-Huber Center for Mind/Brain Sciences (CIMeC) University of Trento Corso Bettini 31 <https://maps.google.com/?q=Corso+Bettini+31+38068+Rovereto&entry=gmail&source=g> 38068 Rovereto <https://maps.google.com/?q=Corso+Bettini+31+38068+Rovereto&entry=gmail&source=g> (TN), Italy e-mail: christoph.huberhuber at unitn.it