statsmodels.duration.hazard_regression.PHRegResults.get_distribution PHRegResults.get_distribution() [source] Returns a scipy distribution object corresponding to the distribution of uncensored endog (duration) values for each case. Returns: A list of objects of type scipy.stats.distributions.rv_discrete : Notes The distributions are obtained from a simple discrete estimate of the survivor function that puts all mass on the observed failure times wihtin a stratum.

statsmodels.emplike.descriptive.DescStatUV.ci_mean DescStatUV.ci_mean(sig=0.05, method='gamma', epsilon=1e-08, gamma_low=-10000000000, gamma_high=10000000000) [source] Returns the confidence interval for the mean. Parameters: sig : float significance level. Default is .05 method : str Root finding method, Can be ?nested-brent? or ?gamma?. Default is ?gamma? ?gamma? Tries to solve for the gamma parameter in the Lagrange (see Owen pg 22) and then determine the weights. ?nested brent? uses

statsmodels.tsa.varma_process.VarmaPoly.reduceform VarmaPoly.reduceform(apoly) [source] this assumes no exog, todo

statsmodels.tsa.arima_model.ARMAResults.arroots static ARMAResults.arroots() [source]

statsmodels.regression.linear_model.RegressionResults.normalized_cov_params RegressionResults.normalized_cov_params()

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.regression.linear_model.OLSResults.eigenvals static OLSResults.eigenvals() Return eigenvalues sorted in decreasing order.

statsmodels.sandbox.regression.try_catdata.cat2dummy statsmodels.sandbox.regression.try_catdata.cat2dummy(y, nonseq=0) [source]