We used the same simulation setup as above, except that we took the continuous outcome y, defined p = 1/(1 + exp(−y)) and used this to generate a two-class outcome z with Prob(z = 1) = p, Prob(z = 0) = 1 − p. We compared the speed of glmnet to the interior point method l1lognet proposed by Koh et al. [2007], Bayesian Logistic Regression (BBR) due to Genkin et al. [2007] and the Lasso Penalized Logistic (LPL) program supplied by Ken Lange [Wu and Lange, 2008b]. The latter two methods also use a coordinate descent approach.
