Hi Sebpe,
the analysis of the data that you describe could be a complex and
lengthy process, in which decisions that you are confronted by are
affected by previous decisions that you have made. I recommend
obtaining the assistance of a statistician, preferably a local one
whose door you can knock
Hi,
I'm working on leaf characteristics of trees. Each tree is characterised by
about 10 leaf traits.
The trees were sampled at 9 different locations (about 20 to 30
trees/location, NOT balanced), grouped in 3 different climatic zones
(Sahelian, Soudanian and Guinean) (NOT balanced).
Further, eac
Hello Again R help readers - I have posted in the past and want to
thank all those who replied with helpful suggestions.
This regards mixed model anova, and the nlme package.
My purpose is to make a comparison of barnacle recruit density from 3
different regions. In each of these regions i have
>> Also i have read in Quinn and Keough 2002, design and analysis of
>> experiments for
>> biologists, that a variance component analysis should only be conducted
>> after a rejection
>> of the null hypothesis of no variance at that level.
Hmmm...
This does rather assume that 'no significant resu
Hi Stephen,
Slip of the dactylus: lm() does not, of course, take a fixed=arg. So you
need
To recap:
mod.rand <- lme(fixed=y ~ x, random=~x|Site, data=...)
mod,fix <- lm(y ~ x, data=...) ## or
##mod,fix <- lm(formula=y ~ x, data=...)
Bye.
Mark Difford wrote:
>
> Hi Stephen,
>
>>> Also
Hi Stephen,
>> Also i have read in Quinn and Keough 2002, design and analysis of
>> experiments for
>> biologists, that a variance component analysis should only be conducted
>> after a rejection
>> of the null hypothesis of no variance at that level.
Once again the caveat: there are experts on
First of all thank you for the responses. I appreciate the
suggestions i have received thus far.
Just to reiterate
I am trying to analyze a data set that has been collected from a
hierarchical sampling design. The model should be a mixed model
nested ANOVA. The purpose of my study is to analyz
Hi Stephen
On 22/02/2008, Stephen Cole <[EMAIL PROTECTED]> wrote:
> hello R help
>
> I am trying to analyze a data set that has been collected from a
> hierarchical sampling design. The model should be a mixed model nested
> ANOVA. The purpose of my study is to analyze the variability at each
So, Site is nested in location. Location is nested in Region. And you are
looking at how density varies. Let's think about this from the point of
view of a model with varying intercepts.
You have some mean density in your study. That mean will deviate by site,
location, and region. Each of w
Hi Stephen,
Hopefully you will get an answer from one of the experts on mixed models who
subscribe to this list. However, you should know that both lme() and lmer()
currently have anova() methods. The first will give you p-values (but no
SS), and the second will give you SS (but no p-values).
hello R help
I am trying to analyze a data set that has been collected from a
hierarchical sampling design. The model should be a mixed model nested
ANOVA. The purpose of my study is to analyze the variability at each
spatial scale in my design (random factors, variance components), and say
some
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