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| <div class="title">Robust Variance<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a> » <a class="el" href="group__grp__regml.html">Regression Models</a></div></div> </div> |
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| <div class="contents"> |
| <div class="toc"><b>Contents</b> <ul> |
| <li class="level1"> |
| <a href="#train_linregr">Robust Linear Regression Training Function</a> </li> |
| <li class="level1"> |
| <a href="#train_logregr">Robust Logistic Regression Training Function</a> </li> |
| <li class="level1"> |
| <a href="#train_mlogregr">Robust Multinomial Logistic Regression Training Function</a> </li> |
| <li class="level1"> |
| <a href="#robust_variance_coxph">Robust Variance Function For Cox Proportional Hazards</a> </li> |
| <li class="level1"> |
| <a href="#examples">Examples</a> </li> |
| <li class="level1"> |
| <a href="#background">Technical Background</a> </li> |
| <li class="level1"> |
| <a href="#literature">Literature</a> </li> |
| <li class="level1"> |
| <a href="#related">Related Topics</a> </li> |
| </ul> |
| </div><p>The functions in this module calculate robust variance (Huber-White estimates) for linear regression, logistic regression, multinomial logistic regression, and Cox proportional hazards. They are useful in calculating variances in a dataset with potentially noisy outliers. The Huber-White implemented here is identical to the "HC0" sandwich operator in the R module "sandwich".</p> |
| <p>The interfaces for robust linear, logistic, and multinomial logistic regression are similar. Each regression type has its own training function. The regression results are saved in an output table with small differences, depending on the regression type.</p> |
| <dl class="section warning"><dt>Warning</dt><dd>Please note that the interface for Cox proportional hazards, unlike the interface of other regression methods, accepts an output model table produced by <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef">coxph_train()</a> function.</dd></dl> |
| <p><a class="anchor" id="train_linregr"></a></p><dl class="section user"><dt>Robust Linear Regression Training Function</dt><dd></dd></dl> |
| <p>The <a class="el" href="robust_8sql__in.html#a390473d2fd45e268f0fc13ca971b49b4">robust_variance_linregr()</a> function has the following syntax: </p><pre class="syntax"> |
| robust_variance_linregr( source_table, |
| out_table, |
| dependent_varname, |
| independent_varname, |
| grouping_cols |
| ) |
| </pre> <dl class="arglist"> |
| <dt>source_table </dt> |
| <dd>VARCHAR. The name of the table containing the training data. </dd> |
| <dt>out_table </dt> |
| <dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table contains the following columns. </p><table class="output"> |
| <tr> |
| <th>coef </th><td>DOUBLE PRECISION[]. Vector of the coefficients of the regression. </td></tr> |
| <tr> |
| <th>std_err </th><td>DOUBLE PRECISION[]. Vector of the standard error of the coefficients. </td></tr> |
| <tr> |
| <th>t_stats </th><td>DOUBLE PRECISION[]. Vector of the t-stats of the coefficients. </td></tr> |
| <tr> |
| <th>p_values </th><td>DOUBLE PRECISION[]. Vector of the p-values of the coefficients. </td></tr> |
| </table> |
| <p class="enddd">A summary table named <out_table>_summary is also created, which is the same as the summary table created by linregr_train function. Please refer to the documentation for linear regression for details. </p> |
| </dd> |
| <dt>dependent_varname </dt> |
| <dd>VARCHAR. The name of the column containing the dependent variable. </dd> |
| <dt>independent_varname </dt> |
| <dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. </dd> |
| <dt>grouping_cols (optional) </dt> |
| <dd>VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. Default value: NULL. </dd> |
| </dl> |
| <p><a class="anchor" id="train_logregr"></a></p><dl class="section user"><dt>Robust Logistic Regression Training Function</dt><dd></dd></dl> |
| <p>The <a class="el" href="robust_8sql__in.html#abc20ec2c5e74f268e7727c33a4bb9054">robust_variance_logregr()</a> function has the following syntax: </p><pre class="syntax"> |
| robust_variance_logregr( source_table, |
| out_table, |
| dependent_varname, |
| independent_varname, |
| grouping_cols, |
| max_iter, |
| optimizer, |
| tolerance, |
| verbose_mode |
| ) |
| </pre> <dl class="arglist"> |
| <dt>source_table </dt> |
| <dd>VARCHAR. The name of the table containing the training data. </dd> |
| <dt>out_table </dt> |
| <dd><p class="startdd">VARCHAR. Name of the generated table containing the output model. The output table has the following columns: </p><table class="output"> |
| <tr> |
| <th>coef </th><td>Vector of the coefficients of the regression. </td></tr> |
| <tr> |
| <th>std_err </th><td>Vector of the standard error of the coefficients. </td></tr> |
| <tr> |
| <th>z_stats </th><td>Vector of the z-stats of the coefficients. </td></tr> |
| <tr> |
| <th>p_values </th><td>Vector of the p-values of the coefficients. </td></tr> |
| </table> |
| <p class="enddd">A summary table named <out_table>_summary is also created, which is the same as the summary table created by logregr_train function. Please refer to the documentation for logistic regression for details. </p> |
| </dd> |
| <dt>dependent_varname </dt> |
| <dd>VARCHAR. The name of the column containing the independent variable. </dd> |
| <dt>independent_varname </dt> |
| <dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. </dd> |
| <dt>grouping_cols (optional) </dt> |
| <dd>VARCHAR, default: NULL. An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. </dd> |
| <dt>max_iter (optional) </dt> |
| <dd>INTEGER, default: 20. The maximum number of iterations that are allowed. </dd> |
| <dt>optimizer </dt> |
| <dd>VARCHAR, default: 'fista'. Name of optimizer, either 'fista' or 'igd'. </dd> |
| <dt>tolerance (optional) </dt> |
| <dd>DOUBLE PRECISION, default: 1e-6. The criteria to end iterations. Both the 'fista' and 'igd' optimizers compute the average difference between the coefficients of two consecutive iterations, and when the difference is smaller than tolerance or the iteration number is larger than max_iter, the computation stops. </dd> |
| <dt>verbose_mode (optional) </dt> |
| <dd>BOOLEAN, default: FALSE. Whether the regression fit should print any warning messages. </dd> |
| </dl> |
| <p><a class="anchor" id="train_mlogregr"></a></p><dl class="section user"><dt>Robust Multinomial Logistic Regression Function</dt><dd></dd></dl> |
| <p>The <a class="el" href="robust_8sql__in.html#a1f27c072a4ef885a55825f75d12b3bd8">robust_variance_mlogregr()</a> function has the following syntax: </p><pre class="syntax"> |
| robust_variance_mlogregr( source_table, |
| out_table, |
| dependent_varname, |
| independent_varname, |
| ref_category, |
| grouping_cols, |
| optimizer_params, |
| verbose_mode |
| ) |
| </pre> <dl class="arglist"> |
| <dt>source_table </dt> |
| <dd>VARCHAR. The name of the table containing training data, properly qualified. </dd> |
| <dt>out_table </dt> |
| <dd><p class="startdd">VARCHAR. The name of the table where the regression model will be stored. The output table has the following columns: </p><table class="output"> |
| <tr> |
| <th>category </th><td>The category. </td></tr> |
| <tr> |
| <th>ref_category </th><td>The refererence category used for modeling. </td></tr> |
| <tr> |
| <th>coef </th><td>Vector of the coefficients of the regression. </td></tr> |
| <tr> |
| <th>std_err </th><td>Vector of the standard error of the coefficients. </td></tr> |
| <tr> |
| <th>z_stats </th><td>Vector of the z-stats of the coefficients. </td></tr> |
| <tr> |
| <th>p_values </th><td>Vector of the p-values of the coefficients. </td></tr> |
| </table> |
| <p class="enddd">A summary table named <out_table>_summary is also created, which is the same as the summary table created by mlogregr_train function. Please refer to the documentation for multinomial logistic regression for details. </p> |
| </dd> |
| <dt>dependent_varname </dt> |
| <dd>VARCHAR. The name of the column containing the dependent variable. </dd> |
| <dt>independent_varname </dt> |
| <dd>VARCHAR. Expression list to evaluate for the independent variables. An intercept variable is not assumed. It is common to provide an explicit intercept term by including a single constant 1 term in the independent variable list. The <em>independent_varname</em> can be the name of a column that contains an array of numeric values. It can also be a string with the format 'ARRAY[1, x1, x2, x3]', where <em>x1</em>, <em>x2</em> and <em>x3</em> are each column names. </dd> |
| <dt>ref_category (optional) </dt> |
| <dd>INTEGER, default: 0. The reference category. </dd> |
| <dt>grouping_cols (optional) </dt> |
| <dd>VARCHAR, default: NULL. <em>Not currently implemented. Any non-NULL value is ignored.</em> An expression list used to group the input dataset into discrete groups, running one regression per group. Similar to the SQL "GROUP BY" clause. When this value is NULL, no grouping is used and a single result model is generated. </dd> |
| <dt>optimizer_params (optional) </dt> |
| <dd>TEXT, default: NULL, which uses the default values of optimizer parameters: max_iter=20, optimizer='newton', tolerance=1e-4. It should be a string that contains pairs of 'key=value' separated by commas. </dd> |
| <dt>verbose_mode (optional) </dt> |
| <dd>BOOLEAN, default FALSE. <em>Not currently implemented.</em> TRUE if the regression fit should print warning messages. </dd> |
| </dl> |
| <p><a class="anchor" id="robust_variance_coxph"></a></p><dl class="section user"><dt>Robust Variance Function For Cox Proportional Hazards</dt><dd></dd></dl> |
| <p>The <a class="el" href="clustered__variance__coxph_8sql__in.html#abaeae5d6cd30db4b06a49d24d714812e">robust_variance_coxph()</a> function has the following syntax: </p><pre class="syntax"> |
| robust_variance_coxph(model_table, output_table) |
| </pre><p><b>Arguments</b> </p><dl class="arglist"> |
| <dt>model_table </dt> |
| <dd>TEXT. The name of the model table, which is exactaly the same as the 'output_table' parameter of <a class="el" href="cox__prop__hazards_8sql__in.html#a737450bbfe0f10204b0074a9d45b0cef" title="Compute cox-regression coefficients and diagnostic statistics. ">coxph_train()</a> function. </dd> |
| <dt>output_table </dt> |
| <dd>TEXT. The name of the table where the output is saved. It has the following columns: <table class="output"> |
| <tr> |
| <th>coef </th><td>FLOAT8[]. Vector of the coefficients. </td></tr> |
| <tr> |
| <th>loglikelihood </th><td>FLOAT8. Log-likelihood value of the MLE estimate. </td></tr> |
| <tr> |
| <th>std_err </th><td>FLOAT8[]. Vector of the standard error of the coefficients. </td></tr> |
| <tr> |
| <th>robust_se </th><td>FLOAT8[]. Vector of the robust standard errors of the coefficients. </td></tr> |
| <tr> |
| <th>robust_z </th><td>FLOAT8[]. Vector of the robust z-stats of the coefficients. </td></tr> |
| <tr> |
| <th>robust_p </th><td>FLOAT8[]. Vector of the robust p-values of the coefficients. </td></tr> |
| <tr> |
| <th>hessian </th><td>FLOAT8[]. The Hessian matrix. </td></tr> |
| </table> |
| </dd> |
| </dl> |
| <p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl> |
| <p><b> Logistic Regression Example </b></p><ol type="1"> |
| <li>View online help for the logistic regression training function. <pre class="example"> |
| SELECT madlib.robust_variance_logregr(); |
| </pre></li> |
| <li>Create the training data table. <pre class="example"> |
| DROP TABLE IF EXISTS patients; |
| CREATE TABLE patients (id INTEGER NOT NULL, second_attack INTEGER, |
| treatment INTEGER, trait_anxiety INTEGER); |
| COPY patients FROM STDIN WITH DELIMITER '|'; |
| 1 | 1 | 1 | 70 |
| 3 | 1 | 1 | 50 |
| 5 | 1 | 0 | 40 |
| 7 | 1 | 0 | 75 |
| 9 | 1 | 0 | 70 |
| 11 | 0 | 1 | 65 |
| 13 | 0 | 1 | 45 |
| 15 | 0 | 1 | 40 |
| 17 | 0 | 0 | 55 |
| 19 | 0 | 0 | 50 |
| 2 | 1 | 1 | 80 |
| 4 | 1 | 0 | 60 |
| 6 | 1 | 0 | 65 |
| 8 | 1 | 0 | 80 |
| 10 | 1 | 0 | 60 |
| 12 | 0 | 1 | 50 |
| 14 | 0 | 1 | 35 |
| 16 | 0 | 1 | 50 |
| 18 | 0 | 0 | 45 |
| 20 | 0 | 0 | 60 |
| \. |
| </pre></li> |
| <li>Run the logistic regression training function and compute the robust logistic variance of the regression: <pre class="example"> |
| DROP TABLE IF EXISTS patients_logregr, patients_logregr_summary; |
| SELECT madlib.robust_variance_logregr( 'patients', |
| 'patients_logregr', |
| 'second_attack', |
| 'ARRAY[1, treatment, trait_anxiety]' |
| ); |
| </pre></li> |
| <li>View the regression results. <pre class="example"> |
| \x on |
| SELECT * FROM patients_logregr; |
| </pre> Result: <pre class="result"> |
|  -[ RECORD 1 ]------------------------------------------------------- |
| coef | {-6.36346994178179,-1.02410605239327,0.119044916668605} |
| std_err | {3.45872062333648,1.1716192578234,0.0534328864185018} |
| z_stats | {-1.83983346294192,-0.874094587943036,2.22793348156809} |
| p_values | {0.0657926909738889,0.382066744585541,0.0258849510757339} |
| </pre> Alternatively, unnest the arrays in the results for easier reading of output. <pre class="example"> |
| \x off |
| SELECT unnest(array['intercept', 'treatment', 'trait_anxiety' ]) as attribute, |
| unnest(coef) as coefficient, |
| unnest(std_err) as standard_error, |
| unnest(z_stats) as z_stat, |
| unnest(p_values) as pvalue |
| FROM patients_logregr; |
| </pre></li> |
| </ol> |
| <p><b> Cox Proportional Hazards Example </b></p><ol type="1"> |
| <li>View online help for the robust Cox Proportional hazards training method. <pre class="example"> |
| SELECT madlib.robust_variance_coxph(); |
| </pre></li> |
| <li>Create an input data set. <pre class="example"> |
| DROP TABLE IF EXISTS sample_data; |
| CREATE TABLE sample_data ( |
| id INTEGER NOT NULL, |
| grp DOUBLE PRECISION, |
| wbc DOUBLE PRECISION, |
| timedeath INTEGER, |
| status BOOLEAN |
| ); |
| COPY sample_data FROM STDIN DELIMITER '|'; |
| 0 | 0 | 1.45 | 35 | t |
| 1 | 0 | 1.47 | 34 | t |
| 3 | 0 | 2.2 | 32 | t |
| 4 | 0 | 1.78 | 25 | t |
| 5 | 0 | 2.57 | 23 | t |
| 6 | 0 | 2.32 | 22 | t |
| 7 | 0 | 2.01 | 20 | t |
| 8 | 0 | 2.05 | 19 | t |
| 9 | 0 | 2.16 | 17 | t |
| 10 | 0 | 3.6 | 16 | t |
| 11 | 1 | 2.3 | 15 | t |
| 12 | 0 | 2.88 | 13 | t |
| 13 | 1 | 1.5 | 12 | t |
| 14 | 0 | 2.6 | 11 | t |
| 15 | 0 | 2.7 | 10 | t |
| 16 | 0 | 2.8 | 9 | t |
| 17 | 1 | 2.32 | 8 | t |
| 18 | 0 | 4.43 | 7 | t |
| 19 | 0 | 2.31 | 6 | t |
| 20 | 1 | 3.49 | 5 | t |
| 21 | 1 | 2.42 | 4 | t |
| 22 | 1 | 4.01 | 3 | t |
| 23 | 1 | 4.91 | 2 | t |
| 24 | 1 | 5 | 1 | t |
| \. |
| </pre></li> |
| <li>Run the Cox regression function. <pre class="example"> |
| DROP TABLE IF EXISTS sample_cox, sample_cox_summary; |
| SELECT madlib.coxph_train( 'sample_data', |
| 'sample_cox', |
| 'timedeath', |
| 'ARRAY[grp,wbc]', |
| 'status' |
| ); |
| </pre></li> |
| <li>Run the Robust Cox regression function. <pre class="example"> |
| SELECT madlib.robust_variance_coxph( 'sample_cox', |
| 'sample_robust_cox' |
| ); |
| </pre></li> |
| <li>View the results of the robust Cox regression. <pre class="example"> |
| \x on |
| SELECT * FROM sample_robust_cox; |
| </pre> Results: <pre class="result"> |
| -[ RECORD 1 ]-+---------------------------------------------------------------------------- |
| coef | {2.54407073265105,1.67172094780081} |
| loglikelihood | -37.8532498733452 |
| std_err | {0.677180599295459,0.387195514577754} |
| robust_se | {0.621095581073685,0.274773521439328} |
| robust_z | {4.09610180811965,6.08399579058399} |
| robust_p | {4.2016521208424e-05,1.17223683104729e-09} |
| hessian | {{2.78043065745405,-2.25848560642669},{-2.25848560642669,8.50472838284265}} |
| </pre></li> |
| </ol> |
| <p><a class="anchor" id="background"></a></p><dl class="section user"><dt>Technical Background</dt><dd></dd></dl> |
| <p>When doing regression analysis, we are sometimes interested in the variance of the computed coefficients \( \boldsymbol c \). While the built-in regression functions provide variance estimates, we may prefer a <em>robust</em> variance estimate.</p> |
| <p>The robust variance calculation can be expressed in a sandwich formation, which is the form </p><p class="formulaDsp"> |
| \[ S( \boldsymbol c) = B( \boldsymbol c) M( \boldsymbol c) B( \boldsymbol c) \] |
| </p> |
| <p> where \( B( \boldsymbol c)\) and \( M( \boldsymbol c)\) are matrices. The \( B( \boldsymbol c) \) matrix, also known as the bread, is relatively straight forward, and can be computed as </p><p class="formulaDsp"> |
| \[ B( \boldsymbol c) = n\left(\sum_i^n -H(y_i, x_i, \boldsymbol c) \right)^{-1} \] |
| </p> |
| <p> where \( H \) is the hessian matrix.</p> |
| <p>The \( M( \boldsymbol c)\) matrix has several variations, each with different robustness properties. The form implemented here is the Huber-White sandwich operator, which takes the form </p><p class="formulaDsp"> |
| \[ M_{H} =\frac{1}{n} \sum_i^n \psi(y_i,x_i, \boldsymbol c)^T \psi(y_i,x_i, \boldsymbol c). \] |
| </p> |
| <p>The above method for calculating robust variance (Huber-White estimates) is implemented for linear regression, logistic regression, and multinomial logistic regression. It is useful in calculating variances in a dataset with potentially noisy outliers. The Huber-White implemented here is identical to the "HC0" sandwich operator in the R module "sandwich".</p> |
| <p>When multinomial logistic regression is computed before the multinomial robust regression, it uses a default reference category of zero and the regression coefficients are included in the output table. The regression coefficients in the output are in the same order as the multinomial logistic regression function, which is described below. For a problem with \( K \) dependent variables \( (1, ..., K) \) and \( J \) categories \( (0, ..., J-1) \), let \( {m_{k,j}} \) denote the coefficient for dependent variable \( k \) and category \( j \) . The output is \( {m_{k_1, j_0}, m_{k_1, j_1} \ldots m_{k_1, j_{J-1}}, m_{k_2, j_0}, m_{k_2, j_1} \ldots m_{k_K, j_{J-1}}} \). The order is NOT CONSISTENT with the multinomial regression marginal effect calculation with function <em>marginal_mlogregr</em>. This is deliberate because the interfaces of all multinomial regressions (robust, clustered, ...) will be moved to match that used in marginal.</p> |
| <p>The robust variance of Cox proportional hazards is more complex because coeeficients are trained by maximizing a partial log-likelihood. Therefore, one cannot directly use the formula for \( M( \boldsymbol c) \) as in Huber-White robust estimator. Extra terms are needed. See [4] for details.</p> |
| <p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl> |
| <p>[1] vce(cluster) function in STATA: <a href="http://www.stata.com/help.cgi?vce_option">http://www.stata.com/help.cgi?vce_option</a></p> |
| <p>[2] clustered estimators in R: <a href="http://people.su.se/~ma/clustering.pdf">http://people.su.se/~ma/clustering.pdf</a></p> |
| <p>[3] Achim Zeileis: Object-oriented Computation of Sandwich Estimators. Research Report Series / Department of Statistics and Mathematics, 37. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna. <a href="http://cran.r-project.org/web/packages/sandwich/vignettes/sandwich-OOP.pdf">http://cran.r-project.org/web/packages/sandwich/vignettes/sandwich-OOP.pdf</a></p> |
| <p>[4] D. Y. Lin and L . J. Wei, <em>The Robust Inference for the Cox Proportional Hazards Model</em>, Journal of the American Statistical Association, Vol. 84, No. 408, p.1074 (1989).</p> |
| <p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd>File <a class="el" href="robust_8sql__in.html" title="SQL functions for robust variance linear and logistic regression. ">robust.sql_in</a> documenting the SQL functions File <a class="el" href="robust__variance__coxph_8sql__in.html" title="SQL functions for robust cox proportional hazards regression. ">robust_variance_coxph.sql_in</a> documenting more the SQL functions</dd></dl> |
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