Hi, All

I am trying to fit a lasso model using 7 variables for the outcome. 
x is the matrix with 7 variables, y is the outcome. 
  object = lars(x = x, y = y, type = "lasso")
  plot(object)

it plots out the coefficients as a function of beta/max(beta). There are several vertical lines in the plot that marks how many variables are chosen, there is one line which has larger width than others, does that correpsond to the optimal shrinkage amount selected by cross-validation?

In addition, what does Cp mean in the following results?

LARS/LASSO
Call: lars(x = x, y = y, type = "lasso")
  Df    Rss      Cp
0  1 24.623 54.7252
1  2 18.427 10.7457
2  3 18.398 12.5317
3  4 18.134 12.5699
4  5 17.518 10.0019
5  6 17.132  9.1379
6  7 17.014 10.2583
7  8 16.440  8.0000

Sorry for the long question, the final one is:

from the attributes of object by fitting lasso, 

attributes(object)
$names
 [1] "call"       "type"       "df"         "lambda"     "R2"        
 [6] "RSS"        "Cp"         "actions"    "entry"      "Gamrat"    
[11] "arc.length" "Gram"       "beta"       "mu"         "normx"     
[16] "meanx"     

which variables can be used to determine what are those selected variables? 

Thanks a lot!

James

       
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