skbio.stats.distance.
permanova
(distance_matrix, grouping, column=None, permutations=999)[source]¶Test for significant differences between groups using PERMANOVA.
State: Experimental as of 0.4.0.
Permutational Multivariate Analysis of Variance (PERMANOVA) is a non-parametric method that tests whether two or more groups of objects (e.g., samples) are significantly different based on a categorical factor. It is conceptually similar to ANOVA except that it operates on a distance matrix, which allows for multivariate analysis. PERMANOVA computes a pseudo-F statistic.
Statistical significance is assessed via a permutation test. The assignment of objects to groups (grouping) is randomly permuted a number of times (controlled via permutations). A pseudo-F statistic is computed for each permutation and the p-value is the proportion of permuted pseudo-F statisics that are equal to or greater than the original (unpermuted) pseudo-F statistic.
Parameters: | distance_matrix : DistanceMatrix
grouping : 1-D array_like or pandas.DataFrame
column : str, optional
permutations : int, optional
|
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Returns: | pandas.Series
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See also
Notes
See [R80] for the original method reference, as well as vegan::adonis
,
available in R’s vegan package [R81].
The p-value will be np.nan
if permutations is zero.
References
[R80] | (1, 2) Anderson, Marti J. “A new method for non-parametric multivariate analysis of variance.” Austral Ecology 26.1 (2001): 32-46. |
[R81] | (1, 2) http://cran.r-project.org/web/packages/vegan/index.html |
Examples
See skbio.stats.distance.anosim
for usage examples (both functions
provide similar interfaces).