Generalized Linear Models

Introduction

Generalized linear models currently supports estimation using the one-parameter exponential families

References

Gill, Jeff. 2000. Generalized Linear Models: A Unified Approach.
SAGE QASS Series.
Green, PJ. 1984. “Iteratively reweighted least squares for maximum
likelihood estimation, and some robust and resistant alternatives.” Journal of the Royal Statistical Society, Series B, 46, 149-192.
Hardin, J.W. and Hilbe, J.M. 2007. “Generalized Linear Models and
Extensions.” 2nd ed. Stata Press, College Station, TX.
McCullagh, P. and Nelder, J.A. 1989. “Generalized Linear Models.” 2nd ed.
Chapman & Hall, Boca Rotan.

Examples

>>> import scikits.statsmodels as sm
>>> data = sm.datasets.scotland.Load()
>>> data.exog = sm.add_constant(data.exog)

Instantiate a gamma family model with the default link function.

>>> gamma_model = sm.GLM(data.endog, data.exog,
        family=sm.family.Gamma())
>>> gamma_results = gamma_model.fit()

see also the examples and the tests folders

Module Reference

Model Class

GLM(endog, exog[, family]) Generalized Linear Models class

Results Class

GLMResults(model, params, ...) Class to contain GLM results.

Families

The distribution families currently implemented are

Family(link, variance) The parent class for one-parameter exponential families.
Binomial([link]) Binomial exponential family distribution.
Gamma([link]) Gamma exponential family distribution.
Gaussian([link]) Gaussian exponential family distribution.
InverseGaussian([link]) InverseGaussian exponential family.
NegativeBinomial([link, alpha]) Negative Binomial exponential family.
Poisson([link]) Poisson exponential family.