statsmodels.genmod.qif.QIF¶

class statsmodels.genmod.qif.QIF(endog, exog, groups, family=None, cov_struct=None, missing='none', **kwargs)[source]¶

Fit a regression model using quadratic inference functions (QIF).

QIF is an alternative to GEE that can be more efficient, and that offers different approaches for model selection and inference.

Parameters:

endog : array_like

The dependent variables of the regression.

exog : array_like

The independent variables of the regression.

groups : array_like

Labels indicating which group each observation belongs to. Observations in different groups should be independent.

family : genmod family

An instance of a GLM family.

cov_struct : QIFCovariance instance

An instance of a QIFCovariance.

References

A. Qu, B. Lindsay, B. Li (2000). Improving Generalized Estimating Equations using Quadratic Inference Functions, Biometrika 87:4. www.jstor.org/stable/2673612

Attributes

`endog_names`	Names of endogenous variables.
`exog_names`	Names of exogenous variables.

Methods

`estimate_scale`(params)	Estimate the dispersion/scale.
`fit`([maxiter, start_params, tol, gtol, …])	Fit a GLM to correlated data using QIF.
`from_formula`(formula, groups, data[, subset])	Create a QIF model instance from a formula and dataframe.
`objective`(params)	Calculate the gradient of the QIF objective function.
`predict`(params[, exog])	After a model has been fit predict returns the fitted values.

Methods

`estimate_scale`(params)	Estimate the dispersion/scale.
`fit`([maxiter, start_params, tol, gtol, …])	Fit a GLM to correlated data using QIF.
`from_formula`(formula, groups, data[, subset])	Create a QIF model instance from a formula and dataframe.
`objective`(params)	Calculate the gradient of the QIF objective function.
`predict`(params[, exog])	After a model has been fit predict returns the fitted values.

Properties

`endog_names`	Names of endogenous variables.
`exog_names`	Names of exogenous variables.