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Adding default priors for Binomial/Bernoulli families with logit link #830
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #830 +/- ##
==========================================
- Coverage 90.01% 89.82% -0.20%
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Files 47 47
Lines 3967 3991 +24
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+ Hits 3571 3585 +14
- Misses 396 406 +10 ☔ View full report in Codecov by Sentry. |
Hi @julianlheureux, thanks a lot for the contribution and sorry for the delay in the review. Could you also re-run a few of the examples using the affected families so the docs are also updated? With that, I think we could merge this. |
Hello! I think I successfully made the changes you highlighted. |
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Hi Julián, thanks for changes. I just suggested a few extra comments.
Co-authored-by: Tomás Capretto <tomicapretto@gmail.com>
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I think the comments are appropriate! Thanks for helping make the code clearer and cleaner!
But there is a problem with the comments. There are some tests that fail becouse the line with the comment is too long. We have to make this shorter...
@julianlheureux I see everything green now, so I assume you fixed the issue related to the comment length, right? |
I think i managed to fix it. If you want, you can check the changes in the last commits I made, making those lines shorter. |
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Hello! I am working on improving default priors.
Starting with some small steps, I did some examples of logistic regression models with logit link. Linear predictor for weakly informative priors should be within -5 and 5. So I tried to improve the prior scaler, with some conditionals looking for these cases.
Setting the intercept prior to N(0,1.5), and common terms to N(0, 1/sd(x_i)). When using categoric variables, priors will be set to N(0,1).
Doing these changes, we arrive to weakly informative priors (at least, better than the originals).