hypotheses

hypotheses

Functions

Name	Description
hypotheses	(Non-)Linear Tests for Null Hypotheses, Joint Hypotheses, Equivalence, Non Superiority, and Non Inferiority.

hypotheses

hypotheses.hypotheses(
    model,
    hypothesis=None,
    conf_level=0.95,
    vcov=True,
    equivalence=None,
    eps_vcov=None,
    joint=False,
    joint_test='f',
)

(Non-)Linear Tests for Null Hypotheses, Joint Hypotheses, Equivalence, Non Superiority, and Non Inferiority.

This function calculates uncertainty estimates as first-order approximate standard errors for linear or non-linear functions of a vector of random variables with known or estimated covariance matrix. It emulates the behavior of the excellent and well-established car::deltaMethod and car::linearHypothesis functions in R, but it supports more models; requires fewer dependencies; expands the range of tests to equivalence and superiority/inferiority; and offers convenience features like robust standard errors.

To learn more, visit the package website: https://marginaleffects.com/

Warning #1: Tests are conducted directly on the scale defined by the type argument. For some models, it can make sense to conduct hypothesis or equivalence tests on the "link" scale instead of the "response" scale which is often the default.

Warning #2: For hypothesis tests on objects produced by the marginaleffects package, it is safer to use the hypothesis argument of the original function. Using hypotheses() may not work in certain environments, in lists, or when working programmatically with *apply style functions.

Warning #3: The tests assume that the hypothesis expression is (approximately) normally distributed, which for non-linear functions of the parameters may not be realistic. More reliable confidence intervals can be obtained using the inferences() function with method = "boot".

Parameters: model : object Model object estimated by statsmodels hypothesis : str formula, int, float, numpy array or optional The null hypothesis value. Default is None. conf_level : float, optional Confidence level for the hypothesis test. Default is 0.95. vcov : bool, optional Whether to use the covariance matrix in the hypothesis test. Default is True. equivalence : tuple, optional The equivalence range for the hypothesis test. Default is None.

Returns: MarginaleffectsDataFrame A DataFrame containing the results of the hypothesis tests.

Examples:

# When `hypothesis` is `None`, `hypotheses()` returns a DataFrame of parameters
hypotheses(model)

# A different null hypothesis
hypotheses(model, hypothesis = 3)

# Test of equality between coefficients
hypotheses(model, hypothesis="param1 = param2")

# Non-linear function
hypotheses(model, hypothesis="exp(param1 + param2) = 0.1")

# Robust standard errors
hypotheses(model, hypothesis="param1 = param2", vcov="HC3")

# Equivalence, non-inferiority, and non-superiority tests
hypotheses(model, equivalence=(0, 10))