Dear useRs,
I want to perform 2nd stage nMDS, as described in Clarke, K.R., et al
(2006). Exploring interactions by second-stage community analyses.
Journal of Experimental Marine Biology and Ecology 338, 179-192. See
Abstract below
Do you know a package in R for that ? Or would you have home-made
scripts, at least a function for computing the distance matrix of
pair-wise correlations among dissimilarity matrices ?
Thank you,
Pierre
Abstract of Clarke et al 2006 :
Many biological data sets, from field observations and manipulative
experiments, involve crossed factor designs, analysed in a univariate
context by higher-way analyses of variance which partition out ‘main’
and ‘interaction’ effects. Indeed, tests for significance of
interactions among factors, such as differing Before–After responses at
Control and Impact sites, are the basis of the widely used BACI strategy
for detecting impacts in the environment. There are difficulties,
however, in generalising simple univariate definitions of interaction,
from classic linear models, to the robust, non-parametric multivariate
methods that are commonly required in handling assemblage data. The size
of an interaction term, and even its existence at all, depends crucially
on the measurement scale, so it is fundamentally a parametric construct.
Despite this, certain forms of interaction can be examined using
non-parametric methods, namely those evidenced by changing assemblage
patterns over many time periods, for replicate sites from different
experimental conditions (types of ‘Beyond BACI’ design) – or changing
multivariate structure over space, at many observed times. *Second-stage
MDS, which can be thought of as an MDS plot of the pairwise similarities
between MDS plots (e.g. of assemblage time trajectories), can be used to
illustrate such interactions, and they can be formally tested by
second-stage ANOSIM permutation tests. Similarities between
(first-stage) multivariate patterns are assessed by rank-based matrix
correlations, preserving the fully non-parametric approach common in
marine community studies. *The method is exemplified using time-series
data on corals from Thailand, macrobenthos from Tees Bay, UK, and
macroalgae from a complex recolonisation experiment carried out in the
Ligurian Sea, Italy. The latter data set is also used to demonstrate how
the analysis copes straightforwardly with certain repeated-measures designs.
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