[R] Efficient algorithm to get a solution path for ridge regression?

Kenneth Lo kenchlo2 at gmail.com
Thu Apr 15 20:01:24 CEST 2010

Thanks for your pointer. I looked into "Applied Regression Analysis"  
by Draper and Smith, and the ridge trace solution can be obtained  
efficiently by expressing it in canonical form. It could be coded  
without much difficulty, but I'm just wondering if there's any package  
which has already implemented this (or other) efficient way of  
returning a solution path for a sequence of regularization parameter?  
The function provided by MASS computes the solution individually for  
each value of the parameter. A quick look into the parcor and  
penalized packages seems to be promising, but I need to check the  
source code to see if they really implement an efficient algorithm as  
desired. Any input to this would be much appreciated.

Thanks again.

On 15-Apr-10, at 8:49 AM, Charles C. Berry wrote:

> On Wed, 14 Apr 2010, Kenneth Lo wrote:
>> With the use of the LARS algorithm, a path of solutions  
>> corresponding to a sequence of the regularization parameter can be  
>> obtained for LASSO (or even the elastic net, a hybrid between LASSO  
>> and ridge) at the cost of one linear regression. In terms of  
>> computational speed LASSO seems to have beaten ridge regression,  
>> the solution of which needs to be computed individually, at the  
>> cost of one linear regression, for each regularization parameter.  
>> Is there any efficient method to compute a path of solutions for  
>> ridge regression corresponding to a sequence of the regularization  
>> parameter? Thanks.
> Yes.
> Check a textbook like Draper and Smith. Or Google for course notes.
> HTH,
> Chuck
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
> Charles C. Berry                            (858) 534-2098
>                                            Dept of Family/Preventive  
> Medicine
> E mailto:cberry at tajo.ucsd.edu	            UC San Diego
> http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego  
> 92093-0901

More information about the R-help mailing list