Generalized linear models currently supports estimation using the one-parameter exponential families
>>> 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()
see also the examples and the tests folders
GLMResults(model, params, ...) | Class to contain GLM results. |
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. |
The link functions currently implemented are the following. Not all link functions are available for each distribution family. The list of available link functions can be obtained by
>>> sm.families.family.<familyname>.links
Link | A generic link function for one-parameter exponential family. |
CDFLink([dbn]) | The use the CDF of a scipy.stats distribution |
CLogLog | The complementary log-log transform |
Log | The log transform |
Logit | The logit transform |
NegativeBinomial([alpha]) | The negative binomial link function |
Power([power]) | The power transform |
cauchy() | The Cauchy (standard Cauchy CDF) transform |
cloglog | The CLogLog transform link function. |
identity() | The identity transform |
inverse_power() | The inverse transform |
inverse_squared() | The inverse squared transform |
log | The log transform |
logit | |
nbinom([alpha]) | The negative binomial link function. |
probit([dbn]) | The probit (standard normal CDF) transform |