Hi Joe
Is time a continuous variable or a factor?
The thing is that the terms ARE nested. The nesting is defined by the random
effects structure. The fixed effects slot into that. They way this happens is
defined by the coding in the data. So I assume you have something like
(simplified):
(apologies - I should have written coast * MBL not ML)
I'm not sure of my ground here, but surely do lose something - you wouldn't
retain coast:MBL if it's not significant, as you lose degrees of freedom, and
this gets worse the more terms and the more interactions you consider. I think
it's
The coast * ML term tests for HSP high/low dependent on coast. To test this
fit the full model with method = ML and compare it to lme(HSP~coast+MBL,
random= ~1|site, method =ML) using anova(model1, model2). There are alot of
technical issues with testing both fixed and random effects in
On Mon, Nov 10, 2008 at 9:22 AM, Mike Dunbar [EMAIL PROTECTED] wrote:
(apologies - I should have written coast * MBL not ML)
I'm not sure of my ground here, but surely do lose something - you wouldn't
retain coast:MBL if it's not significant, as you lose degrees of freedom, and
this gets
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hadley wickham wrote:
On Mon, Nov 10, 2008 at 9:22 AM, Mike Dunbar [EMAIL PROTECTED] wrote:
(apologies - I should have written coast * MBL not ML)
I'm not sure of my ground here, but surely do lose something -
you wouldn't retain coast:MBL if
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 SR, it's comprehensive and well-written.
My problem
Personally, I found GE 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
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 GE to be
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
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
Sebastian P. Luque [EMAIL PROTECTED]
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
On Mon, Nov 10, 2008 at 2:02 PM, Ben Bolker [EMAIL PROTECTED] wrote:
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hadley wickham wrote:
On Mon, Nov 10, 2008 at 9:22 AM, Mike Dunbar [EMAIL PROTECTED] wrote:
(apologies - I should have written coast * MBL not ML)
I'm not sure of my ground
There are two somewhat different objectives highlighted in these posts:
How to best learn classical statistics?
How to best learn R?
One might argue that classical statistics texts (or any well written modern
one) provide a more rudimentary knowledge of underlying theory than one geared
to
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