statsmodels.discrete.discrete_model.MultinomialResults.llf static MultinomialResults.llf()

statsmodels.sandbox.sysreg.SUR.fit SUR.fit(igls=False, tol=1e-05, maxiter=100) [source] igls : bool Iterate until estimates converge if sigma is None instead of two-step GLS, which is the default is sigma is None. tol : float maxiter : int Notes This ia naive implementation that does not exploit the block diagonal structure. It should work for ill-conditioned sigma but this is untested.

statsmodels.regression.mixed_linear_model.MixedLMResults.normalized_cov_params MixedLMResults.normalized_cov_params()

statsmodels.stats.moment_helpers.mnc2mc statsmodels.stats.moment_helpers.mnc2mc(mnc, wmean=True) [source] convert non-central to central moments, uses recursive formula optionally adjusts first moment to return mean

statsmodels.discrete.discrete_model.NegativeBinomial.score_obs NegativeBinomial.score_obs(params) [source]

statsmodels.robust.robust_linear_model.RLM.information RLM.information(params) [source]

Generalized Least Squares Link to Notebook GitHub In [1]: from __future__ import print_function import statsmodels.api as sm import numpy as np from statsmodels.iolib.table import (SimpleTable, default_txt_fmt) The Longley dataset is a time series dataset: In [2]: data = sm.datasets.longley.load() data.exog = sm.add_constant(data.exog) print(data.exog[:5]) [[ 1. 83. 234289. 2356. 1590. 107608. 1947. ] [ 1. 88.5 259426.

statsmodels.tsa.ar_model.AR.information AR.information(params) [source] Not Implemented Yet

statsmodels.discrete.discrete_model.Logit.fit Logit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional The

statsmodels.tsa.vector_ar.var_model.VARProcess.mean VARProcess.mean() [source] Mean of stable process Lutkepohl eq. 2.1.23