[R-sig-ME] Reference ?

[Chris Howden]

Hi Sacha,

I'm not sure I really agree with that statement, although some may. 

Say your sample size was really small, only 6. A simple model may still fit, but I wouldn’t expect the parameters to be particularly stable. Only 1 or 2 different datum could change everything. 

I would also want to consider the SE of the parameter estimates. If they are very large (compared to the parameter estimates), then this is telling me the parameter estimates aren't very stable. Even though the model converged. 

Chris Howden B.Sc. (Hons)
Founding Partner
Data Analysis, Modelling and Training
Evidence Based Strategy/Policy Development, IP Commercialisation and Innovation
(mobile) +61 (0) 410 689 945 | (skype) chris using trickysolutions.com.au

-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> On Behalf Of Fernando Pedro Bruna Quintas
Sent: Sunday, 5 December 2021 6:55 AM
To: R-sig-mixed-models <r-sig-mixed-models using r-project.org>; varin sacha <varinsacha using yahoo.fr>
Subject: Re: [R-sig-ME] Reference ?


Dear SV,

I can feel that you have a very promising research career just in front of your eyes.

FB

________________________________
De: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> en nombre de varin sacha via R-sig-mixed-models <r-sig-mixed-models using r-project.org>
Enviado: s bado, 4 de diciembre de 2021 20:47
Para: R-sig-mixed-models <r-sig-mixed-models using r-project.org>
Asunto: [R-sig-ME] Reference ?

Dear Mixed modelers experts,

I am looking for a reference to justify my sentence here below.
Many thanks for your help.

"The mixed model seemed well specified   it converged and had no singular problem, no overfitting problem. So, even if the sample size is quite small, the estimates are stable and can be trusted".

Best Regards,
SV


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