On Tue, 2007-02-27 at 13:13 -0500, Kuhn, Max wrote: > Helene, > > My point was only that RDA may fit a quadratic model for the terms > specified in your model. The terms that you had specified were already > higher order polynomials (some cubic). So a QDA classifier with the > model terms that you specified my be a fifth order polynomial in the > original data. I don't know the reference you cite or even the > subject-matter specifics. I'm just a simple cave man (for you SNL fans). > But I do know that there are more reliable ways to get nonlinear > classification boundaries than using x^5.
I doubt that Helene is trying to do a classification - unless you consider classification to mean that all rows/samples are in different groups (i.e. n samples therefore n groups) - which is how RDA (Redundancy Analysis) is used in ecology. You could take a look at multispati in package ade4 for a different way to handle spatial constraints. There is also the principle coordinates analysis of neighbour matrices (PCNM) method - not sure this is coded anywhere in R yet though. Here are two references that may be useful: Dray, S., P. Legendre, and P. R. Peres- Neto. 2006. Spatial modeling: a comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecological Modelling, in press. Griffith, D. A., and P. R. Peres- Neto. 2006. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology, in press. HTH G > > If you want a quadratic model, I would suggest that you use QDA with the > predictors in the original units (or see Hastie's book for a good > example of using higher order terms with LDA). > > Looking at your email, you want a "a variation partitioning analyses". > RDA works best as a classification technique. Perhaps a multivariate > ANOVA model may be a more direct way to meet your needs. There is a > connection between LDA and some multivariate linear models, but I don't > know of a similar connection to RDA. > > Max > > -----Original Message----- > From: MORLON [mailto:[EMAIL PROTECTED] > Sent: Tuesday, February 27, 2007 12:53 PM > To: 'Jari Oksanen'; r-help@stat.math.ethz.ch > Cc: Kuhn, Max > Subject: RE: [R] RDA and trend surface regression > > Thanks a lot for your answers, > > I am concerned by your advice not to use polynomial constraints, or to > use > QDA instead of RDA. My final goal is to perform variation partitioning > using > partial RDA to assess the relative importance of environmental vs > spatial > variables. For the spatial analyses, trend surface analysis (polynomial > constraints) is recommended in Legendre and Legendre 1998 (p739). Is > there a > better method to integrate space as an explanatory variable in a > variation > partitioning analyses? > > Also, I don't understand this: when I test for the significant > contribution > of monomials (forward elimination) > > >anova(rda(Helling ~ I(x^2)+Condition(x)+Condition(y))) > > performs the permutation test as expected, whereas > > >anova(rda(Helling ~ I(y^2)+Condition(x)+Condition(y))) > > Returns this error message: > > Error in "names<-.default"(`*tmp*`, value = "Model") : > attempt to set an attribute on NULL > > Thanks again for your help > Kind regards, > Helene > > Helene MORLON > University of California, Merced > > -----Original Message----- > From: Jari Oksanen [mailto:[EMAIL PROTECTED] > Sent: Monday, February 26, 2007 11:27 PM > To: r-help@stat.math.ethz.ch > Cc: [EMAIL PROTECTED] > Subject: [R] RDA and trend surface regression > > > > 'm performing RDA on plant presence/absence data, constrained by > > geographical locations. I'd like to constrain the RDA by the "extended > > matrix of geographical coordinates" -ie the matrix of geographical > > coordinates completed by adding all terms of a cubic trend surface > > regression- . > > > > This is the command I use (package vegan): > > > > > > > > >rda(Helling ~ x+y+x*y+x^2+y^2+x*y^2+y*x^2+x^3+y^3) > > > > > > > > where Helling is the matrix of Hellinger-transformed presence/absence > data > > > > The result returned by R is exactly the same as the one given by: > > > > > > > > >anova(rda(Helling ~ x+y) > > > > > > > > Ie the quadratic and cubic terms are not taken into account > > > > You must *I*solate the polynomial terms with function I ("AsIs") so that > they are not interpreted as formula operators: > > rda(Helling ~ x + y + I(x*y) + I(x^2) + I(y^2) + I(x*y^2) + I(y*x^2) + > I(x^3) + I(y^3)) > > If you don't have the interaction terms, then it is easier and better > (numerically) to use poly(): > > rda(Helling ~ poly(x, 3) + poly(y, 3)) > > Another issue is that in my opinion using polynomial constraints is an > Extremely Bad Idea(TM). > > cheers, Jari Oksanen > > ---------------------------------------------------------------------- > LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}} > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.