Statsmodels Docs Source Example Formulas Rst At Main Github

Leo Migdal

-Dec 4, 2025, 7:41 AM

statsmodels docs source example formulas rst at main github

There was an error while loading. Please reload this page. Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the patsy docs:

You can import explicitly from statsmodels.formula.api Alternatively, you can just use the formula namespace of the main statsmodels.api. These names are just a convenient way to get access to each model’s from_formula classmethod. See, for instance All of the lower case models accept formula and data arguments, whereas upper case ones take endog and exog design matrices. formula accepts a string which describes the model in terms of a patsy formula.

data takes a pandas data frame or any other data structure that defines a __getitem__ for variable names like a structured array or a dictionary of variables. This wiki page assembles a collection "official" and user-contributed examples, tutorials and recipes for statsmodels. A set of notebook examples are provided as part of the official Statsmodels documentation. If you have an interesting example, or if you can write a quick tutorial describing one of statsmodels' features, please consider posting it here. We would be delighted! Feel free to post your example file in any of the common formats (e.g.

.py, .rst, .html) and to use any hosting service you like. One very slick, free, and convenient alternative is to: www.dropbox.com/scl/fo/mylhfjbpl2zlc5z5m4prq/h?dl=0&rlkey=li52chs6rcl6lejspde6n0oqf State space modeling: Local Linear Trends State space models: concentrating out the scale This document describes the Formula API in statsmodels, which provides an R-style formula interface for specifying statistical models.

The Formula API allows users to express model specifications using a concise, string-based syntax rather than directly managing design matrices. This approach simplifies model creation and enhances readability by allowing users to focus on the statistical relationships rather than data manipulation details. For information about direct data management without formulas, see Data Management. The Formula API provides a consistent interface for specifying models using R-like formulas. It leverages the patsy library for formula parsing and design matrix creation, which then feeds into statsmodels' model classes. Sources: statsmodels/formula/api.py12-32

The Formula API provides formula-based constructors for many statsmodels model classes. Each of these constructors is a convenience function that calls the from_formula method of the corresponding model class. This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page SARIMAX: Frequently Asked Questions (FAQ)

State space modeling: Local Linear Trends Fixed / constrained parameters in state space models Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the patsy docs:

Notice that we called statsmodels.formula.api instead of the usual statsmodels.api. The formula.api hosts many of the same functions found in api (e.g. OLS, GLM), but it also holds lower case counterparts for most of these models. In general, lower case models accept formula and df arguments, whereas upper case ones take endog and exog design matrices. formula accepts a string which describes the model in terms of a patsy formula. df takes a pandas data frame.

dir(smf) will print a list of available models. Formula-compatible models have the following generic call signature: (formula, data, subset=None, *args, **kwargs) To begin, we fit the linear model described on the Getting Started page. Download the data, subset columns, and list-wise delete to remove missing observations: This document covers the documentation generation and publishing process for the statsmodels library. It explains how documentation is structured, built, and deployed to the web.

For information about developing or contributing to documentation content, see the Documentation Contributing Guide. Statsmodels maintains comprehensive documentation including API reference, user guides, tutorials, and examples. The documentation system uses Sphinx to generate HTML from reStructuredText files (.rst) and docstrings in the Python code. The documentation source is organized hierarchically with different levels of information for different audiences: The documentation uses reStructuredText (.rst) with specialized directives for mathematical notation, code examples, and API documentation: The statsmodels library provides the webdoc() function to access documentation directly from Python:

This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki page State space modeling: Local Linear Trends Fixed / constrained parameters in state space models TVP-VAR, MCMC, and sparse simulation smoothing

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Statsmodels Docs Source Example Formulas Rst At Main Github

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