title: glmnet - variable importance?
body: <p>I´m using the glmnet package to perform a LASSO regression. Is there a way to get the importance of the individual variables that were selected? I thought about ranking the coefficients that were obtained through the coef(...) command (i.e. the greater the distance from zero the more important a variable would be). Would that be a valid approach?</p>
  
  <p>Thanks for your help!</p>
  
  <pre><code>cvfit = cv.glmnet(x, y, family = "binomial")
  coef(cvfit, s = "lambda.min")
  
  ## 21 x 1 sparse Matrix of class "dgCMatrix"
  ##                    1
  ## (Intercept)  0.14936
  ## V1           1.32975
  ## V2           .      
  ## V3           0.69096
  ## V4           .      
  ## V5          -0.83123
  ## V6           0.53670
  ## V7           0.02005
  ## V8           0.33194
  ## V9           .      
  ## V10          .      
  ## V11          0.16239
  ## V12          .      
  ## V13          .      
  ## V14         -1.07081
  ## V15          .      
  ## V16          .      
  ## V17          .      
  ## V18          .      
  ## V19          .      
  ## V20         -1.04341
  </code></pre>