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<title>MADlib: Regression Models</title>
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<div class="header">
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<a href="#groups">Modules</a>  </div>
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<div class="title">Regression Models<div class="ingroups"><a class="el" href="group__grp__super.html">Supervised Learning</a></div></div>  </div>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<p>A collection of methods for modeling conditional expectation of a response variable. </p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="groups"></a>
Modules</h2></td></tr>
<tr class="memitem:group__grp__clustered__errors"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__clustered__errors.html">Clustered Variance</a></td></tr>
<tr class="memdesc:group__grp__clustered__errors"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates clustered variance for linear, logistic, and multinomial logistic regression models, and Cox proportional hazards models. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__cox__prop__hazards"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__cox__prop__hazards.html">Cox-Proportional Hazards Regression</a></td></tr>
<tr class="memdesc:group__grp__cox__prop__hazards"><td class="mdescLeft">&#160;</td><td class="mdescRight">Models the relationship between one or more independent predictor variables and the amount of time before an event occurs. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__elasticnet"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__elasticnet.html">Elastic Net Regularization</a></td></tr>
<tr class="memdesc:group__grp__elasticnet"><td class="mdescLeft">&#160;</td><td class="mdescRight">Generates a regularized regression model for variable selection in linear and logistic regression problems, combining the L1 and L2 penalties of the lasso and ridge methods. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__glm"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__glm.html">Generalized Linear Models</a></td></tr>
<tr class="memdesc:group__grp__glm"><td class="mdescLeft">&#160;</td><td class="mdescRight">Estimate generalized linear model (GLM). GLM is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__linreg"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__linreg.html">Linear Regression</a></td></tr>
<tr class="memdesc:group__grp__linreg"><td class="mdescLeft">&#160;</td><td class="mdescRight">Also called Ordinary Least Squares Regression, models linear relationship between a dependent variable and one or more independent variables. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__logreg"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__logreg.html">Logistic Regression</a></td></tr>
<tr class="memdesc:group__grp__logreg"><td class="mdescLeft">&#160;</td><td class="mdescRight">Models the relationship between one or more predictor variables and a binary categorical dependent variable by predicting the probability of the dependent variable using a logistic function. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__marginal"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__marginal.html">Marginal Effects</a></td></tr>
<tr class="memdesc:group__grp__marginal"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates marginal effects for the coefficients in regression problems. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__multinom"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__multinom.html">Multinomial Regression</a></td></tr>
<tr class="memdesc:group__grp__multinom"><td class="mdescLeft">&#160;</td><td class="mdescRight">Multinomial regression is to model the conditional distribution of the multinomial response variable using a linear combination of predictors. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__ordinal"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__ordinal.html">Ordinal Regression</a></td></tr>
<tr class="memdesc:group__grp__ordinal"><td class="mdescLeft">&#160;</td><td class="mdescRight">Regression to model data with ordinal response variable. <br /></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:group__grp__robust"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="group__grp__robust.html">Robust Variance</a></td></tr>
<tr class="memdesc:group__grp__robust"><td class="mdescLeft">&#160;</td><td class="mdescRight">Calculates Huber-White variance estimates for linear, logistic, and multinomial regression models, and for Cox proportional hazards models. <br /></td></tr>
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