R help - Mar 2007 - Model selection in LASSO (cross-validation)

Hi, I know how to use LASSO for model selection based on the Cp criterion.  
I heard that we can also use cross validation as a criterion too.  I used 
cv.lars to give me the lowest predicted error & fraction.  But I'm short
of
a step to arrive at the number of variables to be included in the final 
model.  How do we do that?  Is it the predict.lars function?  i tried > 
logprostate.plars.cv=predict.lars(logprostate.lars.cv, M, type =
"fit",
mode="fraction") but it gives me error message:
Error in dim(data) <- dim : attempt to set an attribute on NULL.  Please 
help!

thanks!

_________________________________________________________________
With tax season right around the corner, make sure to follow these few 
simple tips.

Hi, I know how to use LASSO for model selection based on the Cp criterion.  
I heard that we can also use cross validation as a criterion too.  I used 
cv.lars to give me the lowest predicted error & fraction.  But I'm short
of
a step to arrive at the number of variables to be included in the final 
model.  How do we do that?  Is it the predict.lars function?  i tried > 
logprostate.plars.cv=predict.lars(logprostate.lars.cv, M, type =
"fit",
mode="fraction") but it gives me error message:
Error in dim(data) <- dim : attempt to set an attribute on NULL.  Please 
help!

Thanks!

_________________________________________________________________
The average US Credit Score is 675. The cost to see yours: $0 by Experian.