[BioC] package of predicting a continuous variable from more than one continuous predictor variables

shirley zhang shirley0818 at gmail.com
Wed Sep 9 16:38:29 CEST 2009

Hi Steve,

Thanks for your explanation and suggestions. I don't know SVM can also
be used for regression since I only used it for classification.

I will try those methods you suggested. Do you have any experience with CART?

Thanks again,

On Wed, Sep 9, 2009 at 10:26 AM, Steve Lianoglou
<mailinglist.honeypot at gmail.com> wrote:
> Hi Shirley,
> On Sep 9, 2009, at 10:10 AM, shirley zhang wrote:
>> Thanks Steve.
>> Sorry that I did not make myself clear. I am trying to build a
>> biomarker from gene expression microarray data. What I am doing is
>> similar to the weighted-voting algorithm or SVM. But the difference is
>> that the outcome is a continuous variable instead of a categorical
>> variable.  It is a regression problem, but I want to know which
>> package is best for this purpose? How about CART?
> I don't know if there's such thing as "best"(?) What yard stick would you
> use to measure that?
> For instance, you mention "it" is similar to an svm (how?), but SVM's can
> also be used for regression, not just classification (doable from both e1071
> and kernlab). How about going that route? As usual, interpretation of the
> model might be challenging, though (which might be why you're avoiding it
> for biomarker discovery?)
> You also mention weighted-voting:
>  * how about boosted regression models?
>     http://cran.r-project.org/web/packages/gbm/index.html
>  * Also related to boosting: bagging & randomForests (both can be used for
> regression):
>     http://cran.r-project.org/web/packages/randomForest/index.html
>     http://cran.r-project.org/web/packages/ipred/index.html
> I think boosting/bagging/random-forests tend to lead to more interpretable
> models, so maybe that's better for you?
> There are also several penalized regression packages (also good for
> interpretability) for instance glmnet is great:
> http://cran.r-project.org/web/packages/glmnet/index.html
> Maybe you have some info about the grouping of your predictors? Try grouped
> lasso:
> http://cran.r-project.org/web/packages/grplasso/index.html
> -steve
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  |  Memorial Sloan-Kettering Cancer Center
>  |  Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact

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