Generalized Linear Models class
GLM inherits from statsmodels.base.model.LikelihoodModel
Parameters:  endog : arraylike
exog : arraylike
family : family class instance
missing : str


See also
statsmodels.genmod.families.family, Families, Link Functions
Notes
Only the following combinations make sense for family and link
+ ident log logit probit cloglog pow opow nbinom loglog logc
Gaussian  x x x
inv Gaussian  x x x
binomial  x x x x x x x x x
Poission  x x x
neg binomial  x x x x
gamma  x x x
Not all of these link functions are currently available.
Endog and exog are references so that if the data they refer to are already arrays and these arrays are changed, endog and exog will change.
Attributes
Examples
>>> 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()
>>> gamma_results.params
array([0.01776527, 0.00004962, 0.00203442, 0.00007181, 0.00011185,
0.00000015, 0.00051868, 0.00000243])
>>> gamma_results.scale
0.0035842831734919055
>>> gamma_results.deviance
0.087388516416999198
>>> gamma_results.pearson_chi2
0.086022796163805704
>>> gamma_results.llf
83.017202161073527
Attributes
df_model  float  p  1, where p is the number of regressors including the intercept. 
df_resid  float  The number of observation n minus the number of regressors p. 
endog  array  See Parameters. 
exog  array  See Parameters. 
family  family class instance  A pointer to the distribution family of the model. 
mu  array  The estimated mean response of the transformed variable. 
normalized_cov_params  array  p x p normalized covariance of the design / exogenous data. 
pinv_wexog  array  For GLM this is just the pseudo inverse of the original design. 
scale  float  The estimate of the scale / dispersion. Available after fit is called. 
scaletype  str  The scaling used for fitting the model. Available after fit is called. 
weights  array  The value of the weights after the last iteration of fit. 
Methods
estimate_scale(mu)  Estimates the dispersion/scale. 
fit([start_params, maxiter, method, tol, ...])  Fits a generalized linear model for a given family. 
fit_constrained(constraints[, start_params])  fit the model subject to linear equality constraints 
from_formula(formula, data[, subset])  Create a Model from a formula and dataframe. 
hessian(params[, scale, observed])  Hessian, second derivative of loglikelihood function 
hessian_factor(params[, scale, observed])  Weights for calculating Hessian 
information(params[, scale])  Fisher information matrix. 
initialize()  Initialize a generalized linear model. 
loglike(*args)  Loglikelihood function. 
predict(params[, exog, exposure, offset, linear])  Return predicted values for a design matrix 
score(params[, scale])  score, first derivative of the loglikelihood function 
score_factor(params[, scale])  weights for score for each observation 
score_obs(params[, scale])  score first derivative of the loglikelihood for each observation. 
score_test(params_constrained[, ...])  score test for restrictions or for omitted variables 
Attributes
endog_names  
exog_names 