[R-sig-eco] classical statistics in R
tyler
tyler.smith at mail.mcgill.ca
Mon Nov 10 23:45:55 CET 2008
"Sebastian P. Luque" <spluque at gmail.com>
writes:
> 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.
>
>
That's an important point. I should clarify that, for myself, it's not
so important to have actual R code. But the 'sums of squares' framework
presented in S&R is, or at least appears to be, at odds with the linear
model framework used in R. I would appreciate a reference that takes the
same approach as that used in R, so that I can focus on learning the
statistics.
To use S&R as written, I can read through the examples, and implement
them in low-level R code. This is tedious and inflexible. If I properly
understood the linear modelling approach used in R, I expect I could use
higher-level functions, and wouldn't have to re-implement each variation
of a test from scratch. But there's a conceptual gap between R and S&R
that I'm missing.
Cheers,
Tyler
> 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 mailing
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>
>> _________________________________________________________________
>
>
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>
>
>
> Cheers,
--
Better a botanist than a sociopath.
--Charlane Bishop
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