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<h1 id="sandbox"><span id="id1"></span>Sandbox<a class="headerlink" href="#sandbox" title="Link to this heading">¶</a></h1>
<p>This sandbox contains code that is for various reasons not ready to be
included in statsmodels proper. It contains modules from the old stats.models
code that have not been tested, verified and updated to the new statsmodels
structure: cox survival model, mixed effects model with repeated measures,
generalized additive model and the formula framework. The sandbox also
contains code that is currently being worked on until it fits the pattern
of statsmodels or is sufficiently tested.</p>
<p>All sandbox modules have to be explicitly imported to indicate that they are
not yet part of the core of statsmodels. The quality and testing of the
sandbox code varies widely.</p>
<h2 id="examples">Examples<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h2>
<p>There are some examples in the <cite>sandbox.examples</cite> folder. Additional
examples are directly included in the modules and in subfolders of
the sandbox.</p>
<h2 id="module-reference">Module Reference<a class="headerlink" href="#module-reference" title="Link to this heading">¶</a></h2>
<h3 id="time-series-analysis-tsa">Time Series analysis <code class="xref py py-mod docutils literal notranslate"><span class="pre">tsa</span></code><a class="headerlink" href="#time-series-analysis-tsa" title="Link to this heading">¶</a></h3>
<p>In this part we develop models and functions that will be useful for time
series analysis. Most of the models and function have been moved to
<a class="reference internal" href="tsa.html#module-statsmodels.tsa" title="statsmodels.tsa: Time-series analysis"><code class="xref py py-mod docutils literal notranslate"><span class="pre">statsmodels.tsa</span></code></a>.</p>
<h4 id="moving-window-statistics">Moving Window Statistics<a class="headerlink" href="#moving-window-statistics" title="Link to this heading">¶</a></h4>
<p>Most moving window statistics, like rolling mean, moments (up to 4th order), min,
max, mean, and variance, are covered by the functions for <a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html#window-functions">Moving (rolling)
statistics/moments</a> in Pandas.</p>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.tsa.movstat.movorder.html#statsmodels.sandbox.tsa.movstat.movorder" title="statsmodels.sandbox.tsa.movstat.movorder (Python function) — moving order statistics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">movstat.movorder</span></code></a>(x[, order, windsize, lag])</p></td>
<td><p>moving order statistics</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.tsa.movstat.movmean.html#statsmodels.sandbox.tsa.movstat.movmean" title="statsmodels.sandbox.tsa.movstat.movmean (Python function) — moving window mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">movstat.movmean</span></code></a>(x[, windowsize, lag])</p></td>
<td><p>moving window mean</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.tsa.movstat.movvar.html#statsmodels.sandbox.tsa.movstat.movvar" title="statsmodels.sandbox.tsa.movstat.movvar (Python function) — moving window variance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">movstat.movvar</span></code></a>(x[, windowsize, lag])</p></td>
<td><p>moving window variance</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.tsa.movstat.movmoment.html#statsmodels.sandbox.tsa.movstat.movmoment" title="statsmodels.sandbox.tsa.movstat.movmoment (Python function) — non-central moment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">movstat.movmoment</span></code></a>(x, k[, windowsize, lag])</p></td>
<td><p>non-central moment</p></td>
</tr>
</tbody>
</table>
<h3 id="module-statsmodels.sandbox.regression.anova_nistcertified"><span id="regression-and-anova"></span>Regression and ANOVA<a class="headerlink" href="#module-statsmodels.sandbox.regression.anova_nistcertified" title="Link to this heading">¶</a></h3>
<p>The following two ANOVA functions are fully tested against the NIST test data
for balanced one-way ANOVA. <code class="docutils literal notranslate"><span class="pre">anova_oneway</span></code> follows the same pattern as the
oneway anova function in scipy.stats but with higher precision for badly
scaled problems. <code class="docutils literal notranslate"><span class="pre">anova_ols</span></code> produces the same results as the one way anova
however using the OLS model class. It also verifies against the NIST tests,
with some problems in the worst scaled cases. It shows how to do simple ANOVA
using statsmodels in three lines and is also best taken as a recipe.</p>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.anova_nistcertified.anova_oneway.html#statsmodels.sandbox.regression.anova_nistcertified.anova_oneway" title="statsmodels.sandbox.regression.anova_nistcertified.anova_oneway (Python function) — !! processed by numpydoc !!"><code class="xref py py-obj docutils literal notranslate"><span class="pre">anova_oneway</span></code></a>(y, x[, seq])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.anova_nistcertified.anova_ols.html#statsmodels.sandbox.regression.anova_nistcertified.anova_ols" title="statsmodels.sandbox.regression.anova_nistcertified.anova_ols (Python function) — !! processed by numpydoc !!"><code class="xref py py-obj docutils literal notranslate"><span class="pre">anova_ols</span></code></a>(y, x)</p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
<p>The following are helper functions for working with dummy variables and
generating ANOVA results with OLS. They are best considered as recipes since
they were written with a specific use in mind. These function will eventually
be rewritten or reorganized.</p>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_ols_anova.data2dummy.html#statsmodels.sandbox.regression.try_ols_anova.data2dummy" title="statsmodels.sandbox.regression.try_ols_anova.data2dummy (Python function) — convert array of categories to dummy variables by default drops dummy variable for last category uses ravel, 1d only"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_ols_anova.data2dummy</span></code></a>(x[, returnall])</p></td>
<td><p>convert array of categories to dummy variables by default drops dummy variable for last category uses ravel, 1d only</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_ols_anova.data2groupcont.html#statsmodels.sandbox.regression.try_ols_anova.data2groupcont" title="statsmodels.sandbox.regression.try_ols_anova.data2groupcont (Python function) — create dummy continuous variable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_ols_anova.data2groupcont</span></code></a>(x1, x2)</p></td>
<td><p>create dummy continuous variable</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_ols_anova.data2proddummy.html#statsmodels.sandbox.regression.try_ols_anova.data2proddummy" title="statsmodels.sandbox.regression.try_ols_anova.data2proddummy (Python function) — creates product dummy variables from 2 columns of 2d array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_ols_anova.data2proddummy</span></code></a>(x)</p></td>
<td><p>creates product dummy variables from 2 columns of 2d array</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_ols_anova.dropname.html#statsmodels.sandbox.regression.try_ols_anova.dropname" title="statsmodels.sandbox.regression.try_ols_anova.dropname (Python function) — drop names from a list of strings, names to drop are in space delimited list does not change original list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_ols_anova.dropname</span></code></a>(ss, li)</p></td>
<td><p>drop names from a list of strings, names to drop are in space delimited list does not change original list</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_ols_anova.form2design.html#statsmodels.sandbox.regression.try_ols_anova.form2design" title="statsmodels.sandbox.regression.try_ols_anova.form2design (Python function) — convert string formula to data dictionary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_ols_anova.form2design</span></code></a>(ss, data)</p></td>
<td><p>convert string formula to data dictionary</p></td>
</tr>
</tbody>
</table>
<p>The following are helper functions for group statistics where groups are
defined by a label array. The qualifying comments for the previous group
apply also to this group of functions.</p>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.cat2dummy.html#statsmodels.sandbox.regression.try_catdata.cat2dummy" title="statsmodels.sandbox.regression.try_catdata.cat2dummy (Python function) — !! processed by numpydoc !!"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.cat2dummy</span></code></a>(y[, nonseq])</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.convertlabels.html#statsmodels.sandbox.regression.try_catdata.convertlabels" title="statsmodels.sandbox.regression.try_catdata.convertlabels (Python function) — convert labels based on multiple variables or string labels to unique index labels 0,1,2,...,nk-1 where nk is the number of distinct labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.convertlabels</span></code></a>(ys[, indices])</p></td>
<td><p>convert labels based on multiple variables or string labels to unique index labels 0,1,2,...,nk-1 where nk is the number of distinct labels</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.groupsstats_1d.html#statsmodels.sandbox.regression.try_catdata.groupsstats_1d" title="statsmodels.sandbox.regression.try_catdata.groupsstats_1d (Python function) — use ndimage to get fast mean and variance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.groupsstats_1d</span></code></a>(y, x, labelsunique)</p></td>
<td><p>use ndimage to get fast mean and variance</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.groupsstats_dummy.html#statsmodels.sandbox.regression.try_catdata.groupsstats_dummy" title="statsmodels.sandbox.regression.try_catdata.groupsstats_dummy (Python function) — !! processed by numpydoc !!"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.groupsstats_dummy</span></code></a>(y, x[, nonseq])</p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.groupstatsbin.html#statsmodels.sandbox.regression.try_catdata.groupstatsbin" title="statsmodels.sandbox.regression.try_catdata.groupstatsbin (Python function) — uses np.bincount, assumes factors/labels are integers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.groupstatsbin</span></code></a>(factors, values)</p></td>
<td><p>uses np.bincount, assumes factors/labels are integers</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.labelmeanfilter.html#statsmodels.sandbox.regression.try_catdata.labelmeanfilter" title="statsmodels.sandbox.regression.try_catdata.labelmeanfilter (Python function) — !! processed by numpydoc !!"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.labelmeanfilter</span></code></a>(y, x)</p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd.html#statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd" title="statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd (Python function) — !! processed by numpydoc !!"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.labelmeanfilter_nd</span></code></a>(y, x)</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.regression.try_catdata.labelmeanfilter_str.html#statsmodels.sandbox.regression.try_catdata.labelmeanfilter_str" title="statsmodels.sandbox.regression.try_catdata.labelmeanfilter_str (Python function) — !! processed by numpydoc !!"><code class="xref py py-obj docutils literal notranslate"><span class="pre">try_catdata.labelmeanfilter_str</span></code></a>(ys, x)</p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
<p>Additional to these functions, sandbox regression still contains several
examples, that are illustrative of the use of the regression models of
statsmodels.</p>
<h3 id="systems-of-regression-equations-and-simultaneous-equations">Systems of Regression Equations and Simultaneous Equations<a class="headerlink" href="#systems-of-regression-equations-and-simultaneous-equations" title="Link to this heading">¶</a></h3>
<p>The following are for fitting systems of equations models.  Though the returned
parameters have been verified as accurate, this code is still very
experimental, and the usage of the models will very likely change significantly
before they are added to the main codebase.</p>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.sysreg.SUR.html#statsmodels.sandbox.sysreg.SUR" title="statsmodels.sandbox.sysreg.SUR (Python class) — Seemingly Unrelated Regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SUR</span></code></a>(sys[, sigma, dfk])</p></td>
<td><p>Seemingly Unrelated Regression</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.sysreg.Sem2SLS.html#statsmodels.sandbox.sysreg.Sem2SLS" title="statsmodels.sandbox.sysreg.Sem2SLS (Python class) — Two-Stage Least Squares for Simultaneous equations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Sem2SLS</span></code></a>(sys[, indep_endog, instruments])</p></td>
<td><p>Two-Stage Least Squares for Simultaneous equations</p></td>
</tr>
</tbody>
</table>
<h3 id="module-statsmodels.sandbox.tools.tools_tsa"><span id="miscellaneous"></span>Miscellaneous<a class="headerlink" href="#module-statsmodels.sandbox.tools.tools_tsa" title="Link to this heading">¶</a></h3>
<h4 id="module-statsmodels.sandbox"><span id="descriptive-statistics-printing"></span>Descriptive Statistics Printing<a class="headerlink" href="#module-statsmodels.sandbox" title="Link to this heading">¶</a></h4>
<table class="autosummary longtable docutils data align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.descstats.sign_test.html#statsmodels.sandbox.descstats.sign_test" title="statsmodels.sandbox.descstats.sign_test (Python function) — Signs test"><code class="xref py py-obj docutils literal notranslate"><span class="pre">descstats.sign_test</span></code></a>(samp[, mu0])</p></td>
<td><p>Signs test</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/statsmodels.sandbox.descstats.descstats.html#statsmodels.sandbox.descstats.descstats" title="statsmodels.sandbox.descstats.descstats (Python function) — Prints descriptive statistics for one or multiple variables."><code class="xref py py-obj docutils literal notranslate"><span class="pre">descstats.descstats</span></code></a>(data[, cols, axis])</p></td>
<td><p>Prints descriptive statistics for one or multiple variables.</p></td>
</tr>
</tbody>
</table>
<h3 id="original-stats-models">Original stats.models<a class="headerlink" href="#original-stats-models" title="Link to this heading">¶</a></h3>
<p>None of these are fully working. The formula framework is used by cox and
mixed.</p>
<p><strong>Mixed Effects Model with Repeated Measures using an EM Algorithm</strong></p>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">statsmodels.sandbox.mixed</span></code></p>
<p><strong>Cox Proportional Hazards Model</strong></p>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">statsmodels.sandbox.cox</span></code></p>
<p><strong>Generalized Additive Models</strong></p>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">statsmodels.sandbox.gam</span></code></p>
<p><strong>Formula</strong></p>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">statsmodels.sandbox.formula</span></code></p>


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