[R-sig-ME] Mixed model specification (control for location and repeated sampling of same location through time)

[Norman DAURELLE]

Dear Paul, 

thanks ! 

Norman 


De: "Paul Johnson" <paul.johnson using glasgow.ac.uk> 
À: "Norman DAURELLE" <norman.daurelle using agroparistech.fr>, "Thierry Onkelinx" <thierry.onkelinx using inbo.be> 
Cc: "r-sig-mixed-models" <r-sig-mixed-models using r-project.org>, "Brian Gill" <briangillphd using gmail.com> 
Envoyé: Mardi 8 Novembre 2022 16:23:43 
Objet: Re: [R-sig-ME] Mixed model specification (control for location and repeated sampling of same location through time) 

Hi Norman, 

The minimum number of blocks/groups required to support a random effect is discussed in Ben Bolker's GLMM FAQ wiki: 

https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#should-i-treat-factor-xxx-as-fixed-or-random 

"One point of particular relevance to ‘modern’ mixed model estimation (rather than ‘classical’ method-of-moments estimation) is that, for practical purposes, there must be a reasonable number of random-effects levels (e.g. blocks) – more than 5 or 6 at a minimum." 

Best wishes, 
Paul 

Paul Johnson 
Senior Lecturer 
School of Biodiversity, One Health and Veterinary Medicine 
University of Glasgow 
Room 362, Wolfson Link Building 
Glasgow G12 8QQ 
+44 (0)7814 668 613 
paul.johnson using glasgow.ac.uk 
https://www.gla.ac.uk/schools/bohvm/staff/pauljohnson/ 
https://orcid.org/0000-0001-6663-7520 

On 08/11/2022, 15:15, "R-sig-mixed-models on behalf of Norman DAURELLE via R-sig-mixed-models" <r-sig-mixed-models-bounces using r-project.org on behalf of r-sig-mixed-models using r-project.org> wrote: 


Dear list members, Brian, Thierry, 

I am not an expert, but I don't see why the number of sites would be a barrier to introducing it as a random effect. 

Would you care to explain the reasoning behind that statement ? 

To me, the Y ~ X1 + X2 + X3 + (1 | Site) part seems appropriate (I don't know about how to use the different dates, though). 

Sorry if this is not helpful, Brian. 

Cheers, 

Norman 




De: "Thierry Onkelinx via R-sig-mixed-models" <r-sig-mixed-models using r-project.org> 
�: "Brian Gill" <briangillphd using gmail.com> 
Cc: r-sig-mixed-models using r-project.org 
Envoy�: Jeudi 3 Novembre 2022 14:45:01 
Objet: Re: [R-sig-ME] Mixed model specification (control for location and repeated sampling of same location through time) 

Dear Brian, 

You have only 3 sites. That is too few to use as a random effect. 

Look into glmmTMB and INLA. They provide correlated random effects. Which 
is relevant for your Date variable. 

The glmmTMB formula might look like this: Y ~ Site + X1 + X2 + X3 + 
ar1(Date | Site) 
The INLA formula: Y ~ Site + X1 + X2 + X3 + f(Date, model = "rw1", 
replicate = as.integer(Site)) 

Best regards, 

ir. Thierry Onkelinx 
Statisticus / Statistician 

Vlaamse Overheid / Government of Flanders 
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND 
FOREST 
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance 
thierry.onkelinx using inbo.be 
Havenlaan 88 bus 73, 1000 Brussel 
www.inbo.be 

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Op ma 31 okt. 2022 om 18:55 schreef Brian Gill <briangillphd using gmail.com>: 

> I have three locations (Sites) where I repeatedly measured a number of 
> environmental variables (X1, X2, X3) and a response (Y; normally 
> distributed) over time. That is, I have data on each environmental variable 
> and the response at many time points for each of 3 sites. For each 
> timepoints all three sites were sampled. 
> 
> I want to model the response (Y) as a function of the environmental 
> variables (X1, X2, X3) while controlling for effects of Sites and Time. I
> expect responses from the same site to be similar because they come from
> the same location and responses measured at closer timepoints to be more
> similar than those separated by more time. 
> 
> Can people please advise on an appropriate model specification. 
> 
> I've come up with the following so far: 
> 
> Y ~ Site + X1 + X2 + X3 + (1 | Date) 
> 
> Y ~ X1 + X2 + X3 + (1 | Site) + (1 | Date) 
> 
> My hangups are that I think these models treat Date categorically 
> (controlling for variation from a particular date, but not how close or far 
> dates are from each other). Also, a model allowing both random intercepts
> and slopes might be better as responses could vary significantly in 
> magnitude and direction among sites. 
> 
> Any advice would be appreciated. Thanks! 
> 
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