4 ms·

Glmnet and Cox proportional hazards regression (survival analysis) are two recent examples I came across missing Python implementations.

• deleted 10y ago
  [deleted]
• eric_bullington 10y ago
  Did you look in statsmodels? I appreciate the suggestions, and for a moment I was hopeful about the need for survival analysis models, but it looks like both that and GLM are well-covered in the latest version of statsmodels (don't be misled by the old sourceforge site, there's been a huge flurry of recent activity in statsmodels, hundreds of new PRs merged, look at Github and the docs site linked from that repo: http://www.statsmodels.org/stable/ http://www.statsmodels.org/stable/). There's even a Jupyter notebook comparing the R, Stata (that takes me back, used Stata in survival analysis class 10 years ago), and Python versions of proportional hazards regression: http://nbviewer.jupyter.org/urls/umich.box.com/shared/static/epie6pcdk1rgb10zcd5v.ipynb http://nbviewer.jupyter.org/urls/umich.box.com/shared/static...
  • hcarvalhoalves 10y ago
    Last time we tried Cox PH regression from statsmodel it gave a (very) different result than R's, and we weren't comfortable with the lack of tests. Will give another try.
  • rm999 10y ago
    Glmnet has quite a bit of functionality that is lacking in the Python elastic net implementations. The most notable is the regularization parameter sequence grid search (alpha in statsmodel, lambda in glmnet) which works remarkably well and can be orders of magnitude faster than a traditional grid search.