# 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:
endogarray_like

The dependent variables of the regression.

exogarray_like

The independent variables of the regression.

groupsarray_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.

Properties

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

Last update: May 05, 2023
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