Discriminating species between two groups using Bray-Curtis dissimilarities
simper(comm, group, permutations = 999, parallel = 1, ...)
# S3 method for simper
summary(object, ordered = TRUE,
digits = max(3,getOption("digits") - 3), ...)A list of class "simper" with following items:
The species names.
Species contribution to average between-group dissimilarity.
The average between-group dissimilarity. This is the sum of
the item average.
Standard deviation of contribution.
Average to sd ratio.
Average abundances per group.
An index vector to order vectors by their contribution or
order cusum back to the original data order.
Ordered cumulative contribution. These are based on item
average, but they sum up to total 1.
Permutation \(p\)-value. Probability of getting a larger
or equal average contribution in random permutation of the group
factor. These area only available if permutations were used
(default: not calculated).
Community data.
Factor describing the group structure. If this is missing or has only one level, contributions are estimated for non-grouped data and dissimilarities only show the overall heterogeneity in species abundances.
a list of control values for the permutations
as returned by the function how, or the
number of permutations required, or a permutation matrix where each
row gives the permuted indices.
an object returned by simper.
Logical; Should the species be ordered by their average contribution?
Number of digits in output.
Number of parallel processes or a predefined socket
cluster. With parallel = 1 uses ordinary, non-parallel
processing. (Not yet implemented).
Parameters passed to other functions. In simper the
extra parameters are passed to shuffleSet if
permutations are used.
Eduard Szöcs and Jari Oksanen.
Similarity percentage, 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 contribution of each species to the
average between-group Bray-Curtis dissimilarity. Although the method
is called “Similarity Percentages”, it really studied
dissimilarities instead of similarities (Clarke 1993).
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 (including all species) 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 and
they are often misunderstood even in publications. The method gives
the contribution of each species to overall dissimilarities, but
these are caused by variation in species abundances, and only partly
by differences among groups. 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 random noise variation in species
abundances. The most abundant species usually have highest
variances, and they have high contributions even when they do not
differ among groups. Permutation tests study the differences among
groups, and they can be used to find out the species for which the
differences among groups is an important component of their
contribution to dissimilarities. Analysis without group
argument will find species contributions to the average overall
dissimilarity among sampling units. These non-grouped contributions
can be compared to grouped contributions to see how much added value
the grouping has for each species.
Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology, 18, 117–143.
Function meandist shows the average between-group
dissimilarities (as well as the within-group dissimilarities).
data(dune)
data(dune.env)
(sim <- with(dune.env, simper(dune, Management, permutations = 99)))
summary(sim)
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