CODEWITHSUNDEEP
rstatisticscorrelationvegan
Kevin CA
Kevin CA

Reputation: 25

Quantifying importance of variables in Canonical Correspondence Analysis using R? (x-post from researchgate)

I currently have species abundance data for multiple lakes along with measurements of some environmental variables of those lakes. I decided to do Canonical Correspondence Analysis of the data in R, as demonstrated by ter Braak and Verdenschot (1995), see link: http://link.springer.com/article/10.1007%2FBF00877430 (section: "Ranking environmental variables in importance")

I am not very good with R yet, and I do not have access to the software specified in the article (CANOCO). My problem is, in order to do stepwise ranking of importance of environmental variables, I have to obtain the Lambda (Is this the same as Wilk's Lambda?) and perform a Monte Carlo permutation test on each CCA constrained axis. 

Does anybody know how I can do this in R? I would love to be able to use this analysis.

Upvotes: 1

Views: 1022

Answers (1)

Gavin Simpson
Gavin Simpson

Reputation: 174853

You want the anova() method that vegan provides for cca(), the function that does CCA in the package, if you want to test effects in a current model. See ?anova.cca for details and perhaps the by = "margin" option to test marginal terms.

To do stepwise selection you have two options

  1. Use the standard step() function that works with an AIC-like statistic for CCA, or
  2. For the sort of selection done in that paper and implemented in CANOCO you want ordistep(). This does forward selection & backward elimination testing changes to models via permutation tests.

Lambda is often used to indicate Eigenvalues and is not Wilk's Lambda. The pseudo-F statistic will be mentioned in the paper and it is this that is computed in the test and for which permutations give the sampling distribution under the null hypothesis, which ultimately determines significance of terms in the model or whether a term enters or leaves the model.

See ?ordistep for more details.

Upvotes: 2

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