anosim | R Documentation |
Analysis of similarities (ANOSIM) provides a way to test statistically whether there is a significant difference between two or more groups of sampling units.
anosim(x, grouping, permutations = 999, distance = "bray", strata = NULL,
parallel = getOption("mc.cores"))
x |
Data matrix or data frame in which rows are samples and columns are response variable(s), or a dissimilarity object or a symmetric square matrix of dissimilarities. |
grouping |
Factor for grouping observations. |
permutations |
a list of control values for the permutations
as returned by the function |
distance |
Choice of distance metric that measures the
dissimilarity between two observations. See |
strata |
An integer vector or factor specifying the strata for permutation. If supplied, observations are permuted only within the specified strata. |
parallel |
Number of parallel processes or a predefined socket
cluster. With |
Analysis of similarities (ANOSIM) provides a way to test statistically
whether there is a significant difference between two or more groups
of sampling units. Function 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
.
The function returns a list of class "anosim"
with following
items:
call |
Function call. |
statistic |
The value of ANOSIM statistic |
signif |
Significance from permutation. |
perm |
Permutation values of |
class.vec |
Factor with value |
dis.rank |
Rank of dissimilarity entry. |
dissimilarity |
The name of the dissimilarity index: the
|
control |
A list of control values for the permutations
as returned by the function |
The anosim
function can confound the differences between groups
and dispersion within groups and the results can be difficult to
interpret (cf. Warton et al. 2012). The function returns a lot of
information to ease studying its performance. Most anosim
models could be analysed with adonis2
which seems to be a
more robust alternative.
Jari Oksanen, with a help from Peter R. Minchin.
Clarke, K. R. (1993). Non-parametric multivariate analysis of changes in community structure. Australian Journal of Ecology 18, 117–143.
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
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 adonis2
is a more robust
alternative that should preferred.
data(dune)
data(dune.env)
dune.dist <- vegdist(dune)
dune.ano <- with(dune.env, anosim(dune.dist, Management))
summary(dune.ano)
plot(dune.ano)
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