interpreting adonis results

Howdy Folks,

It seems that empty groups (count is zero) are the source of the
problem. You can drop them using factor() command for your interaction
-- this drops empty levels:

mod <- betadisper(betad, factor(groupA))

works for me.

droplevels.factor

However, like Gav said, this may not be a meaningful test (but still
calls for a fix in vegan).

 If I remember correctly, Marti Anderson had
interactions in her software, but that was dropped from current
versions. 

Cheers, jari oksanen

For the dune dataset, the above error occured. But for my data, the 
interaction works. I wonder if i continue to use the new factor generated 
from the factor-interaction for the betadisper, will it affects the 
results? If this is wrong, what would be the recommended function to use?

I would need to check more carefully, but this may not be what Marti
Anderson's software would fit as an interaction model. I may be
misremembering, but IIRC PERMDISP can handle two-way ANOVA type models
within her framework and betadisper is not set-up for that. What you
have done is look at whether certain combinations of your two factors of
interest are more variable/dispersed than others. Which seems a
reasonable hypothesis to me.

G

Sincerely Yours,
J

On Nov 17 2011, Kay Cecil Cichini wrote:

..to be save I would consider to exclude an effect due to different  
multivariate spread. See chapter 5.2, Homogeneity of groups and beta  
diversity, in the vegan tutorial at  
http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf.

best,
kay


Zitat von Gavin Simpson <gavin.simpson at ucl.ac.uk>:

On Wed, 2011-11-16 at 03:43 +0100, Gian Maria Niccol? Benucci wrote:

Hi all,

I had 84 samples collected in 7 different sites. In each sample were 
individuated the different fungal species and recorded. I would test if 
exist a real difference between the sites and if exist a sort of site 
effect that structure the fungal communities... Then, I did adonis test

adonis(community.sq ~ location, data=env.table, permutations=999)

Call: adonis(formula = community.sq ~ location, data = env.table, 
permutations = 999)

          Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)
location   6    12.593 2.09886  6.8867 0.34922  0.001 ***
Residuals 77    23.467 0.30477         0.65078
Total     83    36.060                 1.00000
---
Signif. codes:  0 *** 0.001 ** 0.01 * 0.05 . 0.1   1



The significance is R2=0.349 at P=0.001 Can I assure that exist a 
strong site effect in structuring the communities in each site?

Depends. The test is one of no effect of `location`. You have found
evidence against this hypothesis and thus could reject this hypothesis,
instead accepting the alternative hypothesis that there is an effect of
`location`. As to the strength of this effect? ~35% of the sums of
squares can be explained by `location`. Substantially more of the
variance remains unexplained. As I know nothing about your subject area,
I am unable to comment further on the strength of the relationship.

Seeing as many ecologists whose work I read would say an effect is
significant if the p-value was >= 0.05. Not that I subscribe to this way
or working, but by that criterion, you have identified a significant
`location` effect.

HTH

G

Thanks for helping,

G.

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