[R-sig-ME] Distributional Assumption in lmer()

[Hedyeh Ahmadi]

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 using r-project.org> on behalf of Ben Bolker <bolkerb using mcmaster.ca>
Sent: Friday, February 9, 2024 1:34 PM
To: r-sig-mixed-models using r-project.org <r-sig-mixed-models using 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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Director, School of Computational Science and Engineering
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