[BioC] mean versus linear model?

[Naomi Altman]

I am not quite sure what your model is.  When all of the replicates are at 
the same "level" (i.e. all biological or all technical) and come from one 
treatment the regression estimate is just the mean.  However, once you have 
several treatments, using the ANOVA model allows you to have a pooled 
estimate of standard error.  And if you have blocks and different types of 
replicates (e.g. arrays and spots), the ANOVA model allows you to properly 
account for this in the standard error.

Someone else will need to comment on the Bayesian model used in limma, as I 
am not currently using limma analysis.

--Naomi

At 09:21 PM 1/11/2004, Simon Melov wrote:
>Not being a statistician, I was wondering if there were a straightforward 
>answer as to why using a linear model was "better" than a straight mean in 
>limma when estimating the average M value across multiple chips. I believe 
>that the regression analysis is supposed to be less influenced by 
>outliers, is this the chief reason the linear model is implemented?
>
>thanks
>
>Simon.
>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111