Robust Linear Models
====================


.. _robust_models_0_notebook:

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   <div class="highlight"><pre><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
   <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
   <span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="kn">as</span> <span class="nn">sm</span>
   <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
   <span class="kn">from</span> <span class="nn">statsmodels.sandbox.regression.predstd</span> <span class="kn">import</span> <span class="n">wls_prediction_std</span>
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   <h2 id="estimation">Estimation</h2>
   <p>Load data:</p>
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   <div class="highlight"><pre><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">stackloss</span><span class="o">.</span><span class="n">load</span><span class="p">()</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>
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   <p>Huber&#39;s T norm with the (default) median absolute deviation scaling</p>
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   <div class="highlight"><pre><span class="n">huber_t</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">RLM</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">M</span><span class="o">=</span><span class="n">sm</span><span class="o">.</span><span class="n">robust</span><span class="o">.</span><span class="n">norms</span><span class="o">.</span><span class="n">HuberT</span><span class="p">())</span>
   <span class="n">hub_results</span> <span class="o">=</span> <span class="n">huber_t</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
   <span class="k">print</span><span class="p">(</span><span class="n">hub_results</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">hub_results</span><span class="o">.</span><span class="n">bse</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">hub_results</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="n">yname</span><span class="o">=</span><span class="s">&#39;y&#39;</span><span class="p">,</span>
               <span class="n">xname</span><span class="o">=</span><span class="p">[</span><span class="s">&#39;var_</span><span class="si">%d</span><span class="s">&#39;</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">hub_results</span><span class="o">.</span><span class="n">params</span><span class="p">))]))</span>
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   [-41.0265   0.8294   0.9261  -0.1278]
   [ 9.7919  0.111   0.3029  0.1286]
                       Robust linear Model Regression Results                    
   ==============================================================================
   Dep. Variable:                      y   No. Observations:                   21
   Model:                            RLM   Df Residuals:                       17
   Method:                          IRLS   Df Model:                            3
   Norm:                          HuberT                                         
   Scale Est.:                       mad                                         
   Cov Type:                          H1                                         
   Date:                Wed, 20 May 2015                                         
   Time:                        21:54:51                                         
   No. Iterations:                    19                                         
   ==============================================================================
                    coef    std err          z      P&gt;|z|      [95.0% Conf. Int.]
   ------------------------------------------------------------------------------
   var_0        -41.0265      9.792     -4.190      0.000       -60.218   -21.835
   var_1          0.8294      0.111      7.472      0.000         0.612     1.047
   var_2          0.9261      0.303      3.057      0.002         0.332     1.520
   var_3         -0.1278      0.129     -0.994      0.320        -0.380     0.124
   ==============================================================================
   
   If the model instance has been used for another fit with different fit
   parameters, then the fit options might not be the correct ones anymore .
   
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   <p>Huber&#39;s T norm with &#39;H2&#39; covariance matrix</p>
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   <div class="highlight"><pre><span class="n">hub_results2</span> <span class="o">=</span> <span class="n">huber_t</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">cov</span><span class="o">=</span><span class="s">&quot;H2&quot;</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">hub_results2</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">hub_results2</span><span class="o">.</span><span class="n">bse</span><span class="p">)</span>
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   [-41.0265   0.8294   0.9261  -0.1278]
   [ 9.0895  0.1195  0.3224  0.118 ]
   
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   <p>Andrew&#39;s Wave norm with Huber&#39;s Proposal 2 scaling and &#39;H3&#39; covariance matrix</p>
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   <div class="highlight"><pre><span class="n">andrew_mod</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">RLM</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">M</span><span class="o">=</span><span class="n">sm</span><span class="o">.</span><span class="n">robust</span><span class="o">.</span><span class="n">norms</span><span class="o">.</span><span class="n">AndrewWave</span><span class="p">())</span>
   <span class="n">andrew_results</span> <span class="o">=</span> <span class="n">andrew_mod</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">scale_est</span><span class="o">=</span><span class="n">sm</span><span class="o">.</span><span class="n">robust</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">HuberScale</span><span class="p">(),</span> <span class="n">cov</span><span class="o">=</span><span class="s">&quot;H3&quot;</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="s">&#39;Parameters: &#39;</span><span class="p">,</span> <span class="n">andrew_results</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
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   Parameters:  [-40.8818   0.7928   1.0486  -0.1336]
   
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   <p>See <code>help(sm.RLM.fit)</code> for more options and <code>module sm.robust.scale</code> for scale options</p>
   <h2 id="comparing-ols-and-rlm">Comparing OLS and RLM</h2>
   <p>Artificial data with outliers:</p>
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   <div class="highlight"><pre><span class="n">nsample</span> <span class="o">=</span> <span class="mi">50</span>
   <span class="n">x1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">nsample</span><span class="p">)</span>
   <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">x1</span><span class="p">,</span> <span class="p">(</span><span class="n">x1</span><span class="o">-</span><span class="mi">5</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
   <span class="n">X</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">X</span><span class="p">)</span>
   <span class="n">sig</span> <span class="o">=</span> <span class="mf">0.3</span>   <span class="c"># smaller error variance makes OLS&lt;-&gt;RLM contrast bigger</span>
   <span class="n">beta</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0</span><span class="p">]</span>
   <span class="n">y_true2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">beta</span><span class="p">)</span>
   <span class="n">y2</span> <span class="o">=</span> <span class="n">y_true2</span> <span class="o">+</span> <span class="n">sig</span><span class="o">*</span><span class="mf">1.</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">nsample</span><span class="p">)</span>
   <span class="n">y2</span><span class="p">[[</span><span class="mi">39</span><span class="p">,</span><span class="mi">41</span><span class="p">,</span><span class="mi">43</span><span class="p">,</span><span class="mi">45</span><span class="p">,</span><span class="mi">48</span><span class="p">]]</span> <span class="o">-=</span> <span class="mi">5</span>   <span class="c"># add some outliers (10% of nsample)</span>
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   <h3 id="example-1-quadratic-function-with-linear-truth">Example 1: quadratic function with linear truth</h3>
   <p>Note that the quadratic term in OLS regression will capture outlier effects. </p>
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   <div class="highlight"><pre><span class="n">res</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">y2</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
   <span class="k">print</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">bse</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">predict</span><span class="p">())</span>
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   [ 4.901   0.5565 -0.0163]
   [ 0.4516  0.0697  0.0062]
   [  4.4938   4.7847   5.0702   5.3502   5.6248   5.894    6.1578   6.4161
      6.669    6.9165   7.1586   7.3952   7.6264   7.8522   8.0725   8.2875
      8.497    8.701    8.8997   9.0929   9.2807   9.463    9.64     9.8115
      9.9776  10.1382  10.2934  10.4432  10.5876  10.7265  10.8601  10.9881
     11.1108  11.228   11.3399  11.4462  11.5472  11.6427  11.7328  11.8175
     11.8967  11.9705  12.0389  12.1019  12.1594  12.2115  12.2582  12.2995
     12.3353  12.3657]
   
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   <p>Estimate RLM:</p>
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   <div class="highlight"><pre><span class="n">resrlm</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">RLM</span><span class="p">(</span><span class="n">y2</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
   <span class="k">print</span><span class="p">(</span><span class="n">resrlm</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">resrlm</span><span class="o">.</span><span class="n">bse</span><span class="p">)</span>
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   [ 4.8678  0.5385 -0.0056]
   [ 0.1287  0.0199  0.0018]
   
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   <p>Draw a plot to compare OLS estimates to the robust estimates:</p>
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   <div class="highlight"><pre><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
   <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="s">&#39;o&#39;</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s">&quot;data&quot;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y_true2</span><span class="p">,</span> <span class="s">&#39;b-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;True&quot;</span><span class="p">)</span>
   <span class="n">prstd</span><span class="p">,</span> <span class="n">iv_l</span><span class="p">,</span> <span class="n">iv_u</span> <span class="o">=</span> <span class="n">wls_prediction_std</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">fittedvalues</span><span class="p">,</span> <span class="s">&#39;r-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;OLS&quot;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">iv_u</span><span class="p">,</span> <span class="s">&#39;r--&#39;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">iv_l</span><span class="p">,</span> <span class="s">&#39;r--&#39;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">resrlm</span><span class="o">.</span><span class="n">fittedvalues</span><span class="p">,</span> <span class="s">&#39;g.-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;RLM&quot;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s">&quot;best&quot;</span><span class="p">)</span>
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   <h3 id="example-2-linear-function-with-linear-truth">Example 2: linear function with linear truth</h3>
   <p>Fit a new OLS model using only the linear term and the constant:</p>
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   <div class="highlight"><pre><span class="n">X2</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]]</span> 
   <span class="n">res2</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">y2</span><span class="p">,</span> <span class="n">X2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
   <span class="k">print</span><span class="p">(</span><span class="n">res2</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">res2</span><span class="o">.</span><span class="n">bse</span><span class="p">)</span>
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   [ 5.5575  0.3936]
   [ 0.3997  0.0344]
   
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   <p>Estimate RLM:</p>
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   <div class="highlight"><pre><span class="n">resrlm2</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">RLM</span><span class="p">(</span><span class="n">y2</span><span class="p">,</span> <span class="n">X2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">()</span>
   <span class="k">print</span><span class="p">(</span><span class="n">resrlm2</span><span class="o">.</span><span class="n">params</span><span class="p">)</span>
   <span class="k">print</span><span class="p">(</span><span class="n">resrlm2</span><span class="o">.</span><span class="n">bse</span><span class="p">)</span>
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   [ 5.038   0.4885]
   [ 0.1162  0.01  ]
   
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   <p>Draw a plot to compare OLS estimates to the robust estimates:</p>
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   <div class="highlight"><pre><span class="n">prstd</span><span class="p">,</span> <span class="n">iv_l</span><span class="p">,</span> <span class="n">iv_u</span> <span class="o">=</span> <span class="n">wls_prediction_std</span><span class="p">(</span><span class="n">res2</span><span class="p">)</span>
   
   <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="s">&#39;o&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;data&quot;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y_true2</span><span class="p">,</span> <span class="s">&#39;b-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;True&quot;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">res2</span><span class="o">.</span><span class="n">fittedvalues</span><span class="p">,</span> <span class="s">&#39;r-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;OLS&quot;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">iv_u</span><span class="p">,</span> <span class="s">&#39;r--&#39;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">iv_l</span><span class="p">,</span> <span class="s">&#39;r--&#39;</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">resrlm2</span><span class="o">.</span><span class="n">fittedvalues</span><span class="p">,</span> <span class="s">&#39;g.-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;RLM&quot;</span><span class="p">)</span>
   <span class="n">legend</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s">&quot;best&quot;</span><span class="p">)</span>
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