[R-sig-ME] Distributional Assumption in lmer()
Ben Bolker
bo|kerb @end|ng |rom mcm@@ter@c@
Fri Feb 9 22:34:30 CET 2024
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
> www.linkedin.com/in/hedyeh-ahmadi<http://www.linkedin.com/in/hedyeh-ahmadi>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
--
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
Associate chair (graduate, math), Mathematics & Statistics
> E-mail is sent at my convenience; I don't expect replies outside of
working hours.
More information about the R-sig-mixed-models
mailing list