Description Usage Arguments Details Value Author(s) References See Also Examples
Implements a permutationbased test of multivariate homogeneity of
group dispersions (variances) for the results of a call to
betadisper
.
1 2 3 4 5 
x 
an object of class 
pairwise 
logical; perform pairwise comparisons of group means? 
permutations 
a list of control values for the permutations
as returned by the function 
parallel 
Number of parallel processes or a predefined socket
cluster. With 
... 
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

pairwise 
a list with components 
groups 
character; the levels of the grouping factor. 
control 
a list, the result of a call to

Gavin L. Simpson
Anderson, M.J. (2006) Distancebased 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.
For the main fitting function see betadisper
. For
an alternative approach to determining which groups are more variable,
see TukeyHSD.betadisper
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  data(varespec)
## BrayCurtis 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)
mod
## Perform test
anova(mod)
## Permutation test for F
pmod < permutest(mod, permutations = 99, pairwise = TRUE)
## Tukey's Honest Significant Differences
(mod.HSD < TukeyHSD(mod))
plot(mod.HSD)
## Has permustats() method
pstat < permustats(pmod)
densityplot(pstat, scales = list(x = list(relation = "free")))
qqmath(pstat, scales = list(relation = "free"))

Loading required package: permute
Loading required package: lattice
This is vegan 2.54
Homogeneity of multivariate dispersions
Call: betadisper(d = dis, group = groups)
No. of Positive Eigenvalues: 15
No. of Negative Eigenvalues: 8
Average distance to median:
grazed ungrazed
0.3926 0.2706
Eigenvalues for PCoA axes:
(Showing 8 of 23 eigenvalues)
PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
1.7552 1.1334 0.4429 0.3698 0.2454 0.1961 0.1751 0.1284
Analysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.07931 0.079306 4.6156 0.04295 *
Residuals 22 0.37801 0.017182

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Tukey multiple comparisons of means
95% familywise confidence level
Fit: aov(formula = distances ~ group, data = df)
$group
diff lwr upr p adj
ungrazedgrazed 0.1219422 0.2396552 0.004229243 0.0429502
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.