Hi,
thanks a lot for your kind answer.
I have to specify an additional observation:
the "r"parameter is indeed a numeric variable and also in this situation the result is the same.

Would be reasonable to try and model the design as:
design<- <- model.matrix(~0+r)
#where "r"is a numeric variable?

for the points about the coefficients I have to reason about

Thanks a lot

Michela
Il giorno 15/apr/2014, alle ore 16:23, James W. MacDonald <jmacdon@uw.edu> ha scritto:

> Hi Michela,
> 
> On 4/15/2014 5:05 AM, michela riba [guest] wrote:
>> Hi,
>> I'm sorry for re-posting the message, but I cannot find it in the archive
>> Thanks a lot for attention
>> 
>> 
>> Hi,
>> I would like to model and retrieve differential expression data
>> regarding an experimental design in which different patients (9) have different disease classes (3 disease classes) and a feature represented with a percentage (0, 0.50, 0.75,1).
>>     some conditions are replicated 2 or 3 times, regarding the disease condition
>> Till now I have done an analysis considering Genotype and Disease in the model (as a paired samples analysis)
>> 
>> design <- model.matrix(~Genotype+Disease)
>> or
>> design <- model.matrix(~0+Genotype+Disease)
>> 
>> now I would like to model also considering
>> a continuous variable , namely r
>> 
>> this way: design <- model.matrix(~Genotype+Disease+r)
>> 
>> to see if differential gene expression between two classes of disease are correlated with the r status
>> 
>> but till now it is not possible to gain results
>> Coefficients not estimable: r0,5 r0,75 r1
>> Warning message:
>> Partial NA coefficients for 15246 probe(s)
> 
> This indicates that R is using your 'r' data as factor rather than numeric. I assume that is not what you want? If so you need to ensure that R thinks that 'r' is a numeric vector.
> 
> If you really are trying to treat 'r' as a factor, then note that you have either an over-specified model (meaning you are trying to estimate more parameters than you have observations), or that three of the coefficients for 'r' are linear combinations of existing coefficients when you already have genotype and disease in the model.
> 
> Best,
> 
> Jim
> 
> 
>> 
>> if I model
>> design <- model.matrix(~Disease+r)
>> it goes well, but  it would not consider the different genotypes
>> 
>> I thank you very much for attention
>> 
>> Thanks a lot
>> 
>> Michela
>> 
>>  -- output of sessionInfo():
>> 
>> R version 3.0.2 (2013-09-25)
>> Platform: x86_64-apple-darwin10.8.0 (64-bit)
>> 
>> locale:
>> [1] it_IT.UTF-8/it_IT.UTF-8/it_IT.UTF-8/C/it_IT.UTF-8/it_IT.UTF-8
>> 
>> attached base packages:
>> [1] stats     graphics  grDevices utils     datasets  methods   base
>> 
>> other attached packages:
>> [1] limma_3.18.13
>> 
>> loaded via a namespace (and not attached):
>> [1] tools_3.0.2
>> 
>> --
>> Sent via the guest posting facility at bioconductor.org.
>> 
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@r-project.org
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
> 
> -- 
> James W. MacDonald, M.S.
> Biostatistician
> University of Washington
> Environmental and Occupational Health Sciences
> 4225 Roosevelt Way NE, # 100
> Seattle WA 98105-6099
> 

Dr. Michela Riba
Genome Function Unit
Center for Translational Genomics and Bioinformatics
San Raffaele Scientific Institute
Via Olgettina 58
20132 Milano
Italy

lab: +39 02 2643 9114
skype: mic_mir32
riba.michela@gmail.com
riba.michela@hsr.it


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