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adonis: Beta diversity group significance

Citations

[diversity:adonis:And01]Marti J Anderson. A new method for non-parametric multivariate analysis of variance. Austral ecology, 26(1):32–46, 2001. doi:https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x.

Docstring:

Usage: qiime diversity adonis [OPTIONS]

  Determine whether groups of samples are significantly different from one
  another using the ADONIS permutation-based statistical test in vegan-R.
  The function partitions sums of squares of a multivariate data set, and is
  directly analogous to MANOVA (multivariate analysis of variance). This
  action differs from beta_group_signficance in that it accepts R formulae
  to perform multi-way ADONIS tests; beta_group_signficance only performs
  one-way tests. For more details see
  http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/adonis.html

Options:
  --i-distance-matrix ARTIFACT PATH DistanceMatrix
                                  Matrix of distances between pairs of
                                  samples.  [required]
  --m-metadata-file MULTIPLE FILE
                                  Metadata file or artifact viewable as
                                  metadata. This option may be supplied
                                  multiple times to merge metadata. Sample
                                  metadata containing formula terms.
                                  [required]
  --p-formula TEXT                Model formula containing only independent
                                  terms contained in the sample metadata.
                                  These can be continuous variables or
                                  factors, and they can have interactions as
                                  in a typical R formula. E.g., the formula
                                  "treatment+block" would test whether the
                                  input distance matrix partitions based on
                                  "treatment" and "block" sample metadata. The
                                  formula "treatment*block" would test both of
                                  those effects as well as their interaction.
                                  Enclose formulae in quotes to avoid
                                  unpleasant surprises.  [required]
  --p-permutations INTEGER RANGE  The number of permutations to be run when
                                  computing p-values.  [default: 999]
  --p-n-jobs INTEGER RANGE        Number of parallel processes to run.
                                  [default: 1]
  --o-visualization VISUALIZATION PATH
                                  [required if not passing --output-dir]
  --output-dir DIRECTORY          Output unspecified results to a directory
  --cmd-config FILE               Use config file for command options
  --verbose                       Display verbose output to stdout and/or
                                  stderr during execution of this action.
                                  [default: False]
  --quiet                         Silence output if execution is successful
                                  (silence is golden).  [default: False]
  --citations                     Show citations and exit.
  --help                          Show this message and exit.

Import:

from qiime2.plugins.diversity.visualizers import adonis

Docstring:

Beta diversity group significance

Determine whether groups of samples are significantly different from one
another using the ADONIS permutation-based statistical test in vegan-R. The
function partitions sums of squares of a multivariate data set, and is
directly analogous to MANOVA (multivariate analysis of variance). This
action differs from beta_group_signficance in that it accepts R formulae to
perform multi-way ADONIS tests; beta_group_signficance only performs one-
way tests. For more details see
http://cc.oulu.fi/~jarioksa/softhelp/vegan/html/adonis.html

Parameters
----------
distance_matrix : DistanceMatrix
    Matrix of distances between pairs of samples.
metadata : Metadata
    Sample metadata containing formula terms.
formula : Str
    Model formula containing only independent terms contained in the sample
    metadata. These can be continuous variables or factors, and they can
    have interactions as in a typical R formula. E.g., the formula
    "treatment+block" would test whether the input distance matrix
    partitions based on "treatment" and "block" sample metadata. The
    formula "treatment*block" would test both of those effects as well as
    their interaction. Enclose formulae in quotes to avoid unpleasant
    surprises.
permutations : Int % Range(1, None), optional
    The number of permutations to be run when computing p-values.
n_jobs : Int % Range(1, None), optional
    Number of parallel processes to run.

Returns
-------
visualization : Visualization