statsmodels.genmod.qif.QIF

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

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.

familygenmod family

An instance of a GLM family.

cov_structQIFCovariance 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.