statsmodels.discrete.discrete_model.Probit.loglikeobs Probit.loglikeobs(params) [source] Log-likelihood of probit model for each observation Parameters: params : array-like The parameters of the model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where . This simplification comes from the fact that the normal distribution is symmetric.

statsmodels.regression.quantile_regression.QuantRegResults.fvalue static QuantRegResults.fvalue()

statsmodels.discrete.discrete_model.NegativeBinomial.cdf NegativeBinomial.cdf(X) The cumulative distribution function of the model.

statsmodels.discrete.discrete_model.LogitResults.llr static LogitResults.llr()

statsmodels.regression.mixed_linear_model.MixedLMResults.llf static MixedLMResults.llf()

statsmodels.tsa.vector_ar.var_model.VARResults.summary VARResults.summary() [source] Compute console output summary of estimates Returns: summary : VARSummary

statsmodels.discrete.discrete_model.LogitResults.wald_test LogitResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 given as a string. See the examples. tuple : A tu

statsmodels.sandbox.regression.gmm.GMMResults.jval static GMMResults.jval() [source]

statsmodels.discrete.discrete_model.CountResults.fittedvalues static CountResults.fittedvalues()

statsmodels.tsa.arima_model.ARMAResults.maroots static ARMAResults.maroots() [source]