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

[Karl Cottenie]

Andrea,

do check out Pierre Legendre's website with R functions 

http://www.bio.umontreal.ca/legendre/indexEn.html#RFunctions

because he has apparently written a function for your case
(multRegress.R).

Karl

ps: this web site, together with Jari Oksanen's website should be
bookmarked by any ecologist interested in R (in my humble opinion).

On Wed, 2008-06-04 at 16:48 -0400, Andrea Bertolo wrote:
> 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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> >>> R-sig-ecology at r-project.org
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> >>>
> >>
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