anosim(dat, grouping, permutations = 999, distance = "bray", strata = NULL,
parallel = getOption("mc.cores"))how, or the
number of permutations required, or a permutation matrix where each
row gives the permuted indices.vegdist for
options. This will be used if dat was not a dissimilarity
structure or a symmetrparallel = 1 uses ordinary, non-parallel
processing. The parallel processing is done with "anosim" with following
items:permustats.Between for dissimilarities
between classes and class name for corresponding dissimilarity
within class."method" entry of the dist object.how.anosim operates directly on a
dissimilarity matrix. A suitable dissimilarity matrix is produced by
functions dist or vegdist. The
method is philosophically allied with NMDS ordination
(monoMDS), in that it uses only the rank order of
dissimilarity values. If two groups of sampling units are really different in their species
composition, then compositional dissimilarities between the groups
ought to be greater than those within the groups. The anosim
statistic $R$ is based on the difference of mean ranks between
groups ($r_B$) and within groups ($r_W$):
$$R = (r_B - r_W)/(N (N-1) / 4)$$
The divisor is chosen so that $R$ will be in the interval $-1 \dots +1$, value $0$ indicating completely random grouping.
The statistical significance of observed $R$ is assessed by
permuting the grouping vector to obtain the empirical distribution
of $R$ under null-model. See permutations for
additional details on permutation tests in Vegan. The distribution
of simulated values can be inspected with the permustats
function.
The function has summary and plot methods. These both
show valuable information to assess the validity of the method: The
function assumes that all ranked dissimilarities within groups
have about equal median and range. The plot method uses
boxplot with options notch=TRUE and
varwidth=TRUE.
mrpp for a similar function using original
dissimilarities instead of their ranks.
dist and vegdist for obtaining
dissimilarities, and rank for ranking real values. For
comparing dissimilarities against continuous variables, see
mantel. Function adonis is a more robust
alternative that should preferred.data(dune)
data(dune.env)
dune.dist <- vegdist(dune)
attach(dune.env)
dune.ano <- anosim(dune.dist, Management)
summary(dune.ano)
plot(dune.ano)Run the code above in your browser using DataCamp Workspace