Distributional Assumption in lmer()
For what it's worth I think you can probably also do this in brms, if you want to go down the Bayesian rabbit hole ...
On 2024-02-09 5:01 p.m., Hedyeh Ahmadi wrote:
Thank you for the quick and informative reply. Best, Hedyeh Ahmadi, Ph.D. Statistician Keck School of Medicine Department of Preventive Medicine University of Southern California LinkedIn www.linkedin.com/in/hedyeh-ahmadi<http://www.linkedin.com/in/hedyeh-ahmadi>
________________________________
From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on behalf of Ben Bolker <bolkerb at mcmaster.ca>
Sent: Friday, February 9, 2024 1:34 PM
To: r-sig-mixed-models at r-project.org <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Distributional Assumption in lmer()
No, but:
(1) glmmTMB has this (family = t_family)
(2) you can achieve a similar goal with the robustlmm package
cheers
Ben Bolker
On 2024-02-09 3:42 p.m., Hedyeh Ahmadi wrote:
Hello All,
I was wondering if there is a way to implement t-distribution assumption instead of family ="Gaussian" assumption in the lmer() function.
I am asking since I have been seeing heavy tails in my outcomes and it shows up in my residual diagnostic QQplot hence I think a t-distribution would be more appropriate compared to Normal distribution.
Any help would be greatly appreciated.
Best,
Hedyeh Ahmadi, Ph.D.
Statistician
Keck School of Medicine
Department of Preventive Medicine
University of Southern California
LinkedIn
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