4 4 1 1 2 Statsmodels Formula Api Glm Statsmodels Api V1

Leo Migdal

-Dec 4, 2025, 8:20 AM

4 4 1 1 2 statsmodels formula api glm statsmodels api v1

Create a Model from a formula and dataframe. An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame. Columns to drop from the design matrix. Cannot be used to drop terms involving categoricals. Additional positional argument that are passed to the model.

These are passed to the model with one exception. The eval_env keyword is passed to patsy. It can be either a patsy:patsy.EvalEnvironment object or an integer indicating the depth of the namespace to use. For example, the default eval_env=0 uses the calling namespace. If you wish to use a “clean” environment set eval_env=-1. GLM inherits from statsmodels.base.model.LikelihoodModel

1d array of endogenous response variable. This array can be 1d or 2d. Binomial family models accept a 2d array with two columns. If supplied, each observation is expected to be [success, failure]. A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user (models specified using a formula include an intercept by default).

See statsmodels.tools.add_constant. The default is Gaussian. To specify the binomial distribution family = sm.family.Binomial() Each family can take a link instance as an argument. See statsmodels.family.family for more information. Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done.

If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’ 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.

There was an error while loading. Please reload this page. The main statsmodels API is split into models: statsmodels.api: Cross-sectional models and methods. Canonically imported using import statsmodels.api as sm. statsmodels.tsa.api: Time-series models and methods.

Canonically imported using import statsmodels.tsa.api as tsa. statsmodels.formula.api: A convenience interface for specifying models using formula strings and DataFrames. This API directly exposes the from_formula class method of models that support the formula API. Canonically imported using import statsmodels.formula.api as smf The API focuses on models and the most frequently used statistical test, and tools. Import Paths and Structure explains the design of the two API modules and how importing from the API differs from directly importing from the module where the model is defined.

See the detailed topic pages in the User Guide for a complete list of available models, statistics, and tools. When diving into statistical modeling with Python’s powerful Statsmodels library, preparing your data can sometimes feel like a separate, time-consuming task. Manually creating dummy variables, interaction terms, or transformations often adds complexity before you even fit your first model. This is where the python statsmodels formula api shines! Inspired by R’s elegant formula syntax, it provides a concise and intuitive way to define your models directly from a string, handled by the fantastic Patsy library under the hood. It simplifies your workflow, making your code cleaner and more readable.

In this comprehensive guide, we’ll explore everything you need to know about the Statsmodels Formula API, from basic syntax to advanced transformations and interactions, empowering you to build sophisticated models with ease. The Statsmodels Formula API allows you to specify statistical models using a string-based formula, much like you would in R. This formula describes the relationship between your dependent (response) variable and your independent (predictor) variables. Its primary advantage is abstracting away the tedious data preparation steps. It automatically handles tasks like creating design matrices, generating dummy variables for categorical features, and even constructing interaction terms, all based on a simple formula string. This drastically reduces boilerplate code and improves model interpretability.

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I am modeling how a a chemical reaction yield (Y) depends on the ratio between reagents (X). The higher the ratio, the higher the reagent conversion, with a clear inflection at around 1.5. Below is the full data set I have Generalized linear models currently supports estimation using the one-parameter exponential families. See Module Reference for commands and arguments. The statistical model for each observation \(i\) is assumed to be

\(Y_i \sim F_{EDM}(\cdot|\theta,\phi,w_i)\) and \(\mu_i = E[Y_i|x_i] = g^{-1}(x_i^\prime\beta)\). where \(g\) is the link function and \(F_{EDM}(\cdot|\theta,\phi,w)\) is a distribution of the family of exponential dispersion models (EDM) with natural parameter \(\theta\), scale parameter \(\phi\) and weight \(w\). Its density is given by

4 4 1 1 2 Statsmodels Formula Api Glm Statsmodels Api V1

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Create A Model From A Formula And Dataframe. An Array-like

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