# Guidance on prior predictive checks in ordinal model

**URL:** <https://discourse.mc-stan.org/t/guidance-on-prior-predictive-checks-in-ordinal-model/34532>\
**Category:** brms\
**Tags:** ecology, prior-predictive\
**Created:** [March 19, 2024, 8:27pm UTC](https://discourse.mc-stan.org/t/guidance-on-prior-predictive-checks-in-ordinal-model/34532 "2024-03-19T20:27:11Z")\
**Posts on this page:** 1\
**Showing post:** 28

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**Author:** ![natpac](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/natpac/32/12508_2.png) [@natpac](https://discourse.mc-stan.org/u/natpac)\
**Post date:** [March 6, 2025, 3:09pm UTC](https://discourse.mc-stan.org/t/guidance-on-prior-predictive-checks-in-ordinal-model/34532/28 "2025-03-06T15:09:23Z")

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Hello again,  
I am back to working on this project and have become more familiar with shrinkage priors. I am considering using the R2D2 prior (as suggested by [avehtari](https://discourse.mc-stan.org/u/avehtari)) on `b`, as it allows me to retain all predictors. I am using the default mean and precision of the Beta prior (also suggested in Yanchenko et al. 2024 - _The R2D2 Prior for Generalized Linear Mixed Models_), but I slightly increased the concentration to imply less shrinkage.  
`prior(R2D2(mean_R2 = 0.5, prec_R2 = 2, cons_D2 = 1), class = "b")`  
However, my predictors belong to different classes (numerical [standardized], categorical, and ordinal modeled as monotonic `mo()`) . I came across [R2D2M2 prior and monotonic predictors - #3 by Xavier\_La\_Rochelle](https://discourse.mc-stan.org/t/r2d2m2-prior-and-monotonic-predictors/36712/3) but I can’t dummy-code all my categorical/ordinal covariates (for convergence purpose).  
**My questions :**  
(1) Can I still interpret coefficients and conditional effects, given that not all predictors are on the same scale?  
(2) If yes, is there a general guideline on how to choose the concentration value?  
(3) Regarding the interpretation of the results (see below), some coefficients are centered on 0 (b\_nominalA\_1) which I interpret as shrunk and thus not meaningful. However, the coefficients that seem to have an impact (b\_nominalA\_2 and A3) are still slightly shrunk toward 0. I would have expected the shrinkage to either lead to coefficients centered on 0 (strong shrinkage) or leave them largely unaffected (slighty encompassing 0 to far from 0). Could someone clarify what I am missing here?

 ![R2D2_plot](https://canada1.discourse-cdn.com/flex030/uploads/mc_stan/original/3X/d/9/d918593d3705047491011d91c8ae99c425356a6b.png)

Thanks!

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