statsmodels.miscmodels.count.PoissonZiGMLE.fit PoissonZiGMLE.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit

statsmodels.tsa.vector_ar.var_model.VARResults.tvalues static VARResults.tvalues() [source] Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test significance.

statsmodels.tsa.vector_ar.var_model.VARProcess class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, intercept, sigma_u, names=None) [source] Class represents a known VAR(p) process Parameters: coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k) Returns: **Attributes:** : Methods acf([nlags]) Compute theoretical autocovariance function acorr([nlags]) Compute theoretical autocorrelation function forecast(y, steps) Produ

statsmodels.discrete.discrete_model.BinaryResults.llnull static BinaryResults.llnull()

statsmodels.discrete.discrete_model.DiscreteResults.bic static DiscreteResults.bic() [source]

statsmodels.discrete.discrete_model.Logit.score_obs Logit.score_obs(params) [source] Logit model Jacobian of the log-likelihood for each observation Parameters: params: array-like : The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations

statsmodels.graphics.gofplots.ProbPlot.theoretical_quantiles static ProbPlot.theoretical_quantiles() [source]

statsmodels.regression.linear_model.OLSResults.eigenvals static OLSResults.eigenvals() Return eigenvalues sorted in decreasing order.

statsmodels.sandbox.regression.try_catdata.cat2dummy statsmodels.sandbox.regression.try_catdata.cat2dummy(y, nonseq=0) [source]

statsmodels.tsa.vector_ar.irf.IRAnalysis.lr_effect_cov IRAnalysis.lr_effect_cov(orth=False) [source]