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Fitting Models Using R Style Formulas Statsmodels 0 14 4

Leo Migdal
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fitting models using r style formulas statsmodels 0 14 4

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 in addition to the usual statsmodels.api. In fact, statsmodels.api is used here only to load the dataset.

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: I’ve been working with statistical models in Python for years, and one feature that transformed how I approach regression analysis is statsmodels’ R-style formula syntax. Coming from R, I appreciated having a familiar, readable way to specify models without manually constructing design matrices. Let me show you how this works and why it matters for your statistical modeling workflow.

Statsmodels allows users to fit statistical models using R-style formulas since version 0.5.0, using the patsy package internally to convert formulas and data into matrices for model fitting. The formula syntax provides an intuitive, readable way to specify relationships between variables. At its core, the formula interface uses string notation to describe your model. Instead of creating arrays and matrices manually, you write something like “sales ~ advertising + price” and statsmodels handles the rest. The tilde (~) separates your dependent variable on the left from independent variables on the right, while the plus sign (+) adds variables to your model. The formula API lives in statsmodels.formula.api, which you import separately from the standard API.

Lower case model functions like ols() accept formula and data arguments, while upper case versions take endog and exog design matrices. I prefer the formula approach because it keeps my code readable and reduces preprocessing steps. The standard api provides dataset loading and other utilities, while formula.api gives you access to formula-compatible model functions. I always import both because statsmodels.formula.api doesn’t include everything you might need. Looking at the summary printed above, notice that patsy determined that elements of Region were text strings, so it treated Region as a categorical variable. patsy's default is also to include an intercept, so we automatically dropped one of the Region categories.

If Region had been an integer variable that we wanted to treat explicitly as categorical, we could have done so by using the C( ) operator: We have already seen that "~" separates the left-hand side of the model from the right-hand side, and that "+" adds new columns to the design matrix. The "-" sign can be used to remove columns/variables. For instance, we can remove the intercept from a model by: ":" adds a new column to the design matrix with the interaction of the other two columns. "*" will also include the individual columns that were multiplied together:

Even if a given statsmodels function does not support formulas, you can still use patsy's formula language to produce design matrices. Those matrices can then be fed to the fitting function as endog and exog arguments. There was an error while loading. Please reload this page. 自版本0.5.0起，statsmodels允许用户使用R风格的公式来拟合统计模型。在内部，statsmodels使用patsy包将公式和数据转换为模型拟合中使用的矩阵。公式框架非常强大；本教程仅触及表面。公式语言的完整描述可以在patsy文档中找到： 或者，您可以仅使用主 statsmodels.api 的 formula 命名空间。

这些名称只是获取每个模型的 from_formula 类方法的一种便捷方式。例如，参见 所有小写模型都接受formula和data参数，而大写模型则接受endog和exog设计矩阵。formula接受一个字符串，该字符串以patsy公式的形式描述模型。data接受一个pandas数据框或任何其他定义了变量名的__getitem__的数据结构，如结构化数组或变量字典。 兼容公式的模型具有以下通用调用签名：(formula, data, subset=None, *args, **kwargs) 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. Or you can use the following conventioin

These names are just a convenient way to get access to each model's from_formula classmethod. See, for instance

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