statsmodels.tsa.vector_ar.var_model.VARProcess.is_stable VARProcess.is_stable(verbose=False) [source] Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.genmod.cov_struct.Nested.update Nested.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.sandbox.regression.gmm.IV2SLS.information IV2SLS.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.discrete.discrete_model.ProbitResults.save ProbitResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remo

statsmodels.discrete.discrete_model.BinaryResults.initialize BinaryResults.initialize(model, params, **kwd)

statsmodels.genmod.families.family.Binomial.loglike Binomial.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Binomial exponential family distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes If endog is binary: llf = scale*sum(endog*log(mu/(1-

statsmodels.sandbox.stats.runs.cochrans_q statsmodels.sandbox.stats.runs.cochrans_q(x) [source] Cochran?s Q test for identical effect of k treatments Cochran?s Q is a k-sample extension of the McNemar test. If there are only two treatments, then Cochran?s Q test and McNemar test are equivalent. Test that the probability of success is the same for each treatment. The alternative is that at least two treatments have a different probability of success. Parameters: x : array_like, 2d (N,k) dat

statsmodels.stats.gof.gof_binning_discrete statsmodels.stats.gof.gof_binning_discrete(rvs, distfn, arg, nsupp=20) [source] get bins for chisquare type gof tests for a discrete distribution Parameters: rvs : array sample data distname : string name of distribution function arg : sequence parameters of distribution nsupp : integer number of bins. The algorithm tries to find bins with equal weights. depending on the distribution, the actual number of bins can be smaller. Returns: fre

statsmodels.discrete.discrete_model.NegativeBinomialResults.summary2 NegativeBinomialResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces

statsmodels.stats.proportion.proportions_ztest statsmodels.stats.proportion.proportions_ztest(count, nobs, value=None, alternative='two-sided', prop_var=False) [source] test for proportions based on normal (z) test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as