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

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

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.tsa.arima_model.ARMA.fit ARMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to trans

statsmodels.discrete.discrete_model.NegativeBinomial.score NegativeBinomial.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.MultinomialModel.score MultinomialModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.genmod.families.links.inverse_power.inverse_deriv inverse_power.inverse_deriv(z) Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :

statsmodels.sandbox.regression.gmm.IVGMMResults.f_test IVGMMResults.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

statsmodels.tsa.ar_model.ARResults.pvalues static ARResults.pvalues() [source]

statsmodels.discrete.discrete_model.NegativeBinomialResults.bse static NegativeBinomialResults.bse()