Example #1
0
class TestRDAResults(object):
    # STATUS: L&L only shows results with scaling 1, and they agree
    # with vegan's (module multiplying by a constant). I can also
    # compute scaling 2, agreeing with vegan, but there are no written
    # results in L&L.
    def setup(self):
        """Data from table 11.3 in Legendre & Legendre 1998."""
        Y = np.loadtxt(get_data_path('example2_Y'))
        X = np.loadtxt(get_data_path('example2_X'))
        self.ordination = RDA(Y, X)

    def test_scaling1(self):
        scores = self.ordination.scores(1)

        # Load data as computed with vegan 2.0-8
        vegan_species = np.loadtxt(get_data_path(
            'example2_species_scaling1_from_vegan'))
        npt.assert_almost_equal(scores.species, vegan_species, decimal=6)

        vegan_site = np.loadtxt(get_data_path(
            'example2_site_scaling1_from_vegan'))
        npt.assert_almost_equal(scores.site, vegan_site, decimal=6)

    def test_scaling2(self):
        scores = self.ordination.scores(2)

        # Load data as computed with vegan 2.0-8
        vegan_species = np.loadtxt(get_data_path(
            'example2_species_scaling2_from_vegan'))
        npt.assert_almost_equal(scores.species, vegan_species, decimal=6)

        vegan_site = np.loadtxt(get_data_path(
            'example2_site_scaling2_from_vegan'))
        npt.assert_almost_equal(scores.site, vegan_site, decimal=6)
Example #2
0
class TestRDAResults(object):
    # STATUS: L&L only shows results with scaling 1, and they agree
    # with vegan's (module multiplying by a constant). I can also
    # compute scaling 2, agreeing with vegan, but there are no written
    # results in L&L.
    def setup(self):
        """Data from table 11.3 in Legendre & Legendre 1998."""
        Y = np.loadtxt(get_data_path('example2_Y'))
        X = np.loadtxt(get_data_path('example2_X'))
        self.ordination = RDA(Y, X)

    def test_scaling1(self):
        scores = self.ordination.scores(1)

        # Load data as computed with vegan 2.0-8
        vegan_species = np.loadtxt(
            get_data_path('example2_species_scaling1_from_vegan'))
        npt.assert_almost_equal(scores.species, vegan_species, decimal=6)

        vegan_site = np.loadtxt(
            get_data_path('example2_site_scaling1_from_vegan'))
        npt.assert_almost_equal(scores.site, vegan_site, decimal=6)

    def test_scaling2(self):
        scores = self.ordination.scores(2)

        # Load data as computed with vegan 2.0-8
        vegan_species = np.loadtxt(
            get_data_path('example2_species_scaling2_from_vegan'))
        npt.assert_almost_equal(scores.species, vegan_species, decimal=6)

        vegan_site = np.loadtxt(
            get_data_path('example2_site_scaling2_from_vegan'))
        npt.assert_almost_equal(scores.site, vegan_site, decimal=6)
Example #3
0
 def setup(self):
     """Data from table 11.3 in Legendre & Legendre 1998."""
     Y = np.loadtxt(get_data_path('example2_Y'))
     X = np.loadtxt(get_data_path('example2_X'))
     self.ordination = RDA(Y, X,
                           ['Site0', 'Site1', 'Site2', 'Site3', 'Site4',
                            'Site5', 'Site6', 'Site7', 'Site8', 'Site9'],
                           ['Species0', 'Species1', 'Species2', 'Species3',
                            'Species4', 'Species5'])
Example #4
0
 def setup(self):
     """Data from table 11.3 in Legendre & Legendre 1998."""
     Y = np.loadtxt(get_data_path('example2_Y'))
     X = np.loadtxt(get_data_path('example2_X'))
     self.ordination = RDA(Y, X)
Example #5
0
 def setup(self):
     """Data from table 11.3 in Legendre & Legendre 1998."""
     Y = np.loadtxt(get_data_path('example2_Y'))
     X = np.loadtxt(get_data_path('example2_X'))
     self.ordination = RDA(Y, X)
Example #6
0
import os
import numpy as np

from skbio.maths.stats.ordination import CA, RDA, CCA

path = os.path.dirname(os.path.abspath(__file__))


def get_path(fn):
    return os.path.join(path, os.pardir, 'maths', 'stats', 'ordination',
                        'test', 'data', fn)


X = np.loadtxt(get_path('L&L_CA_data'))
ordint = CA(X)
ordint.biplot(1)
ordint.biplot(2)

Y = np.loadtxt(get_path('example2_Y'))
X = np.loadtxt(get_path('example2_X')).reshape(-1, 4, order='F')
ordint = RDA(Y, X)
ordint.biplot()

Y = np.loadtxt(get_path('example3_Y'))
X = np.loadtxt(get_path('example3_X')).reshape(-1, 4, order='F')
ordint = CCA(Y, X)
ordint.biplot()