[R-sig-eco] classical statistics in R

Tue Nov 11 00:19:44 CET 2008

While I agree with this statement in principal, I disagree in  
practice.  I think one of the challenges of teaching classical (or  
any) ecological stats and analysis of experiments to new students is  
being able to allow them to begin to understand not just the specifics  
and concepts of the methods as quickly as possible, but to also begin  
to confront the real problems of the data analyst.  With a given set  
of data, how will choice of method, violation of assumptions, that  
seagull that ate half of the treatment plots, etc. really affect the  
inferences I can draw?  A large hurdle I've seen in many classes,  
regardless of the package they chose to use, is actually getting  
students to learn and then work with the software.  This has often  
involved whole separate labs or classes that, at worst, can be mere  
exercises in button pushing and far abstracted from the course material.

Written languages, such as SAS and R, take some of that away, of  
course, and having a text that at least features examples in said  
language can more seamlessly integrate the two.  After working through  
Gelman and Hill, I was struck by how the code and the conceptual text  
worked together pretty seamlessly.  At the end, I was able to emerge  
with a working understanding of the concepts of multilevel modeling,  
Bayes, etc.  More importantly, I knew I had a toolset in hand that I  
could always turn to and work with in order to carry forth my data  
analysis.

And, indeed, that _always_ is one of the advantages to R.  It's free.   
You can learn it as an undergrad, keep working with it in grad school,  
end up working at a tiny research station with no money in the middle  
of the North Sea, and you will always be able to use it.  No site  
licensees, etc, needed.  I think this is an enormous practical  
advantage in the long run.  And, indeed, thought it will grow and  
change with time, as it is public domain, there is never any danger of  
it disappearing from the earth, like many favorite canned statistical  
packages of yore.

Hence, why not learn it early, and why not a good book that integrates  
concept and practice?  It's what pleased me so much about G&H as well  
as Ben's book.

Perhaps it is time for a classical statistics book for ecology that  
both emphasized the conceptual meat of the material but is integrated  
with R, allowing students to really explore that meat on their own?

On Nov 10, 2008, at 1:55 PM, Sebastian P. Luque wrote:

> In general, I would not choose a book to learn basic statistics  
> based on
> whether it has R content or not.  What's important is to learn the
> concepts.  Learning how to use them in a particular software is  
> useful,
> but secondary.  If we're careless about this distinction, we risk
> falling into habits promoted by most commercial software, where one
> points and click without understanding what one is doing.  The risk is
> there even in GNU R, as the number of functions and packages keeps
> growing to help us save time developing procedures.  There's a balance
> to be reached between the help received and intellectual independence.
> For classical statistics, many books have long series of editions that
> have made them superb with age (like good wine).  Zar's Biostatistical
> Analysis is my favorite in this domain, but I enjoyed Sokal & Rolf  
> too.
>
>
> Seb
>
>
>
> On Mon, 10 Nov 2008 16:11:47 -0500,
> Brian Campbell <jacarebrazil98 at hotmail.com> wrote:
>
>> I conceded to R shift (mostly) last year and began Crawley (2005)
>> Statistics: An Introduction using R.  Quinn and Keough: Experimental
>> Design and Data Analysis for Biologists is very useful, but if  
>> given a
>> choice of the two with the emphasis on learning R, Crawley might be
>> preferable.  Better yet might even be the "R Book".
>
>> -Brian
>
>>> Date: Mon, 10 Nov 2008 12:30:22 -0800 From:
>>> cparker at pdx.edu To:
>>> r-sig-ecology at r-project.org Subject: Re:
>>> [R-sig-eco] classical statistics in R
>
>>> I agree with Jordan and will also throw in Gelman and Hill's "Data
>>> Analysis Using Regression and Multilevel/Hierarchical Models". Its a
>>> social science based book but is very relevant to ecologists and
>>> includes R code (and bugs code).  -Chris
>
>
>>> Jordan Mayor wrote:
>>>> Personally, I found G&E to be very helpful at only a cursory
>>> interest level.  > Quinn & Keough's "Experimental Design and Data
>>> Analysis for Biologists" is > a practical in-depth text that covers
>>> allot more detail - but, alas no > R-code is provided.  In fact, it
>>> is quite program-independent.
>
>>>> Cheers
>
>>>> On Mon, Nov 10, 2008 at 3:10 PM, tyler  
>>>> <tyler.smith at mail.mcgill.ca> wrote:
>
>
>>>> Hi,
>
>>>>> I've just received my copy of Ben Bolker's new book, "Ecological
>>> Models >>and Data in R". I was a little surprised to see he
>>> recommended Sokal and >>Rohlf's "Biometry" as an introduction to
>>> classical stats. Not because >>there's anything wrong with S&R, it's
>>> comprehensive and well-written.  >>My problem with this book is that
>>> it's written from the perspective of >>filling out tables of sums of
>>> squares according to fixed recipes, while >>R is geared towards more
>>> flexible linear models. Trying to translate the >>more complex
>>> recipes into R code is not a trivial task.
>
>>>>> In response to an email, Ben suggested that Gotelli and Ellison's
>>>>> "Primer of Ecological Statistics" provides a more modern take on
>>> the >>subject than S&R. I have to agree, G&E is one of the best
>>> intros I've >>seen for ecologists. But it doesn't really go very far
>>> into the possible >>complexities of ANOVA and linear regression, and
>>> doesn't specifically >>address implementing tests in R.
>
>>>>> Ben and I are both curious as to what other r-sig-eco readers  
>>>>> think
>>>>> about this issue. What are the best sources for learning about
>>> classical >>statistics as implemented in R? S&R has been the  
>>> standard
>>> reference for >>quite a while, but it now appears to be dated. Is
>>> there a good standard >>text that covers the same breadth of  
>>> material
>>> with a modern, R-compatible >>approach? Ben also recommended several
>>> books by Michael Crawley - any >>strong feelings on these, or other
>>> suggestions?
>
>>>>> Thanks!
>
>>>>> Tyler
>
>>>>> -- >>Research is what I'm doing when I don't know what I'm doing.
>>>>> --Wernher von Braun
>
>>>>> _______________________________________________ >>R-sig-ecology
>>> mailing list >>R-sig-ecology at r-project.org
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>
>
>
>
>
>
>>> _______________________________________________ R-sig-ecology  
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>> _________________________________________________________________
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>
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>
>
>
> Cheers,
>
> -- 
> Seb
>
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