qiime diversity pcoa (version 2019.4)

--i-distance-matrix: ARTIFACT DistanceMatrix The distance matrix on which PCoA should be computed. [required]:

--p-number-of-dimensions: INTEGER Range(1, None) Dimensions to reduce the distance matrix to. This number determines how many eigenvectors and eigenvalues are returned,and influences the choice of algorithm used to compute them. By default, uses the default eigendecomposition method, SciPy's eigh, which computes all eigenvectors and eigenvalues in an exact manner. For very large matrices, this is expected to be slow. If a value is specified for this parameter, then the fast, heuristic eigendecomposition algorithm fsvd is used, which only computes and returns the number of dimensions specified, but suffers some degree of accuracy loss, the magnitude of which varies across different datasets. [optional]:

Principal Coordinate Analysis

Apply principal coordinate analysis.

Parameters

distance_matrix : DistanceMatrix: The distance matrix on which PCoA should be computed.
number_of_dimensions : Int % Range(1, None), optional: Dimensions to reduce the distance matrix to. This number determines how many eigenvectors and eigenvalues are returned,and influences the choice of algorithm used to compute them. By default, uses the default eigendecomposition method, SciPy's eigh, which computes all eigenvectors and eigenvalues in an exact manner. For very large matrices, this is expected to be slow. If a value is specified for this parameter, then the fast, heuristic eigendecomposition algorithm fsvd is used, which only computes and returns the number of dimensions specified, but suffers some degree of accuracy loss, the magnitude of which varies across different datasets.

Returns

pcoa : PCoAResults: The resulting PCoA matrix.