statsmodels.genmod.families.family.InverseGaussian.resid_anscombe InverseGaussian.resid_anscombe(endog, mu) [source] The Anscombe residuals for the inverse Gaussian distribution Parameters: endog : array Endogenous response variable mu : array Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals for the inverse Gaussian distribution as defined below Notes resid_anscombe = log(endog/mu)/sqrt(mu)

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fititer NonlinearIVGMM.fititer(start, maxiter=2, start_invweights=None, weights_method='cov', wargs=(), optim_method='bfgs', optim_args=None) iterative estimation with updating of optimal weighting matrix stopping criteria are maxiter or change in parameter estimate less than self.epsilon_iter, with default 1e-6. Parameters: start : array starting value for parameters maxiter : int maximum number of iterations start_weights : array (nmom

statsmodels.genmod.generalized_estimating_equations.GEEResults.sensitivity_params GEEResults.sensitivity_params(dep_params_first, dep_params_last, num_steps) [source] Refits the GEE model using a sequence of values for the dependence parameters. Parameters: dep_params_first : array-like The first dep_params in the sequence dep_params_last : array-like The last dep_params in the sequence num_steps : int The number of dep_params in the sequence Returns: results : array-like The GEERe

statsmodels.genmod.generalized_estimating_equations.GEEResults.split_centered_resid static GEEResults.split_centered_resid() Returns the residuals centered within each group. The residuals are returned as a list of arrays containing the centered residuals for each cluster.

statsmodels.iolib.foreign.StataReader.file_format StataReader.file_format() [source] Returns the file format. Returns: out : int Notes Format 113: Stata 8/9 Format 114: Stata 10/11 Format 115: Stata 12

statsmodels.sandbox.regression.gmm.GMM.gradient_momcond GMM.gradient_momcond(params, epsilon=0.0001, centered=True) [source] gradient of moment conditions Parameters: params : ndarray parameter at which the moment conditions are evaluated epsilon : float stepsize for finite difference calculation centered : bool This refers to the finite difference calculation. If centered is true, then the centered finite difference calculation is used. Otherwise the one-sided forward differences are

statsmodels.robust.norms.Hampel.rho Hampel.rho(z) [source] The robust criterion function for Hampel?s estimator Parameters: z : array-like 1d array Returns: rho : array rho(z) = (1/2.)*z**2 for |z| <= a rho(z) = a*|z| - 1/2.*a**2 for a < |z| <= b rho(z) = a*(c*|z|-(1/2.)*z**2)/(c-b) for b < |z| <= c rho(z) = a*(b + c - a) for |z| > c

statsmodels.genmod.cov_struct.Nested.update Nested.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.cov_struct.Nested.initialize Nested.initialize(model) [source] Called on the first call to update ilabels is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. designx is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY.