[R-sig-eco] orthogonal variables aliased by rda(vegan)

[Andrea Bertolo]

I agree that lm would be a natural choice with well distibuted univariate
data. However, the zero-inflated distribution I found in my data pushed me
to look for a method based on a randomization test rather than a parametric
one. This was the reason I used the rda fonction (followed by the ANOVA one). 

Probably the best would be to use a ZIP or ZINB regression to model this
kind of data, but I am still not very confortable with this kind of
modeling. By the way, I am exploring the pscl library to run Zero-Inflated
models with the zeroinfl function: does anybody here have tested its
performances and could recommend it ?

best
andrea





At 23:34 2008-06-04 +0300, Jari Oksanen wrote:
>
>On 4 Jun 2008, at 21:28, Andrea Bertolo wrote:
>
>> Dear Jari,
>>
>> this is exactly the structure of my data.
>> I in fact forgot to mention that I was using the rda fonction as a
>> regression here (thanks to Karl Cottenie to let me see it...), i.e. to
>> explain the abundance of a single fish species with 14 MEMs.
>>
>In this case there is no reason to use rda in R: simply use lm.
>
>All independent variables are used in calculations, but not displayed  
>in ordination diagrams. If you only have one dependent variable, you  
>could not see much, though. The biplot arrows are defined to be of  
>unit length  in full constrained space, so that they only can be +1 or  
>-1 in one dimension. However, the regression coefficients should be  
>available for all: just use coef() on the result to get the  
>coefficients.
>
>I cannot find a reason to use a Euclidean multivariate method (rda)  
>for univariate data when you can use univariate methods like the  
>standard lm() -- and lm indeed is the natural choice in this case.
>
>> There is only one thing that I don't understant.
>> What do you mean exactly when you say that "these extra constraints  
>> are
>> taken into account, but not reported in all cases (the aliasing is
>> explicit: there is code to turn down displaying the results that were
>> calculated)." ?
>> Does this mean that the rda is calculated with all the 14 contraints  
>> (in
>> this case), but only the first constraint is plotted ? I ran the rda  
>> with
>> the only "un-aliased" MEM and I obtained the same results, showing  
>> that the
>> aliased constraints are effectively eliminated.
>
>I just tried this, and could not confirm this. The result were  
>different for eigenvalues, configuration, coefficients or anything I  
>looked at. There was this aliasing thing, but that does not mean that  
>the results are similar. The only similar thing is that in when  
>ordinating a single species you get only one axis (but it's a  
>different axis depending on your constraints). So do not ordinate: use  
>lm.
>
>> Also, If I remeber well,
>> when I run the same rda with Canoco I can keep the all 14  
>> constraints in
>> the analysis, getting a different result. Are these differences only a
>> graphic matter or there are more substantial differeces ?
>
>Perhaps somebody knows Canoco and can tell you.
>
>cheers, jari oksanen
>
>> many thanks
>> andrea
>>
>>
>>
>>
>> At 20:51 2008-06-04 +0300, you wrote:
>>> Quoting Andrea Bertolo <andrea.bertolo at uqtr.ca>:
>>>
>>>> Hi to all,
>>>>
>>>> I am running some RDAs on Vegan using Moran eigenvector Maps (MEM)  
>>>> to
>>>> explain the variation in the abundance of a fish species.
>>>>
>>>> In several occasions I noticed that, despite MEM are othogonal by
>>>> definition, they were aliased by the rda fonction and thus not  
>>>> taken into
>>>> account in the analysis, as if they were collinear !
>>>>
>>>> I cannot understand why, since the MEMs used in the analysis  
>>>> showed not
>>>> only to be orthogonal (I post-verified it by calculating a VIF, in  
>>>> case
>>>> of), but also to add each a significant fraction to the explained  
>>>> variation
>>>> of the dependent variable (MEMs were selected by the forward.sel  
>>>> fonction
>>>> available in the packfor package).
>>>>
>>>> Could anybody tell me if I am doing something wrong or there is a  
>>>> real bug
>>>> here ?
>>>
>>> I'll try.
>>>
>>> This is something that really may be a problem in vegan, and may
>>> change in future versions. The problem here is (probably) that the
>>> number of your PCNMs is higher than min(nrow(x), ncol(y)) in your
>>> dependent data. There really are two kind of ranks in rda/cca etc. in
>>> vegan: the rank of the dependent community data, and the rank of the
>>> constraints. It seems that in your case of spatial filtering the rank
>>> of the MEM's is higher than the rank of the dependent matrix. The
>>> current practice in vegan is the take the rank of the solution as the
>>> min of these two ranks, and the extra constraints are aliased.
>>> However, these extra constraints are taken into account, but not
>>> reported in all cases (the aliasing is explicit: there is code to  
>>> turn
>>> down displaying the results that were calculated). This will change  
>>> in
>>> the future, but I'm not yet quite sure how this should be changed --
>>> this needs research, and discussion among developers and other
>>> interested parties. However, the main results will not change. Things
>>> that will change are the display of the same results, and some  
>>> derived
>>> functions (predict, calibrate.cca and perhaps some others, but I  
>>> don't
>>> know yet which). This is a change that has wide reaching effects, and
>>> therefore you cannot expect this before autumn -- or "fall" on that
>>> side of the pond).
>>>
>>> Vegan is not yet ready for the full blown spatial analysis, but we  
>>> are
>>> working for that (and here "we" is really plural and implies several
>>> persons). If you need detailed advice on the possible consequences of
>>> the upcoming change, you may directly contact me or us at the
>>> http://vegan.r-forge.r-project.org/.
>>>
>>> Best wishes, Jari Oksanen
>>>
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>>
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