Hi Konika,

I opened up the URL you provided, hit Ctrl-F ('Find'), and typed in
'ANOVA'.  Two hits, one of which (on page 45) lands you directly onto the
section that you ought to read, namely, 'Factorial Designs', whereupon you
will note that there is an example of... two-way ANOVA (with contrasts and
extensive discussion of why one might want to formulate them in a specific
fashion).  From pages 45 through 47 there is an extremely thorough
discussion of this.

Gordon Smyth and the limma developers put an awful lot of time into writing
that documentation, the least people could do is read a few pages of it.
 Analysis of variance (ANOVA) assumes that a linear model with a Gaussian
error term can have the overall variance (goodness or lack of fit)
partitioned into differences that the model (with one or more factors)
captures, and differences that it doesn't.  If the proportion of the
variance in a data set that is captured by a factor of the model is a large
multiple of that which remains unexplained, then the factor is judged to
show significant differences between the groups it defines.  Hence, the
underlying assumption is that a linear model is adequate to model variation
in the data, connecting this to 'limma', or...

*LI*near *M*odels for *M*icro*A*rray data

If this doesn't make sense to you, please heed Vincent Carey's advice and
consult a statistician.  Statistics isn't a black box, but there are subtle
bits. Once you are comfortable with linear models and 'limma' (the
documentation of which is very well written), you will be better equipped
to answer much harder questions about sequencing data, isoform usage,
technical artifacts, dependent or correlated data, and other topics of
contemporary interest.

Hope this helps,

--t


On Mon, Mar 26, 2012 at 7:54 AM, konika chawla <chawla@bio.ntnu.no> wrote:

> Hi
> Sorry but if you mean
>
> http://bioconductor.org/packages/2.6/bioc/vignettes/limma/inst/doc/usersguide.pdf
> <
> http://bioconductor.org/packages/2.6/bioc/vignettes/limma/inst/doc/usersguide.pdf
> >
> There is nothing about using anova in this document.
> Could you please point me to another document, which explains ANOVA?
> Thanks
> Konika
>
>
> On 03/26/2012 03:57 PM, James W. MacDonald wrote:
> > Hi Konika,
> >
> > As Sean already mentioned, you might want to look at the limma
> > package. It seems you have not done so, in which case I will also
> > advise you to to do so.
> >
> > In particular, you should look at the limma User's Guide.
> >
> > Best,
> >
> > Jim
> >
> > On 3/23/2012 10:15 AM, konika chawla wrote:
> >> Hi
> >> I tried using the code below for the data design I have
> >> aof<- function(x) {
> >>     m<-data.frame(time, treatmentA, treatmentB, x);
> >>     anova(aov(x ~ time + treatmentA + treatmentB + time * treatmentA *
> >> treatmentB, m))
> >> }
> >> time<- factor(c(1,1,1,2,2,2,1,1,1,2,2,2,1,1,1,2,2,2))
> >>    treatmentA<-factor(c(1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0)) #control
> >> treatmentB<-factor(c(0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1)) #infected
> >> anovaresults<- apply(val, 1, aof)
> >>
> >> could you check if it is the correct way to put factors , based on the
> >> design mentioned previously.
> >> Also, It gives a huge file for each gene and the Fvalues and Pvalues.
> >> Wondering how to write the result in order to get P values for effect of
> >> treatmentB , or for checking if treatment A and B are same of different.
> >> Could you help
> >> Thanks
> >> Konika
> >>
>
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> 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
>



-- 
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>

	[[alternative HTML version deleted]]

