Update "body fat" to projpred v2.8.0 #6
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experimental in projpred (see stan-dev/projpred#94). Setting `nloo = n` should not change results compared to omitting argument `nloo`, but I guess it's better not to point to an experimental feature where not needed.
projpred v2.1.0 back to the matrix that `fitrhs_cvvs[["pct_solution_terms_cv"]]` delivered before.
``` Warning message: Continuous limits supplied to discrete scale. Did you mean `limits = factor(...)` or `scale_*_continuous()`? ```
…s not need to be set after a call to `set.seed()`, but I add it here so that the `cv_varsel()` command can be re-run easily without having to re-run all the code from the previous `set.seed()` call on).
introduces a "TODO" with respect to `nloo`.
introduces a "TODO" with respect to `nloo`.
implemented in projpred. This resolves the `nloo` "TODO" added earlier.
implemented in projpred. This resolves the `nloo` "TODO" added earlier.
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PR stan-dev/projpred#471 implements what was missing for lines https://github.com/fweber144/modelselection/blob/9e65616c6bf9e234747b2e97a3f602cb5c264c92/bodyfat_kfoldcv.R#L29-L32. If I have the time, I can try to make use of the new |
Changes not related to projpred v2.8.0: 1) Object `df` (files `bodyfat_bootstrap.R`, `bodyfat_kfoldcv.R`, and `bodyfat_kfoldcv2.R`) is modified for consistency with file `bodyfat.Rmd`. 2) Objects consisting of a model formula are coerced to `formula`s because otherwise, `projpred:::get_refmodel.stanreg()` throws an error.
Done now in commit 1f010f1 (with projpred 2.8.0 which has just been released on CRAN). See the commit message of that commit for some more details. |
fweber144
changed the title
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Uh, I never received any notification about this. I'll check this in next couple weeks |
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Following upon stan-dev/projpred#311, this PR shows how to adapt the "body fat" case study code so that it can be used with the most recent CRAN version of projpred (v2.1.1). Note that I did not run the code from
bodyfat_bootstrap.R,bodyfat_kfoldcv.R, andbodyfat_kfoldcv2.Rbecause they are probably going to take very long. For the same reason, I also did not run the secondcv_varsel()call inbodyfat.Rmd. I also only looked at the "body fat" case study. Other case studies probably have to be adapted, too.I also noticed uses of
nlooinbodyfat_kfoldcv.Randbodyfat_kfoldcv2.Rwhich were probably not doing what they were supposed to. I now switched to projpred's natively supported K-fold CV because I guess that's what those lines were supposed to do.See the commit messages for details, but feel free to ask if you need more information.