[R] overdispersion and quasibinomial model

David Winsemius dwinsemius at comcast.net
Wed Nov 25 16:04:00 CET 2009


On Nov 25, 2009, at 7:04 AM, djpren wrote:

>
> Thanks for the reply. Naturally I already searched the site and help  
> for the
> answers to these questions. I think I've figured out how to run a
> quasi-binomial model, but I cannot figure out how to test for
> over-dispersion or how to apply a shapiro-wilk test.
>
> This is not homework, neither do I have an instructor who is  
> proficient in
> using R. This program was suggested to me by another researcher  
> after he
> witnessed my frustration with the inflexibility of SPSS and other such
> programs. I am on a very tight schedule and I don't have time to  
> become a
> statistician and computer scientist, which is why I wrote 3 very quick
> questions asking for commands that i had already tried to find myself.

"Quick questions" are somewhat deprecated here. Have you read the  
Posting Guide? Its overall message is that the list readership expects  
more detail rather than less. Perhaps with a better search method and  
a pointer to the glm()  function, which will do what was requested,   
you might compose a more complete description of the data and the  
problem, and offer code that shows what progress you are making.

>
> Testing for over-dispersion is probably something I can eventually  
> get to
> grips with, since I just have get variance for the real and modelled  
> data.
> However, I cannot find a command to do shapiro-wilks on the site or  
> on these
> forums.

I would have thought my original reply would have pointed the way to  
more effective searching. The obvious search strategy using the  
RSiteSearch function would seem to be:

 > RSiteSearch("shapiro wilks")
A search query has been submitted to http://search.r-project.org
The results page should open in your browser shortly

A Browser window did open up and there were 8 hits, at least two of  
which were to functions that would do what you appear to be determined  
to do on a rather dubious basis.


> Also, why do you say that most people here wouldn't recommend this
> procedure?

Are you doing this because some reviewer asked you to do so or because  
you are copying a path that someone else laid out for you? Testing for  
normality in a binomial model seems rather puzzling on the face of it.

-- 
David.

>
>
> David Winsemius wrote:
>>
>>
>> On Nov 24, 2009, at 3:41 PM, djpren wrote:
>>
>>>
>>> I am looking for the correct commands to do the following things:
>>>
>>> 1. I have a binomial logistic regression model and i want to test  
>>> for
>>> overdispersion.
>>
>> Under the teach a man to fish precept,   ... try:
>>
>> RSiteSearch("test over dispersion binomial models")
>>
>>> 2. If I do indeed have overdispersion i need to then run a quasi-
>>> binomial
>>> model, but I'm not sure of the command.
>>
>> ?glm
>> # and follow the appropriate links
>>
>>> 3. I can get the residuals of the model, but i need to then apply a
>>> shapiro
>>> wilk test to test them. Does anyone know the command for this?
>>
>>
>> RSiteSearch("shapiro-wilks")   # not that people here recommend this
>> procedure
>>
>> The overall flavor of these questions is "homework", so I'm
>> speculating that you may want to consult your instructors.
>>
>> -- 
>>
>> David Winsemius, MD
>> Heritage Laboratories
>> West Hartford, CT
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
> -- 
> View this message in context: http://old.nabble.com/overdispersion-and-quasibinomial-model-tp26502728p26511410.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

David Winsemius, MD
Heritage Laboratories
West Hartford, CT




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