Dear mailing list,

while working on my Master's thesis at the Technical University of
Munich, I got a problem with my outcomes of the RLQ analysis.
Short summary of my work:
I tried to examine whether some landscape metrics influence a couple of
plant's traits in calcareous grasslands. Therefor I performed a RLQ
analysis (R-package ade4), where, at first, a Correspondence Analysis of
the species table, and a Hill-Smith ordination for each traits table and
landscape metrics table have been implementet. To figure out which
species traits are affected by the ladscape metrics, I need, among other
things, to compute the correlations between the traits scores and the
first axis of the RLQ model, which explains by far most of the variance
between the three tables.
The problem I have now is that for/Pearson's r/  I obtained values > 1
respectivley < -1. I used the R code:

  > t(duditrait_alt$tab)%*%(diag(duditrait_alt$lw))%*%as.matrix(rlq1$mQ)

(modified, adapted from Kleyer et al. 2012 (Appendix S2; "Tutorial:
methods for assessing functional responses to environmental gradients":
http://onlinelibrary.wiley.com/doi/10.1111/j.1654-1103.2012.01402.x/suppinfo)
"duditrait_alt" is the outcome of the Hill-Smith ordination of the
species traits.

My assumption is that those values might not be the correlation
coefficients but maybe the covariance/cross-variance.

Did somebody gain some experiences with this kind of problem?

Thanks a lot in advance.

Oliver Zachow



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