Dear Bioconductors, I'm analyzing a microarray data set with three factors; cancer (yes or no, the interesting one), age and % tumour cells in sample. I'm interested to know how much each factor/coefficent is able to describe the overall variation in the data set. My googlefu turns up little of interest, could someone suggest a suitable package or some key words that could take me further? Cheers, Rob [[alternative HTML version deleted]]

Perhaps and R-squared change test (also known as an F-change test) may be useful. It will allow you to compare two models, i.e. one with and one without your co-efficient of interest and tell you if the fit is improved. Can be implemented using the anova() function in R. I.e. anova(fullModel, reducedModel) I'm fairly sure is the correct syntax. Paul. On Mon, Mar 25, 2013 at 2:58 PM, Robert Svensen <svensen.robert at="" gmail.com=""> wrote: > Dear Bioconductors, > > I'm analyzing a microarray data set with three factors; cancer (yes or no, > the interesting one), age and % tumour cells in sample. > > I'm interested to know how much each factor/coefficent is able to describe > the overall variation in the data set. > > My googlefu turns up little of interest, could someone suggest a suitable > package or some key words that could take me further? > > Cheers, > Rob > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Dr. Paul Geeleher, PhD (Bioinformatics) Section of Hematology-Oncology Department of Medicine The University of Chicago 900 E. 57th St., KCBD, Room 7144 Chicago, IL 60637 -- www.bioinformaticstutorials.com

Hi, The library PVCA can (at least partially) do this: unfortunately, AFAIK, only variation due to categorical variables can be analyzed (thus not continuous ones). http://www.bioconductor.org/packages/2.12/bioc/html/pvca.html Regards, Guido -----Original Message----- From: bioconductor-bounces@r-project.org [mailto:bioconductor- bounces@r-project.org] On Behalf Of Robert Svensen Sent: Monday, March 25, 2013 20:58 To: bioconductor at r-project.org Subject: [BioC] Estimate how much a factor contributes to the variation Dear Bioconductors, I'm analyzing a microarray data set with three factors; cancer (yes or no, the interesting one), age and % tumour cells in sample. I'm interested to know how much each factor/coefficent is able to describe the overall variation in the data set. My googlefu turns up little of interest, could someone suggest a suitable package or some key words that could take me further? Cheers, Rob [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at r-project.org https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor