I need to find a computer-driven way to come up with a model in logistic regression for an exploratory study using R. Usually I would just use the leaps or bestglm packages to find the best subset, but I have more than 300 potential predictor variables so none of them can go through the 2^300 potential models.
Are there any other computer-driven methods to select covariates for logistic regression that someone might recommend? If you could suggest the R package that implements them would be ideal.
What I have been doing so far is running bestglm in groups of 10 predictors at a time, taking note of the ones that appear in the best models and hope that I can reduce the demand on computer power. Not ideal, but I don't know how else to tackle this.