[R-sig-ME] [EXT] Re: Too high condition R-square value - beta family

[Daniel Lüdecke]

It can be that the calculation of the random effects variances is not accurate. The code in the *insight* package (which is used by performance::r2()) has "mu * (1 - mu) / (1 + phi)" to calculate the distributional variance; glmmTMB::beta_family()$variance, however, returns "mu * (1 - mu)". The docs in ?glmmTMB::beta_family, again, say: "Beta distribution: parameterization of Ferrari and Cribari-Neto (2004) and the betareg package (Cribari-Neto and Zeileis 2010); V=μ(1−μ)/(ϕ+1)" (which is what is used in *insight*).

I'm not sure that this is the issue, but it might be. Would be good to know which of the two formulas is the correct / more accurate one.

-----Ursprüngliche Nachricht-----
Von: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> Im Auftrag von Ben Bolker
Gesendet: Dienstag, 29. November 2022 22:01
An: r-sig-mixed-models using r-project.org
Betreff: [EXT] Re: [R-sig-ME] Too high condition R-square value - beta family

    Thanks.  Can you please post the results of summary() applied to 
your fitted model?  That could give us some more clues ...

On 2022-11-29 3:41 PM, camille.montalcini using unibe.ch wrote:
> Dear list members,
>
> I am using glmmTMB to fit a beta family (with log link) to some proportion data (varying from 0-1, which I rescaled from 0.01 to 0.99).  I have two continuous rescaled predictors (including a time variable) and a binary treatment predictor. My only goal is to assess if there is any treatment effect (i.e. not to make predictions, so maybe overfitting is less of an issue here). As random effect I have my individuals ID (~160 individuals, and around 28 observations per individuals). The model fits reasonably well, but the main issue is that I get a very high conditional R-square: 0.986 (from: performance::r2(fit)) (marginal: 0.034) with the warning: "mu of 0.6 is too close to zero, estimate of random effect variances may be unreliable".
>
> I tried many thing, including checking if the model is singular (performance::check_singularity())) and it appeared not to be, removing the fixed effects does not change anything either, shuffling the individualsID lead too conditional R-squared around 0.25, removing hens with random intercept mode in the extreme did not change anything either (though model fits generally better). Visualising the data, reveals the individuals to be indeed quite consistent, but likely not up to the level that we could explain 98.7% of the variance, so I am quite confident the model is not reliable. Its the first time I am using beta regression and I feel that I am missing an important point here, any insight would be greatly appreciated!
>
> Best,
> Camille
>
>
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>
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