Thank you Juan. This does indeed look promising.
Cheers, Marc
On Thu, Jul 18, 2019 at 4:08 PM Juan Antonio Balbuena <j.a.balbu...@uv.es>
wrote:
> You may have a look at the RRPP package (Collyer & Adams 2018, Methods
> Ecol Evol doi: 10.1111/2041-210X.13029)
>
> Best
>
> Juan
>
> El 18/07/2019 a las 14:48, Marc Taylor escribió:
> > Dear all,
> >
> > I am working with rather large community dataset (~30 spp and ~20,000
> > samples) which I am trying to relate to several environmental variables.
> > One of the environmental variables that I am specifically interested in
> is
> > a factor.
> >
> > I have conducted a CCA (vegan::cca) relating env variables to the
> > community, and have found significant effects. I am now interested in
> > understanding how to characterize my community data with respect to the
> > categorical env factor mentioned earlier - i.e. is a given spp more
> > associated with a given level of the env variable.
> >
> > In the past, I have used vegan::simper for such explorations, but am
> having
> > difficulty applying it here due to the size of the data (i.e. a distance
> > matrix for the biological community would be 20,000 X 20,000). I'm
> > wondering if there are any approaches that I might apply that are less
> > computationally intensive.
> >
> > One approach that I explored was to look for the closest env level for
> each
> > species based on CCA coordinates (from significant axes) using a nearest
> > neighbor search (FNN::get.knnx). The results look promising (see example
> > below), but I haven't come across the approach anywhere else. It is also
> > unfortunately not a statistical test, so I lose some quantitative measure
> > of how likely these associations are
> >
> > Does anyone have any other suggestions how I might do such an analysis
> with
> > this large dataset?
> >
> > Many thanks in advance,
> > Marc
> >
> >
> > #### EXAMPLE ####
> >
> > set. seed(1)
> >
> > ### required packages and data
> > library(FNN)
> > library(vegan)
> >
> > data(dune)
> > data(dune.env)
> > str(dune.env)
> >
> > ### fit model
> > mod <- cca(dune ~ A1 + Moisture + Management, data=dune.env)
> > # visualize
> > plot(mod, display = c("sp", "bp", "cn"))
> >
> > ### Permutation Test for CCA
> > # terms sig. test (tested sequentially - i.e. order matters)
> > (At <- anova(mod, by = "terms", permutations = 499))
> > # cca axes sig. test
> > (Ax <- anova(mod, by = "axis", permutations = 499, cutoff = 0.1))
> >
> > ### Determine nearest neighbor of Moisture level for each species
> > # number of significant CCA axes
> > n <- 2
> > # retrieve un-scaled CCA coordinates
> > res <- summary(mod, scaling = 0, axes = n, display = c("sp", "wa", "lc",
> > "bp", "cn"))
> > # get CCA indices for Moisture levels
> > mat <- match(paste0("Moisture",levels(dune.env$Moisture)),
> > rownames(res$centroids))
> >
> > # nearest neighbor
> > pred <- get.knnx(data = res$centroids[mat,], query = res$species,
> > k = length(levels(dune.env$Moisture)))$nn.index[,1]
> > # return results
> > tmp <- data.frame(spp = rownames(res$species),
> > nearest_level = paste0("Moisture",levels(dune.env$Moisture))[pred])
> > tmp
> >
> >
> > plot(mod, display = c("cn"))
> > text(mod, display = "sp",
> > col =
> >
> rainbow(length(levels(dune.env$Moisture)))[as.numeric(tmp$nearest_level)])
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > R-sig-ecology@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> >
> --
>
> Dr. Juan A. Balbuena
> Cavanilles Institute of Biodiversity and Evolutionary Biology
> Symbiont Ecology and Evolution Lab
> University of Valencia http://www.uv.es/~balbuena
> <http://www.uv.es/%7Ebalbuena>
> P.O. Box 22085 http://www.uv.es/cophylpaco
> 46071 Valencia, Spain
> e-mail: j.a.balbu...@uv.es <mailto:j.a.balbu...@uv.es>tel. +34 963 543
> 658 fax +34 963 543 733
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