statsmodels.graphics.factorplots.interaction_plot statsmodels.graphics.factorplots.interaction_plot(x, trace, response, func=, ax=None, plottype='b', xlabel=None, ylabel=None, colors=[], markers=[], linestyles=[], legendloc='best', legendtitle=None, **kwargs) [source] Interaction plot for factor level statistics. Note. If categorial factors are supplied levels will be internally recoded to integers. This ensures matplotlib compatiblity. uses pandas.DataFrame to calculate an aggregate statist

statsmodels.sandbox.regression.gmm.IVRegressionResults.wald_test IVRegressionResults.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.

statsmodels.sandbox.regression.gmm.LinearIVGMM.fitgmm LinearIVGMM.fitgmm(start, weights=None, optim_method=None, **kwds) [source] estimate parameters using GMM for linear model Uses closed form expression instead of nonlinear optimizers Parameters: start : not used starting values for minimization, not used, only for consistency of method signature weights : array weighting matrix for moment conditions. If weights is None, then the identity matrix is used optim_method : not used, optim

statsmodels.discrete.discrete_model.DiscreteResults.pvalues static DiscreteResults.pvalues()

statsmodels.nonparametric.kde.KDEUnivariate.entropy static KDEUnivariate.entropy() [source] Returns the differential entropy evaluated at the support Notes Will not work if fit has not been called. 1e-12 is added to each probability to ensure that log(0) is not called.

statsmodels.genmod.generalized_linear_model.GLMResults.tvalues static GLMResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.tsa.arima_model.ARIMA.loglike_kalman ARIMA.loglike_kalman(params, set_sigma2=True) Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.

statsmodels.discrete.discrete_model.MNLogit.fit MNLogit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional The metho

statsmodels.robust.norms.RobustNorm.psi RobustNorm.psi(z) [source] Derivative of rho. Sometimes referred to as the influence function. Abstract method: psi = rho?

statsmodels.sandbox.regression.gmm.GMMResults.normalized_cov_params GMMResults.normalized_cov_params()