Regression Models

A collection of methods for modeling conditional expectation of a response variable.

## Modules | |

Clustered Variance | |

Calculates clustered variance for linear, logistic, and multinomial logistic regression models, and Cox proportional hazards models. | |

Cox-Proportional Hazards Regression | |

Models the relationship between one or more independent predictor variables and the amount of time before an event occurs. | |

Elastic Net Regularization | |

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. | |

Generalized Linear Models | |

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. | |

Linear Regression | |

Also called Ordinary Least Squares Regression, models linear relationship between a dependent variable and one or more independent variables. | |

Logistic Regression | |

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. | |

Marginal Effects | |

Calculates marginal effects for the coefficients in regression problems. | |

Multinomial Regression | |

Multinomial regression is to model the conditional distribution of the multinomial response variable using a linear combination of predictors. | |

Ordinal Regression | |

Regression to model data with ordinal response variable. | |

Robust Variance | |

Calculates Huber-White variance estimates for linear, logistic, and multinomial regression models, and for Cox proportional hazards models. | |