permutest.betadisper {vegan}R Documentation

Permutation test of multivariate homogeneity of groups dispersions (variances)


Implements a permutation-based test of multivariate homogeneity of group dispersions (variances) for the results of a call to betadisper.


## S3 method for class 'betadisper':
permutest(x, pairwise = FALSE,
         control = permControl(nperm = 999), ...)


x an object of class "betadisper", the result of a call to betadisper.
pairwise logical; perform pairwise comparisons of group means?
control a list of control values for the permutations to replace the default values returned by the function permControl
... Arguments passed to other methods.


To test if one or more groups is more variable than the others, ANOVA of the distances to group centroids can be performed and parametric theory used to interpret the significance of F. An alternative is to use a permutation test. permutest.betadisper permutes model residuals to generate a permutation distribution of F under the Null hypothesis of no difference in dispersion between groups.

Pairwise comparisons of group mean dispersions can be performed by setting argument pairwise to TRUE. A classical t test is performed on the pairwise group dispersions. This is combined with a permutation test based on the t statistic calculated on pairwise group dispersions. An alternative to the classical comparison of group dispersions, is to calculate Tukey's Honest Significant Differences between groups, via TukeyHSD.betadisper.


permutest.betadisper returns a list of class "permutest.betadisper" with the following components:

tab the ANOVA table which is an object inheriting from class "data.frame".
pairwise a list with components observed and permuted containing the observed and permuted p-values for pairwise comparisons of group mean distances (dispersions or variances).
groups character; the levels of the grouping factor.
control a list, the result of a call to permControl.


Gavin L. Simpson


Anderson, M.J. (2006) Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62(1), 245–253.

Anderson, M.J., Ellingsen, K.E. & McArdle, B.H. (2006) Multivariate dispersion as a measure of beta diversity. Ecology Letters 9(6), 683–693.

See Also

For the main fitting function see betadisper. For an alternative approach to determining which groups are more variable, see TukeyHSD.betadisper.



## Bray-Curtis distances between samples
dis <- vegdist(varespec)

## First 16 sites grazed, remaining 8 sites ungrazed
groups <- factor(c(rep(1,16), rep(2,8)), labels = c("grazed","ungrazed"))

## Calculate multivariate dispersions
mod <- betadisper(dis, groups)

## Perform test

## Permutation test for F
permutest(mod, pairwise = TRUE)

## Tukey's Honest Significant Differences
(mod.HSD <- TukeyHSD(mod))

[Package vegan version 1.16-32 Index]