statsmodels.genmod.generalized_linear_model.GLM.fit¶
- GLM.fit(start_params=None, maxiter=100, method='IRLS', tol=1e-08, scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None, full_output=True, disp=False, max_start_irls=3, **kwargs)[source]¶
Fits a generalized linear model for a given family.
- Parameters
- start_paramsarray_like,
optional
Initial guess of the solution for the loglikelihood maximization. The default is family-specific and is given by the
family.starting_mu(endog)
. If start_params is given then the initial mean will be calculated asnp.dot(exog, start_params)
.- maxiter
int
,optional
Default is 100.
- method
str
Default is ‘IRLS’ for iteratively reweighted least squares. Otherwise gradient optimization is used.
- tol
float
Convergence tolerance. Default is 1e-8.
- scale
str
orfloat
,optional
scale can be ‘X2’, ‘dev’, or a float The default value is None, which uses X2 for Gamma, Gaussian, and Inverse Gaussian. X2 is Pearson’s chi-square divided by df_resid. The default is 1 for the Binomial and Poisson families. dev is the deviance divided by df_resid
- cov_type
str
The type of parameter estimate covariance matrix to compute.
- cov_kwdsdict-like
Extra arguments for calculating the covariance of the parameter estimates.
- use_tbool
If True, the Student t-distribution is used for inference.
- full_outputbool,
optional
Set to True to have all available output in the Results object’s mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information. Not used if methhod is IRLS.
- dispbool,
optional
Set to True to print convergence messages. Not used if method is IRLS.
- max_start_irls
int
The number of IRLS iterations used to obtain starting values for gradient optimization. Only relevant if method is set to something other than ‘IRLS’.
- atol
float
,optional
(available with IRLS fits) The absolute tolerance criterion that must be satisfied. Defaults to
tol
. Convergence is attained when: \(rtol * prior + atol > abs(current - prior)\)- rtol
float
,optional
(available with IRLS fits) The relative tolerance criterion that must be satisfied. Defaults to 0 which means
rtol
is not used. Convergence is attained when: \(rtol * prior + atol > abs(current - prior)\)- tol_criterion
str
,optional
(available with IRLS fits) Defaults to
'deviance'
. Can optionally be'params'
.- wls_method
str
,optional
(available with IRLS fits) options are ‘lstsq’, ‘pinv’ and ‘qr’ specifies which linear algebra function to use for the irls optimization. Default is lstsq which uses the same underlying svd based approach as ‘pinv’, but is faster during iterations. ‘lstsq’ and ‘pinv’ regularize the estimate in singular and near-singular cases by truncating small singular values based on rcond of the respective numpy.linalg function. ‘qr’ is only valid for cases that are not singular nor near-singular.
- optim_hessian{‘eim’, ‘oim’},
optional
(available with scipy optimizer fits) When ‘oim’–the default–the observed Hessian is used in fitting. ‘eim’ is the expected Hessian. This may provide more stable fits, but adds assumption that the Hessian is correctly specified.
- start_paramsarray_like,
Notes
If method is ‘IRLS’, then an additional keyword ‘attach_wls’ is available. This is currently for internal use only and might change in future versions. If attach_wls’ is true, then the final WLS instance of the IRLS iteration is attached to the results instance as results_wls attribute.