statsmodels.genmod.families.family.Binomial.starting_mu Binomial.starting_mu(y) [source] The starting values for the IRLS algorithm for the Binomial family. A good choice for the binomial family is starting_mu = (y + .5)/2

statsmodels.sandbox.regression.gmm.GMMResults.f_test GMMResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be

statsmodels.tools.tools.rank statsmodels.tools.tools.rank(X, cond=1e-12) [source] Return the rank of a matrix X based on its generalized inverse, not the SVD.

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.