statsmodels.regression.linear_model.OLSResults.llf static OLSResults.llf()

statsmodels.nonparametric.kernel_regression.KernelReg.r_squared KernelReg.r_squared() [source] Returns the R-Squared for the nonparametric regression. Notes For more details see p.45 in [2] The R-Squared is calculated by: where is the mean calculated in fit at the exog points.

statsmodels.sandbox.regression.gmm.LinearIVGMM.fit LinearIVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None) Estimate parameters using GMM and return GMMResults TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results Parameters: start_params : array (optional) starting value for parameters ub m

statsmodels.genmod.families.links.Link.deriv Link.deriv(p) [source] Derivative of the link function g?(p). Just a placeholder. Parameters: p : array-like Returns: The value of the derivative of the link function g?(p) :

statsmodels.genmod.families.family.Gamma.weights Gamma.weights(mu) Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))

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

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.std ExpTransf_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution

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.IVRegressionResults.wresid static IVRegressionResults.wresid()

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