- skbio.stats.distance.permanova(distance_matrix, grouping, column=None, permutations=999)#
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.
Distance matrix containing distances between objects (e.g., distances between samples of microbial communities).
- grouping1-D array_like or pandas.DataFrame
Vector indicating the assignment of objects to groups. For example, these could be strings or integers denoting which group an object belongs to. If grouping is 1-D
array_like, it must be the same length and in the same order as the objects in distance_matrix. If grouping is a
DataFrame, the column specified by column will be used as the grouping vector. The
DataFramemust be indexed by the IDs in distance_matrix (i.e., the row labels must be distance matrix IDs), but the order of IDs between distance_matrix and the
DataFrameneed not be the same. All IDs in the distance matrix must be present in the
DataFrame. Extra IDs in the
DataFrameare allowed (they are ignored in the calculations).
- columnstr, optional
Column name to use as the grouping vector if grouping is a
DataFrame. Must be provided if grouping is a
DataFrame. Cannot be provided if grouping is 1-D
- permutationsint, optional
Number of permutations to use when assessing statistical significance. Must be greater than or equal to zero. If zero, statistical significance calculations will be skipped and the p-value will be
Results of the statistical test, including
The p-value will be
np.nanif permutations is zero.
Anderson, Marti J. “A new method for non-parametric multivariate analysis of variance.” Austral Ecology 26.1 (2001): 32-46.
skbio.stats.distance.anosimfor usage examples (both functions provide similar interfaces).