anosim {vegan} | 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(dat, grouping, permutations = 999, distance = "bray", strata)

`dat` |
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` |
Number of permutation to assess the significance of the ANOSIM statistic. |

`distance` |
Choice of distance metric that measures the
dissimilarity between two observations . See `vegdist` for
options. This will be used if `dat` was not a dissimilarity
structure or a symmetric square matrix. |

`strata` |
An integer vector or factor specifying the strata for permutation. If supplied, observations are permuted only within the specified strata. |

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
(`isoMDS`

), 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 ... +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.

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 R |

`signif` |
Significance from permutation. |

`perm` |
Permutation values of R |

`class.vec` |
Factor with value `Between` for dissimilarities
between classes and class name for corresponding dissimilarity
within class. |

`dis.rank` |
Rank of dissimilarity entry. |

`dissimilarity` |
The name of the dissimilarity index: the
`"method"` entry of the `dist` object. |

I don't quite trust this method. Somebody should study its
performance carefully. The function returns a lot of information
to ease further scrutiny. Most `anosim`

models could be analysed
with `adonis`

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.

`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)

[Package *vegan* version 1.16-32 Index]