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<div class="title">multilogistic.sql_in File Reference</div> </div>
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<p>SQL functions for multinomial logistic regression.
<a href="#details">More...</a></p>
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Functions</h2></td></tr>
<tr class="memitem:aef43e4a6363495901045daf339d5c6d7"><td class="memItemLeft" align="right" valign="top">float8 []&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#aef43e4a6363495901045daf339d5c6d7">__mlogregr_irls_step_transition</a> (float8[] state, integer y, integer num_categories, integer ref_category, float8[] x, float8[] prev_state)</td></tr>
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<tr class="memitem:a7cae4ea602fc9c159fa2cc8c1a7653a6"><td class="memItemLeft" align="right" valign="top">float8 []&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a7cae4ea602fc9c159fa2cc8c1a7653a6">__mlogregr_irls_step_merge_states</a> (float8[] state1, float8[] state2)</td></tr>
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<tr class="memitem:a690a74b753dceec66c4e0ad22f50c51e"><td class="memItemLeft" align="right" valign="top">float8 []&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a690a74b753dceec66c4e0ad22f50c51e">__mlogregr_irls_step_final</a> (float8[] state)</td></tr>
<tr class="separator:a690a74b753dceec66c4e0ad22f50c51e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a695395e04b6d95afafc7c8ac9e01b7b2"><td class="memItemLeft" align="right" valign="top">aggregate float8 []&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a695395e04b6d95afafc7c8ac9e01b7b2">__mlogregr_irls_step</a> (integer y, integer numcategories, integer ref_category, float8[] x, float8[] previous_state)</td></tr>
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<tr class="separator:aad300d3120db2ecaabf4809cf6be81e7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae890704e55f57bd9105b63021e0f86ae"><td class="memItemLeft" align="right" valign="top">mlogregr_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#ae890704e55f57bd9105b63021e0f86ae">__internal_mlogregr_irls_result</a> (float8[] state)</td></tr>
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<tr class="memitem:a48e0f07fcd855a9abcdf6ff070474b73"><td class="memItemLeft" align="right" valign="top">mlogregr_summary_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a48e0f07fcd855a9abcdf6ff070474b73">__internal_mlogregr_summary_results</a> (float8[] state)</td></tr>
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<tr class="memitem:aedc13474e6abbc88451d120ad97e44d4"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#aedc13474e6abbc88451d120ad97e44d4">mlogregr_train</a> (varchar source_table, varchar output_table, varchar dependent_varname, varchar independent_varname, integer ref_category, varchar optimizer_params)</td></tr>
<tr class="memdesc:aedc13474e6abbc88451d120ad97e44d4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute multinomial logistic regression coefficients. <a href="#aedc13474e6abbc88451d120ad97e44d4">More...</a><br /></td></tr>
<tr class="separator:aedc13474e6abbc88451d120ad97e44d4"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="memitem:a0b4643da1ecfcfaf3a1563c820b3347d"><td class="memItemLeft" align="right" valign="top">varchar&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a0b4643da1ecfcfaf3a1563c820b3347d">mlogregr_train</a> (varchar message)</td></tr>
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<tr class="memitem:ac7da2fbd9877d94b2f1f013cc000566b"><td class="memItemLeft" align="right" valign="top">integer&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#ac7da2fbd9877d94b2f1f013cc000566b">__compute_mlogregr</a> (varchar source_table, varchar dependent_varname, varchar independent_varname, integer num_categories, integer max_iter, varchar optimizer, float8 precision, integer ref_category)</td></tr>
<tr class="separator:ac7da2fbd9877d94b2f1f013cc000566b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7be20ccb465d47808e18149140fc666f"><td class="memItemLeft" align="right" valign="top">mlogregr_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a7be20ccb465d47808e18149140fc666f">mlogregr</a> (varchar source, varchar depvar, varchar indepvar, integer max_num_iterations=20, varchar optimizer=&quot;irls&quot;, float8 precision=0.0001, integer ref_category)</td></tr>
<tr class="memdesc:a7be20ccb465d47808e18149140fc666f"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute logistic-regression coefficients and diagnostic statistics. <a href="#a7be20ccb465d47808e18149140fc666f">More...</a><br /></td></tr>
<tr class="separator:a7be20ccb465d47808e18149140fc666f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a116c95de21b112dedf99035a9b243fd7"><td class="memItemLeft" align="right" valign="top">mlogregr_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a116c95de21b112dedf99035a9b243fd7">mlogregr</a> (varchar source, varchar depvar, varchar indepvar)</td></tr>
<tr class="separator:a116c95de21b112dedf99035a9b243fd7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1ab68f7396f53ae3e32362240d077cbf"><td class="memItemLeft" align="right" valign="top">mlogregr_result&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a1ab68f7396f53ae3e32362240d077cbf">mlogregr</a> (varchar source, varchar depvar, varchar indepvar, integer max_num_iterations)</td></tr>
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<tr class="memitem:a55ffab7c3e95b301b3ad5733f185a1ec"><td class="memItemLeft" align="right" valign="top">set&lt; __mlogregr_cat_coef &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a55ffab7c3e95b301b3ad5733f185a1ec">__mlogregr_format</a> (float8[] coef, integer num_feature, integer num_category, integer ref_category)</td></tr>
<tr class="separator:a55ffab7c3e95b301b3ad5733f185a1ec"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<tr class="memitem:a00f0c988e1b2b2fee9e4021450840061"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a00f0c988e1b2b2fee9e4021450840061">mlogregr_predict</a> (text model, text source, text id_col_name, text output, text pred_type)</td></tr>
<tr class="separator:a00f0c988e1b2b2fee9e4021450840061"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a4c61f2fd5a67a7babb700fdfbd4d146f"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a4c61f2fd5a67a7babb700fdfbd4d146f">mlogregr_predict</a> (text model, text source, text id_col_name, text output)</td></tr>
<tr class="separator:a4c61f2fd5a67a7babb700fdfbd4d146f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a0fdfed54d63cefe260a0b74b9c7bbad5"><td class="memItemLeft" align="right" valign="top">text&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="multilogistic_8sql__in.html#a0fdfed54d63cefe260a0b74b9c7bbad5">mlogregr_predict</a> (text message)</td></tr>
<tr class="separator:a0fdfed54d63cefe260a0b74b9c7bbad5"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><dl class="section date"><dt>Date</dt><dd>July 2012</dd></dl>
<dl class="section see"><dt>See also</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>
</div><h2 class="groupheader">Function Documentation</h2>
<a id="ac7da2fbd9877d94b2f1f013cc000566b"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ac7da2fbd9877d94b2f1f013cc000566b">&#9670;&nbsp;</a></span>__compute_mlogregr()</h2>
<div class="memitem">
<div class="memproto">
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<td class="memname">integer __compute_mlogregr </td>
<td>(</td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>source_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>dependent_varname</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>independent_varname</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>num_categories</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>max_iter</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>optimizer</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8&#160;</td>
<td class="paramname"><em>precision</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
<a id="ae890704e55f57bd9105b63021e0f86ae"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ae890704e55f57bd9105b63021e0f86ae">&#9670;&nbsp;</a></span>__internal_mlogregr_irls_result()</h2>
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<td class="memname">mlogregr_result __internal_mlogregr_irls_result </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>state</em></td><td>)</td>
<td></td>
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</div><div class="memdoc">
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</div>
<a id="aad300d3120db2ecaabf4809cf6be81e7"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aad300d3120db2ecaabf4809cf6be81e7">&#9670;&nbsp;</a></span>__internal_mlogregr_irls_step_distance()</h2>
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<td class="memname">float8 __internal_mlogregr_irls_step_distance </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>state1</em>, </td>
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<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>state2</em>&#160;</td>
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<td></td>
<td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a48e0f07fcd855a9abcdf6ff070474b73">&#9670;&nbsp;</a></span>__internal_mlogregr_summary_results()</h2>
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<td class="memname">mlogregr_summary_result __internal_mlogregr_summary_results </td>
<td>(</td>
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<td></td>
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<h2 class="memtitle"><span class="permalink"><a href="#a55ffab7c3e95b301b3ad5733f185a1ec">&#9670;&nbsp;</a></span>__mlogregr_format()</h2>
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<td class="memname">set&lt;__mlogregr_cat_coef&gt; __mlogregr_format </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>coef</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>num_feature</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>num_category</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
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<h2 class="memtitle"><span class="permalink"><a href="#a695395e04b6d95afafc7c8ac9e01b7b2">&#9670;&nbsp;</a></span>__mlogregr_irls_step()</h2>
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<td class="memname">aggregate float8 [] __mlogregr_irls_step </td>
<td>(</td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>y</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">integer&#160;</td>
<td class="paramname"><em>ref_category</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>x</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>previous_state</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
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</table>
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<h2 class="memtitle"><span class="permalink"><a href="#a690a74b753dceec66c4e0ad22f50c51e">&#9670;&nbsp;</a></span>__mlogregr_irls_step_final()</h2>
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<td class="memname">float8 [] __mlogregr_irls_step_final </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>state</em></td><td>)</td>
<td></td>
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<h2 class="memtitle"><span class="permalink"><a href="#a7cae4ea602fc9c159fa2cc8c1a7653a6">&#9670;&nbsp;</a></span>__mlogregr_irls_step_merge_states()</h2>
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<td class="memname">float8 [] __mlogregr_irls_step_merge_states </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>state1</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>state2</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
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<h2 class="memtitle"><span class="permalink"><a href="#aef43e4a6363495901045daf339d5c6d7">&#9670;&nbsp;</a></span>__mlogregr_irls_step_transition()</h2>
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<td class="memname">float8 [] __mlogregr_irls_step_transition </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>state</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>y</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>num_categories</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>x</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>prev_state</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
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<h2 class="memtitle"><span class="permalink"><a href="#a7418e1ce65432793d0889f7e53f668cd">&#9670;&nbsp;</a></span>__mlogregr_predict_prob()</h2>
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<td class="memname">float8 [] __mlogregr_predict_prob </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>coef</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>col_ind_var</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
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</table>
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<h2 class="memtitle"><span class="permalink"><a href="#ad5e9a79ac38439db8ecd25148ca6f244">&#9670;&nbsp;</a></span>__mlogregr_predict_response()</h2>
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<td class="memname">integer __mlogregr_predict_response </td>
<td>(</td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>coef</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">float8 []&#160;</td>
<td class="paramname"><em>col_ind_var</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a7be20ccb465d47808e18149140fc666f">&#9670;&nbsp;</a></span>mlogregr() <span class="overload">[1/4]</span></h2>
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<td class="memname">mlogregr_result mlogregr </td>
<td>(</td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>source</em>, </td>
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<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">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>max_num_iterations</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>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>&#160;</td>
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<td></td>
<td>)</td>
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<p>To include an intercept in the model, set one coordinate in the <code>independentVariables</code> array to 1.</p>
<dl class="params"><dt>Parameters</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) </td></tr>
<tr><td class="paramname">indepvar</td><td>Name of the independent column (of type DOUBLE PRECISION[]) </td></tr>
<tr><td class="paramname">max_num_iterations</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 \ max_num_iterations iterations. </td></tr>
<tr><td class="paramname">ref_category</td><td>The reference category specified by the user</td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>A composite value:<ul>
<li><code>ref_category INTEGER</code> - Reference category</li>
<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="section user"><dt>Usage</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="section note"><dt>Note</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>
</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a116c95de21b112dedf99035a9b243fd7">&#9670;&nbsp;</a></span>mlogregr() <span class="overload">[2/4]</span></h2>
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<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">varchar&#160;</td>
<td class="paramname"><em>indepvar</em>&#160;</td>
</tr>
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<td></td>
<td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a1ab68f7396f53ae3e32362240d077cbf">&#9670;&nbsp;</a></span>mlogregr() <span class="overload">[3/4]</span></h2>
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<td class="memname">mlogregr_result mlogregr </td>
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<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">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>max_num_iterations</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#ae1b5954340a0c98a6ed06eb5f3bb43b8">&#9670;&nbsp;</a></span>mlogregr() <span class="overload">[4/4]</span></h2>
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<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">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>max_num_iterations</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>optimizer</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a00f0c988e1b2b2fee9e4021450840061">&#9670;&nbsp;</a></span>mlogregr_predict() <span class="overload">[1/3]</span></h2>
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<td class="memname">void mlogregr_predict </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>source</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>id_col_name</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>output</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>pred_type</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a4c61f2fd5a67a7babb700fdfbd4d146f">&#9670;&nbsp;</a></span>mlogregr_predict() <span class="overload">[2/3]</span></h2>
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<td class="memname">void mlogregr_predict </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>model</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>source</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>id_col_name</em>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>output</em>&#160;</td>
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<td></td>
<td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a0fdfed54d63cefe260a0b74b9c7bbad5">&#9670;&nbsp;</a></span>mlogregr_predict() <span class="overload">[3/3]</span></h2>
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<td class="memname">text mlogregr_predict </td>
<td>(</td>
<td class="paramtype">text&#160;</td>
<td class="paramname"><em>message</em></td><td>)</td>
<td></td>
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<h2 class="memtitle"><span class="permalink"><a href="#aedc13474e6abbc88451d120ad97e44d4">&#9670;&nbsp;</a></span>mlogregr_train() <span class="overload">[1/5]</span></h2>
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<td class="memname">void mlogregr_train </td>
<td>(</td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>source_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>output_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>dependent_varname</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>independent_varname</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>optimizer_params</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
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</table>
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<p>To include an intercept in the model, set one coordinate in the <code>independentVariables</code> array to 1.</p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">source_table</td><td>Name of the source relation containing the training data </td></tr>
<tr><td class="paramname">output_table</td><td>Name of the output relation to contain the resulting model </td></tr>
<tr><td class="paramname">dependent_varname</td><td>Name of the dependent column (of type INTEGER) </td></tr>
<tr><td class="paramname">independent_varname</td><td>Name of the independent column (or an array expression) </td></tr>
<tr><td class="paramname">ref_category</td><td>The reference category specified by the user </td></tr>
<tr><td class="paramname">optimizer_params</td><td>Comma-separated list of parameters for the optimizer function</td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>An output table (named 'output_table' above) containing following columns:<ul>
<li><code>ref_category INTEGER</code> - Reference category</li>
<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) An output table (named 'output_table'_summary) containing following columns:</li>
<li><code>regression_type VARCHAR</code> - The regression type run (in this case it will be 'mlogit')</li>
<li><code>source_table VARCHAR</code> - Source table containing the training data</li>
<li><code>output_table VARCHAR</code> - Output table containing the trained model</li>
<li><code>dependent_varname VARCHAR</code> - Name of the dependent column used for training</li>
<li><code>independent_varname VARCHAR</code> - Name of the independent column used for training (or the ARRAY expression used for training)</li>
<li><code>ref_category INTEGER</code> - The reference category specified by the user</li>
<li><code>num_iterations INTEGER</code> - The number of iterations before the algorithm terminated</li>
<li><code>num_rows_processed INTEGER</code> - The number of rows from training data used for training</li>
<li><code>num_missing_rows_skipped INTEGER</code> - The number of rows skipped during training</li>
</ul>
</dd></dl>
<dl class="section user"><dt>Usage</dt><dd><ul>
<li>Get vector of coefficients \( \boldsymbol c \) and all diagnostic statistics:<br />
<pre>SELECT mlogregr_train('<em>sourceName</em>', '<em>outputName</em>',
'<em>dependentVariable</em>', '<em>independentVariables</em>');
SELECT * from <em>outputName</em>;
</pre></li>
<li>Get vector of coefficients \( \boldsymbol c \):<br />
<pre>SELECT coef from <em>outputName</em>;</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 <em>outputName</em>;</pre> </li>
</ul>
</dd></dl>
</div>
</div>
<a id="afea8bd51ec241fa7a749a7c74ae0f580"></a>
<h2 class="memtitle"><span class="permalink"><a href="#afea8bd51ec241fa7a749a7c74ae0f580">&#9670;&nbsp;</a></span>mlogregr_train() <span class="overload">[2/5]</span></h2>
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<td class="memname">void mlogregr_train </td>
<td>(</td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>source_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>output_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>dependent_varname</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>independent_varname</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">integer&#160;</td>
<td class="paramname"><em>ref_category</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
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<a id="aa596f23c7bcfcd47b051d78de0b99c36"></a>
<h2 class="memtitle"><span class="permalink"><a href="#aa596f23c7bcfcd47b051d78de0b99c36">&#9670;&nbsp;</a></span>mlogregr_train() <span class="overload">[3/5]</span></h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">void mlogregr_train </td>
<td>(</td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>source_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>output_table</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>dependent_varname</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>independent_varname</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
<a id="a0b4643da1ecfcfaf3a1563c820b3347d"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a0b4643da1ecfcfaf3a1563c820b3347d">&#9670;&nbsp;</a></span>mlogregr_train() <span class="overload">[4/5]</span></h2>
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<td class="memname">varchar mlogregr_train </td>
<td>(</td>
<td class="paramtype">varchar&#160;</td>
<td class="paramname"><em>message</em></td><td>)</td>
<td></td>
</tr>
</table>
</div><div class="memdoc">
</div>
</div>
<a id="ab8b5a7eb69a945435cba5a068576f2e4"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ab8b5a7eb69a945435cba5a068576f2e4">&#9670;&nbsp;</a></span>mlogregr_train() <span class="overload">[5/5]</span></h2>
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<tr>
<td class="memname">varchar mlogregr_train </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td></td>
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