Dear All,

Thank you very much.

Regards,
Chris

On Sun, May 6, 2012 at 9:10 AM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Dear Chris,
>
> Steve has asked you some highly relevant questions and suggested a
> reference on glms.  I will clarify some other issues.
>
> I assume you are actually refering to the glmFit() and glmLRT() functions
> in the edgeR package (I admit that I have myself written fitLRT instead of
> glmLRT on occasion.)  These functions fit negative binomial rather than
> binomial glms.  And they do this in a parallelized fashion to multiple
> response vectors.
>
> The algorithm and strategy of the glmFit() function is outlined in the
> publication:
>
>  McCarthy et al "Differential expression analysis of multifactor
>  RNA-Seq experiments with respect to biological variation",
>  Nucleic Acids Research 2012
>  http://www.ncbi.nlm.nih.gov/**pubmed/22287627<http://www.ncbi.nlm.nih.gov/pubmed/22287627>
>
> I have also updated the documentation page for these functions in the past
> few days to give better points to the lower level functions that they call.
>
> However you should not expect an elementary tutorial on these algorithms,
> because they are not elementary by any stretch of the imagination, or at
> least so it seems to me.  Indeed, the whole intention of the edgeR package
> is that it should give biological researchers access to statistically
> advanced algorithms without their having to worry too much about the
> numerics under the hood.
>
> Best wishes
> Gordon
>
>  Date: Thu, 3 May 2012 09:48:03 -0400
>> From: Steve Lianoglou <mailinglist.honeypot@gmail.**com<mailinglist.honeypot@gmail.com>
>> >
>> To: chris Jhon <cjhon217@gmail.com>
>> Cc: bioconductor@r-project.org
>> Subject: Re: [BioC] basics of GLMFit
>>
>> Hi,
>>
>> On Thu, May 3, 2012 at 3:59 AM, chris Jhon <cjhon217@gmail.com> wrote:
>>
>>> Hi All,
>>>
>>> I read the manual of glmFit and fitLRT functions but i would appreciate
>>> if
>>> any one can explain or (send me a tutorial) about the basics of fitting
>>> binomial generalized linear model to a data.
>>>
>>> Thank you.
>>> Regards,
>>> Chris
>>>
>>
>> Is it the "negative binomial" part, or the "glm" part that you are
>> curious about learning? Or are you curious about how one actually
>> "fits" a model to data?
>>
>> This is a hard question to answer because there's just so much to know
>> and it's unclear what you mean by "the basics" since ... well ...
>> depending on your background it's not all that basic ;-)
>>
>> Have you read through:
>> http://en.wikipedia.org/wiki/**Generalized_linear_model<http://en.wikipedia.org/wiki/Generalized_linear_model>
>>
>> Lots of refs there including a link to McCullagh & Nelder Textbook if
>> that's what you're interested in. There's also lots of things that pop
>> up when you google "fit glm" (it's not just R help pages, either ;-)
>>
>> -steve
>>
>> --
>> Steve Lianoglou
>> Graduate Student: Computational Systems Biology
>> ?| Memorial Sloan-Kettering Cancer Center
>> ?| Weill Medical College of Cornell University
>> Contact Info: http://cbio.mskcc.org/~lianos/**contact<http://cbio.mskcc.org/~lianos/contact>
>>
>
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