[R-sig-eco] classical statistics in R

Sebastian P. Luque spluque at gmail.com
Mon Nov 10 22:55:28 CET 2008

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.


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
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