statsmodels.discrete.discrete_model.ProbitResults.f_test ProbitResults.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

statsmodels.sandbox.regression.gmm.LinearIVGMM class statsmodels.sandbox.regression.gmm.LinearIVGMM(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds) [source] class for linear instrumental variables models estimated with GMM Uses closed form expression instead of nonlinear optimizers for each step of the iterative GMM. The model is assumed to have the following moment condition E( z * (y - x beta)) = 0 Where y is the dependent endogenous variable, x are the explan

statsmodels.genmod.families.family.Binomial.starting_mu Binomial.starting_mu(y) [source] The starting values for the IRLS algorithm for the Binomial family. A good choice for the binomial family is starting_mu = (y + .5)/2

statsmodels.sandbox.regression.gmm.GMMResults.f_test GMMResults.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 be

statsmodels.tools.tools.rank statsmodels.tools.tools.rank(X, cond=1e-12) [source] Return the rank of a matrix X based on its generalized inverse, not the SVD.

statsmodels.discrete.discrete_model.MultinomialResults.llf static MultinomialResults.llf()

statsmodels.sandbox.sysreg.SUR.fit SUR.fit(igls=False, tol=1e-05, maxiter=100) [source] igls : bool Iterate until estimates converge if sigma is None instead of two-step GLS, which is the default is sigma is None. tol : float maxiter : int Notes This ia naive implementation that does not exploit the block diagonal structure. It should work for ill-conditioned sigma but this is untested.

statsmodels.regression.mixed_linear_model.MixedLMResults.normalized_cov_params MixedLMResults.normalized_cov_params()

statsmodels.stats.moment_helpers.mnc2mc statsmodels.stats.moment_helpers.mnc2mc(mnc, wmean=True) [source] convert non-central to central moments, uses recursive formula optionally adjusts first moment to return mean