[BioC] LIMMA No residual using rmaPLM

[Ron Ophir]

Thanks, Ben. I haven't seen your response probably due to problem in the
way I send my e-mail or the way my e-mail address is subbmited to the
server. Anyhow, what you mean that when lmFit identifies PLMset object
it requires weights either from the PLMset object or by the users. And
in the PLMset object that generated by rmaPLM does not supply weights.
Is that so?
Thanks,
Ron

II'd recommend that you use fitPLM rather than rmaPLM for these
purposes.The reason why it is giving "reasonable results" in the first
case andan error in the other case, is that it is using your user
suppliedweights in the earlier situation and trying to derive some
sensibleweights based on the PLM model fit. Unfortunately rmaPLM does
not returnthe information that is used to construct these but fitPLM
does.Ben

On Mon, 2005-09-05 at 13:51 +0300, Ron Ophir wrote:
> Hi
> I am analyzing affymetrix data having the following experimental>
design:> > analysis$design>                 Normal IOP IOPC
> L91RAE230 2.CEL      0   1    0
> L92RAE230 2.CEL      0   1    0
> L93RAE230 2.CEL      0   0    1
> L94RAE230 2.CEL      0   0    1
> L95RAE230 2.CEL      1   0    0
> L96RAE230 2.CEL      1   0    0
>  and following contrasts
> > analysis$contrasts
>        IOPC.IOP IOPC.Normal IOP.Normal> Normal        0          -1  
      -1> IOP          -1           0          1
> IOPC          1           1          0
> The preprocessing was done using affyPLM packge
> analysis$data<-rmaPLM(analysis$raw)> where analysis$raw is AfyyBatch
object. Then I fitted the the model> once with weighting matrix using
AMP flags
> fm<-as.matrix(analysis$Flags!="A")
>
analysis$fit<-lmFit(analysis$data,analysis$design,w=matrix(as.numeric(fm),dim(fm)[1],dim(fm)[2],dimnames=dimnames(fm)))>
  and got a reasonable results and once without this matrix. What was>
surprising is that fitting without the weighting matrix I got the
> following error message:
> > analysis$bayes<-eBayes(analysis$contrasts.fit)> Error in ebayes(fit
= fit, proportion = proportion, stdev.coef.lim =
> stdev.coef.lim) : 
>         No residual degrees of freedom in linear model fits> I've
checked the fit coeffecient matrix (analyis$fit object) and is all NA.
> Does someone have an idea why? I did not applied any weighting
matrix> for this fitting.
> Thanks
> Ron
> > >  > > 	[[alternative HTML version deleted]]
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