glmnet - variable importance?
<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>