[R-sig-ME] ordinal mixed model - which one to use?

[Diana Michl]

Dear List, dear Thierry,

thank you for pointing out my formatting got screwed up and still 
fighting your way through! I'm resending my email below. Complete 
separation: Well, not quite, but I do have few cases with few cells:



Conceptually, this is wanted and makes perfect sense. If this is the 
reason, I'm not sure what to do. It still seems strange to me that 
because one's cases are pretty straight forward and results are, too, 
this should make modelling so difficult or impossible... Thank you and 
kind regards
> Dear Diana,
>
> Posting in HTML makes the R output very hard to read.
>
> The first thing that I do when I'm confronted with such large
> coefficients is checking for quasi-complete separation.
>
> Best regards,
>
> Thierry
>
> 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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> more than asking him to perform a post-mortem examination: he may be
> able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
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> not ensure that a reasonable answer can be extracted from a given body
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-------- Weitergeleitete Nachricht --------
Betreff: 	ordinal mixed model - which one to use?
Datum: 	Tue, 29 May 2018 20:30:47 +0200
Von: 	Diana Michl <dmichl using uni-potsdam.de>
An: 	r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>



Dear List,

I'm fitting ordinal mixed models with package {ordinal}. I have a clmm 
with 1 predictor (fixed effect, factor with 2 levels "woe" and "meta"), 
2 random effects, and an ordinal outcome, ratings from 1-4. Items=82, 
n=26. My question: Do I use

link="logit" or link="cloglog"? Or something else all together?

For all I know, cloglog is rather used when higher outcomes are more 
likely, but it also depends on the model fit. I thought cloglog made 
sense here b/c I have 53 cases of "woe" and 29 cases of "meta". "woe" 
are conceptually more likely to be rated as 4 or 3 (higher events).
If this is incorrect, please correct me.

In my logit model, I get a ridiculously huge odds ratio - but much 
better fit.
In my cloglog model, the odds ratio is still worryingly large, but less 
a tenth, while the fit is much worse. I post the outputs below.

A few remarks: Overall, I don't understand the huge OR. I have an 
extremely similar dataset (items=80, n=28) where the OR with the logit 
model are just 4.7 and the cloglog OR are only 2.73. So that seems fine. 
The difference between dataset 2 and the problematic one is the means: 
Their difference is much bigger in the problematic dataset:

#mean of typ meta = 1.27

#mean of typ woe = 3.42

as opposed to dataset 2:

#mean of typ meta = 2.35

#mean of typ woe = 3.02

cloglog model:


comparison:


My sd seems fine at 1.26. Checking for outliers and several model 
assumptions isn't possible for a clmm.

Thanks very much in advance for any input

-- 
Diana Michl