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 <p>SQL functions for multinomial logistic regression.
 <a href="#details">More...</a></p>

 <p><a href="multilogistic_8sql__in_source.html">Go to the source code of this file.</a></p>
 <table class="memberdecls">
 <tr><td colspan="2"><h2><a name="func-members"></a>
 Functions</h2></td></tr>
 <tr><td class="memItemLeft" align="right" valign="top">mlogregr_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a8037cc95b1349de7f8910768be3262c8">mlogregr</a> (varchar source, varchar depvar, integer numcategories, varchar indepvar, integer maxnumiterations=20, varchar optimizer=&quot;irls&quot;, float8 precision=0.0001)</td></tr>
 <tr><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute logistic-regression coefficients and diagnostic statistics.  <a href="#a8037cc95b1349de7f8910768be3262c8"></a><br/></td></tr>
 </table>
 <hr/><a name="details" id="details"></a><h2>Detailed Description</h2>
 <div class="textblock"><dl class="date"><dt><b>Date:</b></dt><dd>July 2012</dd></dl>
 <dl class="see"><dt><b>See also:</b></dt><dd>For a brief introduction to multinomial <a class="el" href="logistic_8sql__in.html#a4ded9be5c8b111dbb3109efaad83d69e" title="Evaluate the usual logistic function in an under-/overflow-safe way.">logistic</a> regression, see the module description <a class="el" href="group__grp__mlogreg.html">Multinomial Logistic Regression</a>. </dd></dl>

 <p>Definition in file <a class="el" href="multilogistic_8sql__in_source.html">multilogistic.sql_in</a>.</p>
 </div><hr/><h2>Function Documentation</h2>
 <a class="anchor" id="a8037cc95b1349de7f8910768be3262c8"></a><!-- doxytag: member="multilogistic.sql_in::mlogregr" ref="a8037cc95b1349de7f8910768be3262c8" args="(varchar source, varchar depvar, integer numcategories, varchar indepvar, integer maxnumiterations=20, varchar optimizer=&quot;irls&quot;, float8 precision=0.0001)" -->
 <div class="memitem">
 <div class="memproto">
       <table class="memname">
         <tr>
           <td class="memname">mlogregr_result mlogregr </td>
           <td>(</td>
           <td class="paramtype">varchar&#160;</td>
           <td class="paramname"><em>source</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">varchar&#160;</td>
           <td class="paramname"><em>depvar</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">integer&#160;</td>
           <td class="paramname"><em>numcategories</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">varchar&#160;</td>
           <td class="paramname"><em>indepvar</em>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">integer&#160;</td>
           <td class="paramname"><em>maxnumiterations</em> = <code>20</code>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">varchar&#160;</td>
           <td class="paramname"><em>optimizer</em> = <code>&quot;irls&quot;</code>, </td>
         </tr>
         <tr>
           <td class="paramkey"></td>
           <td></td>
           <td class="paramtype">float8&#160;</td>
           <td class="paramname"><em>precision</em> = <code>0.0001</code>&#160;</td>
         </tr>
         <tr>
           <td></td>
           <td>)</td>
           <td></td><td></td>
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       </table>
 </div>
 <div class="memdoc">
 <p>To include an intercept in the model, set one coordinate in the <code>independentVariables</code> array to 1.</p>
 <dl><dt><b>Parameters:</b></dt><dd>
   <table class="params">
     <tr><td class="paramname">source</td><td>Name of the source relation containing the training data </td></tr>
     <tr><td class="paramname">depvar</td><td>Name of the dependent column (of type INTEGER &lt; numcategories - 1) </td></tr>
     <tr><td class="paramname">numcategories</td><td>Number of categories for the dependant variables ( of type INTEGER) </td></tr>
     <tr><td class="paramname">indepvar</td><td>Name of the independent column (of type DOUBLE PRECISION[]) </td></tr>
     <tr><td class="paramname">maxnumiterations</td><td>The maximum number of iterations </td></tr>
     <tr><td class="paramname">optimizer</td><td>The optimizer to use ( <code>'irls'</code>/<code>'newton'</code> for iteratively reweighted least squares) </td></tr>
     <tr><td class="paramname">precision</td><td>The difference between log-likelihood values in successive iterations that should indicate convergence. Note that a non-positive value here disables the convergence criterion, and execution will only stop after \ maxNumIterations iterations.</td></tr>
   </table>
   </dd>
 </dl>
 <dl class="return"><dt><b>Returns:</b></dt><dd>A composite value:<ul>
 <li><code>coef FLOAT8[]</code> - Array of coefficients, \( \boldsymbol c \)</li>
 <li><code>log_likelihood FLOAT8</code> - Log-likelihood \( l(\boldsymbol c) \)</li>
 <li><code>std_err FLOAT8[]</code> - Array of standard errors, \( \mathit{se}(c_1), \dots, \mathit{se}(c_k) \)</li>
 <li><code>z_stats FLOAT8[]</code> - Array of Wald z-statistics, \( \boldsymbol z \)</li>
 <li><code>p_values FLOAT8[]</code> - Array of Wald p-values, \( \boldsymbol p \)</li>
 <li><code>odds_ratios FLOAT8[]</code>: Array of odds ratios, \( \mathit{odds}(c_1), \dots, \mathit{odds}(c_k) \)</li>
 <li><code>condition_no FLOAT8</code> - The condition number of matrix \( X^T A X \) during the iteration immediately <em>preceding</em> convergence (i.e., \( A \) is computed using the coefficients of the previous iteration)</li>
 <li><code>num_iterations INTEGER</code> - The number of iterations before the algorithm terminated</li>
 </ul>
 </dd></dl>
 <dl class="user"><dt><b>Usage:</b></dt><dd><ul>
 <li>Get vector of coefficients \( \boldsymbol c \) and all diagnostic statistics:<br/>
  <pre>SELECT * FROM mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
     '<em>numCategories</em>', '<em>independentVariables</em>');</pre></li>
 <li>Get vector of coefficients \( \boldsymbol c \):<br/>
  <pre>SELECT (mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
     '<em>numCategories</em>', '<em>independentVariables</em>')).coef;</pre></li>
 <li>Get a subset of the output columns, e.g., only the array of coefficients \( \boldsymbol c \), the log-likelihood of determination \( l(\boldsymbol c) \), and the array of p-values \( \boldsymbol p \): <pre>SELECT coef, log_likelihood, p_values
     FROM mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
    '<em>numCategories</em>', '<em>independentVariables</em>');</pre></li>
 </ul>
 </dd></dl>
 <dl class="note"><dt><b>Note:</b></dt><dd>This function starts an iterative algorithm. It is not an aggregate function. Source and column names have to be passed as strings (due to limitations of the SQL syntax). </dd></dl>

 <p>Definition at line <a class="el" href="multilogistic_8sql__in_source.html#l00362">362</a> of file <a class="el" href="multilogistic_8sql__in_source.html">multilogistic.sql_in</a>.</p>

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	<p>SQL functions for multinomial logistic regression.
	<a href="#details">More...</a></p>

	<p><a href="multilogistic_8sql__in_source.html">Go to the source code of this file.</a></p>
	<table class="memberdecls">
	<tr><td colspan="2"><h2><a name="func-members"></a>
	Functions</h2></td></tr>
	<tr><td class="memItemLeft" align="right" valign="top">mlogregr_result </td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a8037cc95b1349de7f8910768be3262c8">mlogregr</a> (varchar source, varchar depvar, integer numcategories, varchar indepvar, integer maxnumiterations=20, varchar optimizer="irls", float8 precision=0.0001)</td></tr>
	<tr><td class="mdescLeft"> </td><td class="mdescRight">Compute logistic-regression coefficients and diagnostic statistics. <a href="#a8037cc95b1349de7f8910768be3262c8"></a><br/></td></tr>
	</table>
	<hr/><a name="details" id="details"></a><h2>Detailed Description</h2>
	<div class="textblock"><dl class="date"><dt><b>Date:</b></dt><dd>July 2012</dd></dl>
	<dl class="see"><dt><b>See also:</b></dt><dd>For a brief introduction to multinomial <a class="el" href="logistic_8sql__in.html#a4ded9be5c8b111dbb3109efaad83d69e" title="Evaluate the usual logistic function in an under-/overflow-safe way.">logistic</a> regression, see the module description <a class="el" href="group__grp__mlogreg.html">Multinomial Logistic Regression</a>. </dd></dl>

	<p>Definition in file <a class="el" href="multilogistic_8sql__in_source.html">multilogistic.sql_in</a>.</p>
	</div><hr/><h2>Function Documentation</h2>
	<a class="anchor" id="a8037cc95b1349de7f8910768be3262c8"></a><!-- doxytag: member="multilogistic.sql_in::mlogregr" ref="a8037cc95b1349de7f8910768be3262c8" args="(varchar source, varchar depvar, integer numcategories, varchar indepvar, integer maxnumiterations=20, varchar optimizer="irls", float8 precision=0.0001)" -->
	<div class="memitem">
	<div class="memproto">
	<table class="memname">
	<tr>
	<td class="memname">mlogregr_result mlogregr </td>
	<td>(</td>
	<td class="paramtype">varchar </td>
	<td class="paramname"><em>source</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">varchar </td>
	<td class="paramname"><em>depvar</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">integer </td>
	<td class="paramname"><em>numcategories</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">varchar </td>
	<td class="paramname"><em>indepvar</em>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">integer </td>
	<td class="paramname"><em>maxnumiterations</em> = <code>20</code>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">varchar </td>
	<td class="paramname"><em>optimizer</em> = <code>"irls"</code>, </td>
	</tr>
	<tr>
	<td class="paramkey"></td>
	<td></td>
	<td class="paramtype">float8 </td>
	<td class="paramname"><em>precision</em> = <code>0.0001</code> </td>
	</tr>
	<tr>
	<td></td>
	<td>)</td>
	<td></td><td></td>
	</tr>
	</table>
	</div>
	<div class="memdoc">
	<p>To include an intercept in the model, set one coordinate in the <code>independentVariables</code> array to 1.</p>
	<dl><dt><b>Parameters:</b></dt><dd>
	<table class="params">
	<tr><td class="paramname">source</td><td>Name of the source relation containing the training data </td></tr>
	<tr><td class="paramname">depvar</td><td>Name of the dependent column (of type INTEGER < numcategories - 1) </td></tr>
	<tr><td class="paramname">numcategories</td><td>Number of categories for the dependant variables ( of type INTEGER) </td></tr>
	<tr><td class="paramname">indepvar</td><td>Name of the independent column (of type DOUBLE PRECISION[]) </td></tr>
	<tr><td class="paramname">maxnumiterations</td><td>The maximum number of iterations </td></tr>
	<tr><td class="paramname">optimizer</td><td>The optimizer to use ( <code>'irls'</code>/<code>'newton'</code> for iteratively reweighted least squares) </td></tr>
	<tr><td class="paramname">precision</td><td>The difference between log-likelihood values in successive iterations that should indicate convergence. Note that a non-positive value here disables the convergence criterion, and execution will only stop after \ maxNumIterations iterations.</td></tr>
	</table>
	</dd>
	</dl>
	<dl class="return"><dt><b>Returns:</b></dt><dd>A composite value:<ul>
	<li><code>coef FLOAT8[]</code> - Array of coefficients, \( \boldsymbol c \)</li>
	<li><code>log_likelihood FLOAT8</code> - Log-likelihood \( l(\boldsymbol c) \)</li>
	<li><code>std_err FLOAT8[]</code> - Array of standard errors, \( \mathit{se}(c_1), \dots, \mathit{se}(c_k) \)</li>
	<li><code>z_stats FLOAT8[]</code> - Array of Wald z-statistics, \( \boldsymbol z \)</li>
	<li><code>p_values FLOAT8[]</code> - Array of Wald p-values, \( \boldsymbol p \)</li>
	<li><code>odds_ratios FLOAT8[]</code>: Array of odds ratios, \( \mathit{odds}(c_1), \dots, \mathit{odds}(c_k) \)</li>
	<li><code>condition_no FLOAT8</code> - The condition number of matrix \( X^T A X \) during the iteration immediately <em>preceding</em> convergence (i.e., \( A \) is computed using the coefficients of the previous iteration)</li>
	<li><code>num_iterations INTEGER</code> - The number of iterations before the algorithm terminated</li>
	</ul>
	</dd></dl>
	<dl class="user"><dt><b>Usage:</b></dt><dd><ul>
	<li>Get vector of coefficients \( \boldsymbol c \) and all diagnostic statistics:<br/>
	<pre>SELECT * FROM mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
	'<em>numCategories</em>', '<em>independentVariables</em>');</pre></li>
	<li>Get vector of coefficients \( \boldsymbol c \):<br/>
	<pre>SELECT (mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
	'<em>numCategories</em>', '<em>independentVariables</em>')).coef;</pre></li>
	<li>Get a subset of the output columns, e.g., only the array of coefficients \( \boldsymbol c \), the log-likelihood of determination \( l(\boldsymbol c) \), and the array of p-values \( \boldsymbol p \): <pre>SELECT coef, log_likelihood, p_values
	FROM mlogregr('<em>sourceName</em>', '<em>dependentVariable</em>',
	'<em>numCategories</em>', '<em>independentVariables</em>');</pre></li>
	</ul>
	</dd></dl>
	<dl class="note"><dt><b>Note:</b></dt><dd>This function starts an iterative algorithm. It is not an aggregate function. Source and column names have to be passed as strings (due to limitations of the SQL syntax). </dd></dl>

	<p>Definition at line <a class="el" href="multilogistic_8sql__in_source.html#l00362">362</a> of file <a class="el" href="multilogistic_8sql__in_source.html">multilogistic.sql_in</a>.</p>

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