Well,
I may have gotten your question over-simplified, but, isn't it the F in
your table (*in bold*)?
Df Deviance Resid. Df Resid. Dev*F*Pr(F)
NULL 26 3204.6
Sex 2 6.8924 3197.7 *2.2640* 0.1851036
Time1
Hi Mitchell;
If I understood what you want, it should work:
beta - betadisper(vegdist(matrix), type=centroid)
beta$distances
This will give you the raw values and then you can move with your analyses.
But check the new paper by Baselga 2013 Ecography before using
dissimilarity. He has
Hi mates;
I was wondering here if it's possible to apply the Sample-based, or the
H2 randomisation proposed in Crist et al. 2003 using adipart or any similar
function. As they describe there, by randomising samples we'd be able to
account for sampling design randomness, instead of individuals
Hi Lara;
You're dealing with mixed models in this case, I suggest you to take a look
on this wesite: http://glmm.wikidot.com
Also, there's a specific mailing list for this, there your questions were
either probably already answered or can be properly answered.
With my best, Ricardo.
Hello all;
A couple of years ago, our colleague in this group Kevin McCluney asked
what is for me a quite important question, though I couldn't find an answer
to this. If someone can shed some light on this, I'd be really grateful:
The question is:
*Is it innapropriate to use the significant
Hi;
If I understood, you can create a copy of the variable and rename levels,
jointing them and building a model with the levels merged. Then you compare
the models and find if the models are or are not different (p0.05), if
they are ... your levels are different.
I also know that Ronaldo Reis
Hi people;
I'm thinking about a solution to show that a sample coming from 3 types of
killing solutions are nested. However, when I tried to do this with
nestedness function in Vegan I couldn't specify the groups. I guess it's
impossible at all, so my question is: is there any other analysis
