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Generalized Linear Models

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

See Module Reference for commands and arguments.

Examples

# Load modules and data
import statsmodels.api 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.families.Gamma())
gamma_results = gamma_model.fit()

Detailed examples can be found here:

Technical Documentation

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.

Module Reference

Model Class

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

Results Class

GLMResults(model, params, ...[, cov_type, ...]) 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.