classical statistics in R

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