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‎docs/dev/generated/skbio.stats.distance.anosim.html‎

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<h1>skbio.stats.distance.anosim<a class="headerlink" href="#skbio-stats-distance-anosim" title="Link to this heading">#</a></h1>
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<dl class="py function">
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<dt class="sig sig-object py" id="skbio.stats.distance.anosim">
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<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">anosim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distance_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grouping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_anosim.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.anosim" title="Link to this definition">#</a></dt>
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<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">anosim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distmat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grouping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_anosim.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.anosim" title="Link to this definition">#</a></dt>
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<dd><p>Test for significant differences between groups using ANOSIM.</p>
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<p>Analysis of Similarities (ANOSIM) is a non-parametric method that tests
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whether two or more groups of objects (e.g., samples) are significantly
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<dl class="field-list">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><dl>
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<dt><strong>distance_matrix</strong><span class="classifier">DistanceMatrix</span></dt><dd><p>Distance matrix containing distances between objects (e.g., distances
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<dt><strong>distmat</strong><span class="classifier">DistanceMatrix</span></dt><dd><p>Distance matrix containing distances between objects (e.g., distances
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between samples of microbial communities).</p>
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<div class="versionchanged">
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<p><span class="versionmodified changed">Changed in version 0.7.0: </span>Renamed from <code class="docutils literal notranslate"><span class="pre">distance_matrix</span></code>. The old name is kept as an alias.</p>
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</div>
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</dd>
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<dt><strong>grouping</strong><span class="classifier">1-D array_like or pandas.DataFrame</span></dt><dd><p>Vector indicating the assignment of objects to groups. For example,
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these could be strings or integers denoting which group an object
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belongs to. If <cite>grouping</cite> is 1-D <code class="docutils literal notranslate"><span class="pre">array_like</span></code>, it must be the same
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length and in the same order as the objects in <cite>distance_matrix</cite>. If
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length and in the same order as the objects in <cite>distmat</cite>. If
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<cite>grouping</cite> is a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, the column specified by <cite>column</cite> will be
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used as the grouping vector. The <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> must be indexed by the
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IDs in <cite>distance_matrix</cite> (i.e., the row labels must be distance matrix
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IDs), but the order of IDs between <cite>distance_matrix</cite> and the
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IDs in <cite>distmat</cite> (i.e., the row labels must be distance matrix
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IDs), but the order of IDs between <cite>distmat</cite> and the
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<code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> need not be the same. All IDs in the distance matrix must
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be present in the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. Extra IDs in the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> are
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allowed (they are ignored in the calculations).</p>

‎docs/dev/generated/skbio.stats.distance.bioenv.html‎

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<h1>skbio.stats.distance.bioenv<a class="headerlink" href="#skbio-stats-distance-bioenv" title="Link to this heading">#</a></h1>
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<dl class="py function">
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<dt class="sig sig-object py" id="skbio.stats.distance.bioenv">
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<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">bioenv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distance_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_frame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_bioenv.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.bioenv" title="Link to this definition">#</a></dt>
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<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">bioenv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distmat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_frame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_bioenv.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.bioenv" title="Link to this definition">#</a></dt>
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<dd><p>Find subset of variables maximally correlated with distances.</p>
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<p>Finds subsets of variables whose Euclidean distances (after scaling the
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variables; see Notes section below for details) are maximally
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community distance matrix and all possible Euclidean environmental distance
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matrices at the given subset size. The combination of environmental
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variables with maximum correlation is chosen as the “best” subset.</p>
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<dl class="field-list simple">
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<dl class="field-list">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><dl class="simple">
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<dt><strong>distance_matrix</strong><span class="classifier">DistanceMatrix</span></dt><dd><p>Distance matrix containing distances between objects (e.g., distances
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<dd class="field-odd"><dl>
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<dt><strong>distmat</strong><span class="classifier">DistanceMatrix</span></dt><dd><p>Distance matrix containing distances between objects (e.g., distances
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between samples of microbial communities).</p>
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<div class="versionchanged">
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<p><span class="versionmodified changed">Changed in version 0.7.0: </span>Renamed from <code class="docutils literal notranslate"><span class="pre">distance_matrix</span></code>. The old name is kept as an alias.</p>
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</div>
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</dd>
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<dt><strong>data_frame</strong><span class="classifier">pandas.DataFrame</span></dt><dd><p>Contains columns of variables (e.g., numeric environmental variables
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such as pH) associated with the objects in <cite>distance_matrix</cite>. Must be
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indexed by the IDs in <cite>distance_matrix</cite> (i.e., the row labels must be
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distance matrix IDs), but the order of IDs between <cite>distance_matrix</cite>
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such as pH) associated with the objects in <cite>distmat</cite>. Must be
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indexed by the IDs in <cite>distmat</cite> (i.e., the row labels must be
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distance matrix IDs), but the order of IDs between <cite>distmat</cite>
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and <cite>data_frame</cite> need not be the same. All IDs in the distance matrix
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must be present in <cite>data_frame</cite>. Extra IDs in <cite>data_frame</cite> are allowed
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(they are ignored in the calculations).</p>
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<dt>TypeError</dt><dd><p>If invalid input types are provided, or if one or more specified
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columns in <cite>data_frame</cite> are not numeric.</p>
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</dd>
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<dt>ValueError</dt><dd><p>If column name(s) or <cite>distance_matrix</cite> IDs cannot be found in
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<dt>ValueError</dt><dd><p>If column name(s) or <cite>distmat</cite> IDs cannot be found in
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<cite>data_frame</cite>, if there is missing data (<code class="docutils literal notranslate"><span class="pre">NaN</span></code>) in the environmental
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variables, or if the environmental variables cannot be scaled (e.g.,
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due to zero variance).</p>

‎docs/dev/generated/skbio.stats.distance.html‎

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@@ -787,13 +787,13 @@ <h2>Distance-based statistics<a class="headerlink" href="#distance-based-statist
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<h3>Categorical Variable Stats<a class="headerlink" href="#categorical-variable-stats" title="Link to this heading">#</a></h3>
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<div class="pst-scrollable-table-container"><table class="autosummary longtable table">
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<tbody>
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<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.anosim.html#skbio.stats.distance.anosim" title="skbio.stats.distance.anosim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">anosim</span></code></a>(distance_matrix, grouping[, column, ...])</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.anosim.html#skbio.stats.distance.anosim" title="skbio.stats.distance.anosim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">anosim</span></code></a>(distmat, grouping[, column, ...])</p></td>
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<td><p>Test for significant differences between groups using ANOSIM.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="skbio.stats.distance.permanova.html#skbio.stats.distance.permanova" title="skbio.stats.distance.permanova"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permanova</span></code></a>(distance_matrix, grouping[, ...])</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="skbio.stats.distance.permanova.html#skbio.stats.distance.permanova" title="skbio.stats.distance.permanova"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permanova</span></code></a>(distmat, grouping[, column, ...])</p></td>
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<td><p>Test for significant differences between groups using PERMANOVA.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.permdisp.html#skbio.stats.distance.permdisp" title="skbio.stats.distance.permdisp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permdisp</span></code></a>(distance_matrix, grouping[, ...])</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.permdisp.html#skbio.stats.distance.permdisp" title="skbio.stats.distance.permdisp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">permdisp</span></code></a>(distmat, grouping[, column, test, ...])</p></td>
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<td><p>Test for Homogeneity of Multivariate Groups Disperisons.</p></td>
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</tr>
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</tbody>
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<h3>Continuous Variable Stats<a class="headerlink" href="#continuous-variable-stats" title="Link to this heading">#</a></h3>
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<div class="pst-scrollable-table-container"><table class="autosummary longtable table">
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<tbody>
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<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.bioenv.html#skbio.stats.distance.bioenv" title="skbio.stats.distance.bioenv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bioenv</span></code></a>(distance_matrix, data_frame[, columns])</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="skbio.stats.distance.bioenv.html#skbio.stats.distance.bioenv" title="skbio.stats.distance.bioenv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bioenv</span></code></a>(distmat, data_frame[, columns])</p></td>
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<td><p>Find subset of variables maximally correlated with distances.</p></td>
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</tr>
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</tbody>

‎docs/dev/generated/skbio.stats.distance.permanova.html‎

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<h1>skbio.stats.distance.permanova<a class="headerlink" href="#skbio-stats-distance-permanova" title="Link to this heading">#</a></h1>
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<dl class="py function">
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<dt class="sig sig-object py" id="skbio.stats.distance.permanova">
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<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">permanova</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distance_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grouping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_permanova.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.permanova" title="Link to this definition">#</a></dt>
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<span class="sig-prename descclassname"><span class="pre">skbio.stats.distance.</span></span><span class="sig-name descname"><span class="pre">permanova</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distmat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grouping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-bio/scikit-bio/blob/main/skbio/stats/distance/_permanova.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#skbio.stats.distance.permanova" title="Link to this definition">#</a></dt>
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<dd><p>Test for significant differences between groups using PERMANOVA.</p>
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<p>Permutational Multivariate Analysis of Variance (PERMANOVA) is a
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non-parametric method that tests whether two or more groups of objects
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<dl class="field-list">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><dl>
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<dt><strong>distance_matrix</strong><span class="classifier">DistanceMatrix</span></dt><dd><p>Distance matrix containing distances between objects (e.g., distances
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<dt><strong>distmat</strong><span class="classifier">DistanceMatrix</span></dt><dd><p>Distance matrix containing distances between objects (e.g., distances
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between samples of microbial communities).</p>
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<div class="versionchanged">
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<p><span class="versionmodified changed">Changed in version 0.7.0: </span>Renamed from <code class="docutils literal notranslate"><span class="pre">distance_matrix</span></code>. The old name is kept as an alias.</p>
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</div>
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</dd>
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<dt><strong>grouping</strong><span class="classifier">1-D array_like or pandas.DataFrame</span></dt><dd><p>Vector indicating the assignment of objects to groups. For example,
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these could be strings or integers denoting which group an object
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belongs to. If <cite>grouping</cite> is 1-D <code class="docutils literal notranslate"><span class="pre">array_like</span></code>, it must be the same
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length and in the same order as the objects in <cite>distance_matrix</cite>. If
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length and in the same order as the objects in <cite>distmat</cite>. If
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<cite>grouping</cite> is a <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>, the column specified by <cite>column</cite> will be
741744
used as the grouping vector. The <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> must be indexed by the
742-
IDs in <cite>distance_matrix</cite> (i.e., the row labels must be distance matrix
743-
IDs), but the order of IDs between <cite>distance_matrix</cite> and the
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IDs in <cite>distmat</cite> (i.e., the row labels must be distance matrix
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IDs), but the order of IDs between <cite>distmat</cite> and the
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<code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> need not be the same. All IDs in the distance matrix must
745748
be present in the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code>. Extra IDs in the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code> are
746749
allowed (they are ignored in the calculations).</p>

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