title: Variable selection methods for big sparse data in R with many NULL values
body: <p>I am working with datasets with at least 100K rows and at least 10K variables. There are a considerable amount of NULL values. Most of the variables are numeric with a handful being categorical. </p>
  
  <p>I want to use something like randomForest for variable selection, but of course the NAs are a problem.</p>
  
  <p>What are some robust variable selection methods for such data?</p>