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<h1 id="missing-data"><span id="id1"></span>Missing Data<a class="headerlink" href="#missing-data" title="Link to this heading">¶</a></h1>
<p>All of the models can handle missing data. For performance reasons, the default is not to do any checking for missing data. If, however, you would like for missing data to be handled internally, you can do so by using the missing keyword argument. The default is to do nothing</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><code><span class="gp">In [1]: </span><span class="kn">import</span><span class="w"> </span><span class="nn">statsmodels.api</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sm</span>
<span class="gp">In [2]: </span><span class="n">data</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">longley</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="gp">In [3]: </span><span class="n">data</span><span class="o">.</span><span class="n">exog</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">exog</span><span class="p">)</span>
<span class="go"># add in some missing data</span>
<span class="gp">In [4]: </span><span class="n">missing_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="kc">False</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">endog</span><span class="p">))</span>
<span class="gp">In [5]: </span><span class="n">missing_idx</span><span class="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">True</span>
<span class="gp">In [6]: </span><span class="n">data</span><span class="o">.</span><span class="n">endog</span><span class="p">[</span><span class="n">missing_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="gp">In [7]: </span><span class="n">ols_model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">endog</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">exog</span><span class="p">)</span>
<span class="gp">In [8]: </span><span class="n">ols_fit</span> <span class="o">=</span> <span class="n">ols_model</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
<span class="gp">In [9]: </span><span class="nb">print</span><span class="p">(</span><span class="n">ols_fit</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
<span class="go">const NaN</span>
<span class="go">GNPDEFL NaN</span>
<span class="go">GNP NaN</span>
<span class="go">UNEMP NaN</span>
<span class="go">ARMED NaN</span>
<span class="go">POP NaN</span>
<span class="go">YEAR NaN</span>
<span class="go">dtype: float64</span>
</code></pre></div>
</div>
<p>This silently fails and all of the model parameters are NaN, which is probably not what you expected. If you are not sure whether or not you have missing data you can use <cite>missing = ‘raise’</cite>. This will raise a <cite>MissingDataError</cite> during model instantiation if missing data is present so that you know something was wrong in your input data.</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><code><span class="gp">In [10]: </span><span class="n">ols_model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">endog</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="o">=</span><span class="s1">'raise'</span><span class="p">)</span>
<span class="gt">---------------------------------------------------------------------------</span>
<span class="ne">MissingDataError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
<span class="ne">----> </span><span class="mi">1</span> <span class="n">ols_model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">endog</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="o">=</span><span class="s1">'raise'</span><span class="p">)</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:921,</span> in <span class="ni">OLS.__init__</span><span class="nt">(self, endog, exog, missing, hasconst, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">918</span> <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"Weights are not supported in OLS and will be ignored"</span>
<span class="g g-Whitespace"> </span><span class="mi">919</span> <span class="s2">"An exception will be raised in the next version."</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">920</span> <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">msg</span><span class="p">,</span> <span class="n">ValueWarning</span><span class="p">)</span>
<span class="ne">--> </span><span class="mi">921</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="o">=</span><span class="n">missing</span><span class="p">,</span>
<span class="g g-Whitespace"> </span><span class="mi">922</span> <span class="n">hasconst</span><span class="o">=</span><span class="n">hasconst</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">923</span> <span class="k">if</span> <span class="s2">"weights"</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_keys</span><span class="p">:</span>
<span class="g g-Whitespace"> </span><span class="mi">924</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_keys</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s2">"weights"</span><span class="p">)</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:746,</span> in <span class="ni">WLS.__init__</span><span class="nt">(self, endog, exog, weights, missing, hasconst, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">744</span> <span class="k">else</span><span class="p">:</span>
<span class="g g-Whitespace"> </span><span class="mi">745</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="ne">--> </span><span class="mi">746</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="o">=</span><span class="n">missing</span><span class="p">,</span>
<span class="g g-Whitespace"> </span><span class="mi">747</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">,</span> <span class="n">hasconst</span><span class="o">=</span><span class="n">hasconst</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">748</span> <span class="n">nobs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">exog</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="g g-Whitespace"> </span><span class="mi">749</span> <span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:200,</span> in <span class="ni">RegressionModel.__init__</span><span class="nt">(self, endog, exog, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">199</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="ne">--> </span><span class="mi">200</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">201</span> <span class="bp">self</span><span class="o">.</span><span class="n">pinv_wexog</span><span class="p">:</span> <span class="n">Float64Array</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span>
<span class="g g-Whitespace"> </span><span class="mi">202</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_attr</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="s1">'pinv_wexog'</span><span class="p">,</span> <span class="s1">'wendog'</span><span class="p">,</span> <span class="s1">'wexog'</span><span class="p">,</span> <span class="s1">'weights'</span><span class="p">])</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/base/model.py:275,</span> in <span class="ni">LikelihoodModel.__init__</span><span class="nt">(self, endog, exog, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">274</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="ne">--> </span><span class="mi">275</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">276</span> <span class="bp">self</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/base/model.py:97,</span> in <span class="ni">Model.__init__</span><span class="nt">(self, endog, exog, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">95</span> <span class="n">missing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'missing'</span><span class="p">,</span> <span class="s1">'none'</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">96</span> <span class="n">hasconst</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'hasconst'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="ne">---> </span><span class="mi">97</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_handle_data</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="p">,</span> <span class="n">hasconst</span><span class="p">,</span>
<span class="g g-Whitespace"> </span><span class="mi">98</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">99</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_constant</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">k_constant</span>
<span class="g g-Whitespace"> </span><span class="mi">100</span> <span class="bp">self</span><span class="o">.</span><span class="n">exog</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">exog</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/base/model.py:137,</span> in <span class="ni">Model._handle_data</span><span class="nt">(self, endog, exog, missing, hasconst, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">136</span> <span class="k">def</span><span class="w"> </span><span class="nf">_handle_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="p">,</span> <span class="n">hasconst</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="ne">--> </span><span class="mi">137</span> <span class="n">data</span> <span class="o">=</span> <span class="n">handle_data</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="p">,</span> <span class="n">hasconst</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">138</span> <span class="c1"># kwargs arrays could have changed, easier to just attach here</span>
<span class="g g-Whitespace"> </span><span class="mi">139</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/base/data.py:702,</span> in <span class="ni">handle_data</span><span class="nt">(endog, exog, missing, hasconst, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">699</span> <span class="n">exog</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">exog</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">701</span> <span class="n">klass</span> <span class="o">=</span> <span class="n">handle_data_class_factory</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">)</span>
<span class="ne">--> </span><span class="mi">702</span> <span class="k">return</span> <span class="n">klass</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="o">=</span><span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="o">=</span><span class="n">missing</span><span class="p">,</span> <span class="n">hasconst</span><span class="o">=</span><span class="n">hasconst</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/base/data.py:76,</span> in <span class="ni">ModelData.__init__</span><span class="nt">(self, endog, exog, missing, hasconst, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">74</span> <span class="bp">self</span><span class="o">.</span><span class="n">formula</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"formula"</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">75</span> <span class="k">if</span> <span class="n">missing</span> <span class="o">!=</span> <span class="s2">"none"</span><span class="p">:</span>
<span class="ne">---> </span><span class="mi">76</span> <span class="n">arrays</span><span class="p">,</span> <span class="n">nan_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle_missing</span><span class="p">(</span><span class="n">endog</span><span class="p">,</span> <span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">77</span> <span class="bp">self</span><span class="o">.</span><span class="n">missing_row_idx</span> <span class="o">=</span> <span class="n">nan_idx</span>
<span class="g g-Whitespace"> </span><span class="mi">78</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">arrays</span><span class="p">)</span> <span class="c1"># attach all the data arrays</span>
<span class="nn">File /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/statsmodels/base/data.py:297,</span> in <span class="ni">ModelData.handle_missing</span><span class="nt">(cls, endog, exog, missing, **kwargs)</span>
<span class="g g-Whitespace"> </span><span class="mi">294</span> <span class="k">return</span> <span class="n">combined</span><span class="p">,</span> <span class="p">[]</span>
<span class="g g-Whitespace"> </span><span class="mi">296</span> <span class="k">elif</span> <span class="n">missing</span> <span class="o">==</span> <span class="s2">"raise"</span><span class="p">:</span>
<span class="ne">--> </span><span class="mi">297</span> <span class="k">raise</span> <span class="n">MissingDataError</span><span class="p">(</span><span class="s2">"NaNs were encountered in the data"</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">299</span> <span class="k">elif</span> <span class="n">missing</span> <span class="o">==</span> <span class="s2">"drop"</span><span class="p">:</span>
<span class="g g-Whitespace"> </span><span class="mi">300</span> <span class="n">nan_mask</span> <span class="o">=</span> <span class="o">~</span><span class="n">nan_mask</span>
<span class="ne">MissingDataError</span>: NaNs were encountered in the data
</code></pre></div>
</div>
<p>If you want statsmodels to handle the missing data by dropping the observations, use <cite>missing = ‘drop’</cite>.</p>
<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><code><span class="gp">In [11]: </span><span class="n">ols_model</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">OLS</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">endog</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">exog</span><span class="p">,</span> <span class="n">missing</span><span class="o">=</span><span class="s1">'drop'</span><span class="p">)</span>
</code></pre></div>
</div>
<p>We are considering adding a configuration framework so that you can set the option with a global setting.</p>
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