statsmodels.genmod.cov_struct.Nested.summary Nested.summary() [source] Returns a summary string describing the state of the dependence structure.

statsmodels.sandbox.sysreg.SUR.predict SUR.predict(design) [source]

statsmodels.discrete.discrete_model.ProbitResults.bic static ProbitResults.bic()

statsmodels.miscmodels.tmodel.TLinearModel.fit TLinearModel.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit

statsmodels.nonparametric.kernel_regression.KernelCensoredReg class statsmodels.nonparametric.kernel_regression.KernelCensoredReg(endog, exog, var_type, reg_type, bw='cv_ls', censor_val=0, defaults=) [source] Nonparametric censored regression. Calculates the condtional mean E[y|X] where y = g(X) + e, where y is left-censored. Left censored variable Y is defined as Y = min {Y', L} where L is the value at which Y is censored and Y' is the true value of the variable. Parameters: endog: list wi

statsmodels.duration.hazard_regression.PHRegResults.t_test PHRegResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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 tuple

statsmodels.sandbox.distributions.transformed.LogTransf_gen.entropy LogTransf_gen.entropy(*args, **kwds) Differential entropy of the RV. 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).

statsmodels.sandbox.distributions.extras.NormExpan_gen.nnlf NormExpan_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.genmod.generalized_estimating_equations.GEEMargins.pvalues static GEEMargins.pvalues() [source]

statsmodels.genmod.families.links.identity.inverse identity.inverse(z) Inverse of the power transform link function Parameters: `z` : array-like Value of the transformed mean parameters at p Returns: `p` : array Mean parameters Notes g^(-1)(z`) = z`**(1/`power)