statsmodels.genmod.families.links.Power.inverse_deriv Power.inverse_deriv(z) [source] 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.genmod.generalized_linear_model.GLM.information GLM.information(params, scale=None) [source] Fisher information matrix.

statsmodels.genmod.generalized_estimating_equations.GEEResults.conf_int GEEResults.conf_int(alpha=0.05, cols=None, cov_type=None) [source] Returns confidence intervals for the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. i.e., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return cov_type : string The covariance type used for computing standard err

statsmodels.genmod.cov_struct.CovStruct.covariance_matrix_solve CovStruct.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each observatio

statsmodels.genmod.cov_struct.Nested.covariance_matrix_solve Nested.covariance_matrix_solve(expval, index, stdev, rhs) Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each observation in the group.

statsmodels.discrete.discrete_model.NegativeBinomialResults.lnalpha_std_err static NegativeBinomialResults.lnalpha_std_err() [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.f_test IVRegressionResults.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 hypothes

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.jac_error NonlinearIVGMM.jac_error(params, weights, args=None, centered=True, epsilon=None) [source]

statsmodels.duration.hazard_regression.PHReg.fit_regularized PHReg.fit_regularized(method='coord_descent', maxiter=100, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-07, zero_tol=1e-08, **kwargs) [source] Return a regularized fit to a linear regression model. Parameters: method : : Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar

statsmodels.robust.norms.LeastSquares.psi LeastSquares.psi(z) [source] The psi function for the least squares estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z