[BioC] Limma User's Guide Example of design matrices

[kfbargad at ehu.es]

Dear Mike, 

I will leave the explanations to the experts, but as a beginner I 
found two books very useful in understanding linear models and 
contrasts:

Introductory Statistics with R, by Peter Dalgaard 

and

Design and Analysis of experiments, by Douglas C Montgomery

HTH,

David

> I am working my way through the Limma User's Guide and had a 
question  
> about the design matrices for the example in section 8.4 (2 groups,  
> same reference).
> I understand the difference between the two design matrices in 
terms  
> of what you can extract directly from the linear model and what has  
> to be obtained by contrasts and how you directly construct the  
> matrices using cbind as in the manual. I have two questions, one of  
> which may trivial (i.e., stupid), and the other not. I will preface  
> this by admitting that my knowledge of statistics beyond the very  
> basics is relatively weak.
> 
> The non-trivial question:
> 
> I realize that more than one design matrix can be set up to analyze  
> the same set of data (as in the example), and that similar results  
> should be obtainable with each design. If you are eventually  
> obtaining the same information from each design (i.e., identifying  
> differentially expressed genes) what is the benefit of one design  
> over the other- could one design produce a different level of  
> statistical confidence that a given set of genes is differentially  
> regulated? Is there any rule of thumb for choosing one design 
matrix  
> over another?
> 
> The trivial (?) question
> 
> I set up the two types of design matrices using the factor Group 
and  
> the model.matrix function as in the manual:
> 
>  > Group-> factor(c("WT","WT","MU","MU","MU"),levels=c("WT","MU"))
>  > Group
> [1] WT WT MU MU MU
> Levels: WT MU
>  > design-> model.matrix(~Group)
>  > design
>    (Intercept) GroupMU
> 1           1       0
> 2           1       0
> 3           1       1
> 4           1       1
> 5           1       1
> attr(,"assign")
> [1] 0 1
> attr(,"contrasts")
> attr(,"contrasts")$Group
> [1] "contr.treatment"
> 
>  > design2-> model.matrix(~0+Group)
>  > design2
>    GroupWT GroupMU
> 1       1       0
> 2       1       0
> 3       0       1
> 4       0       1
> 5       0       1
> attr(,"assign")
> [1] 1 1
> attr(,"contrasts")
> attr(,"contrasts")$Group
> [1] "contr.treatment"
> 
> 
> I have not been able to find a clear explanation of what the tilde  
> (~)  does in model.matrix to produce the design matrix, especially 
in  
> the context of "~0+Group." Any idea as to where  I can get an  
> explanation of how this works? (The 2445-page R manual wasn't any  
> help!).
> 
> Thanks for you help!
> 
> Mike White
> 
> 
> 
> Michael M. White, Ph.D.
> Department of Pharmacology & Physiology
> MS #488
> Drexel University College of Medicine
> 245 N. 15th Street
> Philadelphia, PA 19102-1192
> 
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: 
http://news.gmane.org/gmane.science.biology.informatics.conductor
>