[R-sig-eco] Partitioning spatial effects using trend surface analysis or PCNM

[trichter]

Thank you very much for adressing my problem.

Maybe i can re-formulate it in a different way - moving away from 
specific partial CCA/RDAs to the core of my task.

If said task is the approximate quantification of partial effects on my 
bacterial counts (for example edaphic soil properties, above-ground 
plant diversity, and spatial autocorrelation) via the varpart function 
in vegan, is the classical way of having orthogonal polynomes of the x,y 
axis as CCA/RDA constraints still considered valid?

If i understand you correctly, i would alternatively:
- generate PCNMs from my x,y coordinate matrices
- extract those with a Moran I >0
- perform a RDA/CCA with forward selection
- use the PCNM found to be significant in the varpart function?

I think the only forward selection for CCA would be ordistep function in 
vegan. What would be an acceptable treshold for entering into my final 
set of accepted significant PCNMs?

The other problem is that on the one hand, RDA is not able to separate 
my community shifts as well as CCA, on the other hand varpart is based 
on RDA. I wonder if i can justify using varpart when my ordination of 
choice is based on CCA. But i have never seen a dedicated variance 
partition function for CCA. I just read an old answer of yours:
http://r.789695.n4.nabble.com/partitioning-variation-using-the-Vegan-CCA-routine-td823966.html

So, i can basically transform my raw data to chi2 and use them in an RDA 
to have a CCA proxy?


Thank you very much! as you can see, i am not really trained in statistics.

Tim



On 06.05.2015 02:45, Gavin Simpson-2 [via r-sig-ecology] wrote:
> Hi "trichter"
>
> On 5 May 2015 at 13:34, trichter <[hidden email] 
> </user/SendEmail.jtp?type=node&node=7579428&i=0>> wrote:
> <snip />
>
> > Here is what i do:
> >
> > spat <- as.data.frame(poly(as.matrix(spatxy), degree=3))
> >
> > cca1_s <- cca(OTU~., data=spat)
> > #significances
> > anova(cca1_s)
> > anova(cca1_s, by="term", perm=999)
> >
>
> Don't think last analysis makes much sense; if you have a cubic 
> polynomials
> plus interactions you should only consider the interactions first for
> removal, then decide if quadratic rather than cubic are needed
>
>
> >
> > #forward selection for most parsimonious model
> > cca1_s.f <- ordistep(cca(OTI~1, data=spat), scope=formula(cca1_s),
> > direction="forward", pstep=1000)
> > sig1_s.f <- anova(cca1_s.f, by="term", perm=999)
> >
>
> Again, as above, you have to be very careful with this. Just because you
> made a matrix with 9 "covariates" it doesn't mean it makes sense to 
> cherry
> pick from these terms.
>
>
> > The result is a significant CCA object. Spat is usuable in VarPart and
> > yields a low but significant value for overall autocorrelation.
> >
> > For PCNM i do
> >
> > rs <- rowSums(OTU)/sum(OTU)
> > pcnmw <- pcnm(dist(spatxy), w = rs)
> > cca1_pcnm <- cca(acido1 ~ scores(pcnmw))
> >
> > pcnmw consists of 250 vectors, and the result is a non-significant CCA
> > object, where i expected a "finer" spatial decomposition.
> >
>
> You are supposed to choose from among the set of PCNMs which explain the
> species data best, not use them all in the model. The problem appears 
> to be
> that you have a model that is far too complex with lots of redundant axes
> (or more likely too few constraints).
>
> One suggestion is to use only those PCNMs that have positive spatial
> correlation. Compute that using Moran's I of which there are a few
> implementations around in various R packages. You can do CCA analysis 
> with
> the positive spatial correlation PCNMs separately from the negatively
> correlated PCNMs if you wish.
>
> You will probably need to do some type of forward selection but the
> preferred method seems to be limited to RDA (because the adjusted R2
> measure used in the global significance test isn't worked out for 
> CCA). If
> you skip the global test, you could just do forward selection on the
> positive PCNMs, but you probably want to try to control for accepting too
> many PCNMs by having low entry threshold for significance.
>
> HTH
>
> G
>
>
>
> > The same is true if i am using total count data (hellinger 
> transformed or
> > not).
> >
> > I am sure i am doing it wrong, so if you have advise to properly do the
> > calculation, please let me know. Thank you for the help.
> >
> >
> >
> >
> >
> > --
> > View this message in context:
> > 
> http://r-sig-ecology.471788.n2.nabble.com/Partitioning-spatial-effects-using-trend-surface-analysis-or-PCNM-tp7579427.html
> > Sent from the r-sig-ecology mailing list archive at Nabble.com.
> >
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> >
>
>
>
> -- 
> Gavin Simpson, PhD
>
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-- 
Tim Richter-Heitmann (M.Sc.)
PhD Candidate



International Max-Planck Research School for Marine Microbiology
University of Bremen
Microbial Ecophysiology Group (AG Friedrich)
FB02 - Biologie/Chemie
Leobener Straße (NW2 A2130)
D-28359 Bremen
Tel.: 0049(0)421 218-63062
Fax: 0049(0)421 218-63069





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