[R] question on lasso

[oslo]

Hi Dr. Franggakis;
This can be explained because of collinearity and suppressor variable in multiple regression models. In the first scenario, you have both correlated variables and suppressor variables in the second scenario you do not have this problem. I do wonder why to do not use the scale elastic net for this particular problem.
Good luck,Oslo 

    On Saturday, November 5, 2016 4:29 PM, Constantine Frangakis <cfranga1 at jhu.edu> wrote:
 

 I would appreciate any comments to the following question.
I am trying to build a model for survival based on 155 patients and 70 covariates using lasso. Lasso picks, three variables only, say X1,X2,X3, and  omits the others. I wanted to check why a particular (clinically important) variable, say X4, is omitted by lasso. One of the things I did was I ran lasso on X1,X2,X3 and X4 only. The results (coefs) I get are different from running all 70 variables, and in fact now X4 is not omitted.
Why is that ? should it not be that the global (among all 70 variables) optimum, which is X1,X2,X3 and not X4, be also the local (among the four only) optimum ?
Thank you for your consideration


Constantine Frangakis, PhD
Professor
Departments of Biostatistics
Psychiatry, and Radiology
Johns Hopkins University






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