New Feature: Fix and improve coeftable for optimize() output

[DominiqueMakowski]

This PR aims at the following:

• Fixes coeftable on optimize output (MLE and MAP) throws error #2033 by converting the symbols to String
• Following this comment, it would be super nice to add indices typically present in coeftables, such as p-values and confidence intervals. I have added (commented out) the code to do so, and I'm happy to include it.
• I would also suggest to rename the columns to make it more comparable to a regular coeftable

coeftable(GLM.lm(@formula(y ~ x), DataFrame(x=x, y=y)))

────────────────────────────────────────────────────────────────────────
                 Coef.  Std. Error     t  Pr(>|t|)  Lower 95%  Upper 95%
────────────────────────────────────────────────────────────────────────
(Intercept)  22.2225      3.59373   6.18    <1e-06  14.9474    29.4977
x             0.482927    0.152752  3.16    0.0031   0.173697   0.792157
────────────────────────────────────────────────────────────────────────

• However, I would make one exception: rename t as z because as far as I understood this is a z approximation, not a t-based estimation. I know that in some cases, the parameter is named t even though it's a z, but I think we could avoid confusion by being transparent

Note: the only changes were done for StatsBase.coeftable, the rest is formatting fixes (removal trailing spaces) done by the autoformatter.

Let me know if I should add the suggested changes :)

[yebai]

Thanks, @DominiqueMakowski -- it looks like very sensible improvements!

[DominiqueMakowski]

Done! I've exposed the level parameters to match StatsBase

Also, but this is more a question, would it be in principle feasible to add a predict() method to predict data based on the MLE estimates?

[yebai]

Also, but this is more a question, would it be in principle feasible to add a predict() method to predict data based on the MLE estimates?

There have been discussions about adding predict to Turing, although that PR has not been merged yet. Feel free to open another PR for predict to Turing.jl in the meantime.

cc @sethaxen

[torfjelde]

FYI we already have a predict @yebai , it just doesn't do what @DominiqueMakowski mentioned:) But yeah, such a method would be nice to add!

[DominiqueMakowski]

Feel free to open another PR for predict to Turing.jl in the meantime.

That's probably beyond my skills, I'm not even sure where I'd need to start

[torfjelde]

This looks good to me:) Good stuff @DominiqueMakowski !

I'm letting someone else hit the "approve" button though, as I was not particularly invovled in the original impl of this mode-estimation stuff, and so don't know if there are any particular concerns regarding changing the outputs (though from a quick glance, I agree with what you're saying, so it all seems sensible to me).

@cpfiffer you have any thoughts?

[sethaxen]

There have been discussions about adding predict to Turing, although that PR has not been merged yet. Feel free to open another PR for predict to Turing.jl in the meantime.

It may have been a mistake to try to get the predict overload into AbstractPPL. The original issue was TuringLang/DynamicPPL.jl#466.

[torfjelde]

Btw @DominiqueMakowski can you bump the patch version?

[DominiqueMakowski]

Thanks @devmotion !

Btw @DominiqueMakowski can you bump the patch version?

How do I do that?

[coveralls]

Pull Request Test Coverage Report for Build 5510378272

• 0 of 11 (0.0%) changed or added relevant lines in 1 file are covered.
• No unchanged relevant lines lost coverage.
• Overall coverage remained the same at 0.0%

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Changes Missing Coverage	Covered Lines	Changed/Added Lines	%
src/modes/OptimInterface.jl	0	11	0.0%

Totals
Change from base Build 5510129926:	0.0%
Covered Lines:	0
Relevant Lines:	1448

---

💛 - Coveralls

[codecov]

Codecov Report

Patch and project coverage have no change.

Comparison is base (90e1d21) 0.00% compared to head (b27b394) 0.00%.

Additional details and impacted files

@@          Coverage Diff           @@
##           master   #2034   +/-   ##
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  Coverage    0.00%   0.00%           
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  Files          22      22           
  Lines        1444    1448    +4     
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- Misses       1444    1448    +4     

Impacted Files	Coverage Δ
src/modes/OptimInterface.jl	0.00% <0.00%> (ø)

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[cpfiffer]

This is fantastic thank you for doing this! I left one minor comment but for the most part I'd be happy to merge this after the tests pass.

[DominiqueMakowski]

but for the most part I'd be happy to merge this after the tests pass.

We bumped the version, I'm not sure the failing tests are related to this PR

[cpfiffer]

I'm on my phone, could someone tag the registrator?

[yebai]

@cpfiffer Just tagged a release.