Precision about the glmer model for Bernoulli variables
Mixed models can assume negative correlations when you include something more than random intercepts. Check https://emcbiostatistics.shinyapps.io/Repeated_Measurements/ Chapter 3, Section 3.3 -> Select random intercepts & random slopes, and make the correlation between the intercepts and slopes negative. When including quadratic random slopes even get more negative correlations. Best, Dimitris ?? Dimitris Rizopoulos Professor of Biostatistics Erasmus University Medical Center The Netherlands
From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on behalf of David Duffy <David.Duffy at qimrberghofer.edu.au>
Sent: Wednesday, April 29, 2020 8:23:03 AM
To: Vaida, Florin <fvaida at health.ucsd.edu>; John Maindonald <john.maindonald at anu.edu.au>
Cc: r-sig-mixed-models at r-project.org <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Precision about the glmer model for Bernoulli variables
Sent: Wednesday, April 29, 2020 8:23:03 AM
To: Vaida, Florin <fvaida at health.ucsd.edu>; John Maindonald <john.maindonald at anu.edu.au>
Cc: r-sig-mixed-models at r-project.org <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Precision about the glmer model for Bernoulli variables
Hi Florin. > ...but negative correlations do not correspond to a mixed-effects model specification. (I thought Geert > Molenberghs had a paper to this point but I can't find it now.) Hopefully still vaguely R-related - in the case of meta-analyses of correlations, the observed correlation for a given, say, sub-study can be negative, and _some_ mixed models will inappropriately truncate this contribution at zero, leading to inflated estimates for the global parameters. This comes up when meta-analysing heritability, where the genetic model (as you have pointed out) contrains this to be non-negative for a single trait. Because of the computational difficulties, many geneticists still fit linear-normal mixed models to binary data (eg genome-wide association studies of large datasets eg UK Biobank), and don't usually get burnt. The "better" alternative for this has been PQL, implemented in several R packages. Cheers, David Duffy. _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-sig-mixed-models&data=02%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C5b7ae5701bf24a34e2a608d7ec05d2be%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C1%7C637237382072594631&sdata=O%2FiLczmjWal5AWB7GVxb0MFuotrQynWbWqOACIbNSgI%3D&reserved=0