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

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.