simper(comm, group, permutations = 0, trace = FALSE,
parallel = getOption("mc.cores"), ...)
## S3 method for class 'simper':
summary(object, ordered = TRUE,
digits = max(3,getOption("digits") - 3), ...)how, or the
number of permutations required, or a permutation matrix where each
row gives the permuted indices.simper.parallel = 1 uses ordinary, non-parallel
processing.simper the
extra parameters are passed to shuffleSet if
permutations are used."simper" with following items:cusum back to the original data order.simper (Clarke 1993) is based
on the decomposition of Bray-Curtis dissimilarity index (see
vegdist, designdist). The contribution
of individual species $i$ to the overall Bray-Curtis dissimilarity
$d_{jk}$ is given by $$d_{ijk} = \frac{|x_{ij}-x_{ik}|}{\sum_{i=1}^S (x_{ij}+x_{ik})}$$
where $x$ is the abundance of species $i$ in sampling units
$j$ and $k$. The overall index is the sum of the individual
contributions over all $S$ species
$d_{jk}=\sum_{i=1}^S d_{ijk}$.
The simper functions performs pairwise comparisons of groups
of sampling units and finds the average contributions
of each species to the average overall Bray-Curtis dissimilarity.
The function displays most important species for each pair of
groups. These species contribute at least to 70 % of the
differences between groups. The function returns much more
extensive results which can be accessed directly from the result
object (see section Value). Function summary transforms the
result to a list of data frames. With argument ordered = TRUE
the data frames also include the cumulative contributions and
are ordered by species contribution.
The results of simper can be very difficult to interpret. The
method very badly confounds the mean between group differences and
within group variation, and seems to single out variable species
instead of distinctive species (Warton et al. 2012). Even if you make
groups that are copies of each other, the method will single out
species with high contribution, but these are not contributions
to non-existing between-group differences but to within-group
variation in species abundance.
Warton, D.I., Wright, T.W., Wang, Y. 2012. Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3, 89--101.
data(dune)
data(dune.env)
(sim <- with(dune.env, simper(dune, Management)))
summary(sim)Run the code above in your browser using DataLab