Timeline for Cox regression with penalized package in R
Current License: CC BY-SA 3.0
11 events
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| Nov 21, 2017 at 15:38 | history | edited | kjetil b halvorsen♦ |
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| Sep 10, 2017 at 23:23 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Aug 7, 2017 at 0:20 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jul 3, 2017 at 16:33 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jun 1, 2017 at 12:36 | answer | added | Ivo | timeline score: 1 | |
| Mar 24, 2016 at 17:28 | comment | added | EdM | If you have enough events, there should be no problem with only 37 predictors. Just 740 events in your 32538 cases would give 20 events per predictor, a rule of thumb to avoid overfitting in Cox models. If I understand your data set correctly, that would only be about a 2% incidence of unemployment events; if you have even more events, all the better. Limited backwards step-down selection from your full model can be OK if you require parsimony and accept the tradeoff in accuracy; see page 131 of Harrell's rms course notes. | |
| Mar 24, 2016 at 15:12 | history | edited | GiannisZ | CC BY-SA 3.0 |
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| Mar 24, 2016 at 15:05 | comment | added | GiannisZ | I tried to use only 1 value of lambda with penalized(..., lambda=x ) . But again, it is efficient until N=12000. I have it running now at the full dataset, but does not seem to respond... I want to reduce them basically because I was told to do so, and secondly, as far as I know, 37 predictors are far too many for a cox regression.....Thanks!!! | |
| Mar 24, 2016 at 13:38 | comment | added | Scortchi♦ | You're trying to find a good penalty factor by searching over models: it might be an idea to start by seeing how long it takes to fit a single model with a given penalty factor. (Also, is the full model over-fitting in any case? - why do you want to reduce the number of predictors?) | |
| Mar 24, 2016 at 12:20 | review | First posts | |||
| Mar 24, 2016 at 12:57 | |||||
| Mar 24, 2016 at 12:16 | history | asked | GiannisZ | CC BY-SA 3.0 |