statsmodels.regression.quantile_regression.QuantRegResults.prsquared static QuantRegResults.prsquared() [source]

statsmodels.graphics.correlation.plot_corr_grid statsmodels.graphics.correlation.plot_corr_grid(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None, ynames=None, fig=None, cmap='RdYlBu_r') [source] Create a grid of correlation plots. The individual correlation plots are assumed to all have the same variables, axis labels can be specified only once. Parameters: dcorrs : list or iterable of ndarrays List of correlation matrices. titles : list of str, optional List of titles for t

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

statsmodels.sandbox.regression.gmm.GMMResults.cov_params GMMResults.cov_params(**kwds) [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.compare_lr_test IVRegressionResults.compare_lr_test(restricted, large_sample=False) Likelihood ratio test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. large_sample : bool Flag indic

statsmodels.nonparametric.bandwidths.select_bandwidth statsmodels.nonparametric.bandwidths.select_bandwidth(x, bw, kernel) [source] Selects bandwidth for a selection rule bw this is a wrapper around existing bandwidth selection rules Parameters: x : array-like Array for which to get the bandwidth bw : string name of bandwidth selection rule, currently supported are: normal_reference, scott, silverman kernel : not used yet Returns: bw : float The estimate of the bandwidth

statsmodels.genmod.generalized_linear_model.GLMResults.deviance static GLMResults.deviance() [source]

statsmodels.discrete.discrete_model.DiscreteResults.wald_test DiscreteResults.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. tuple

statsmodels.tsa.vector_ar.var_model.VARResults.acf VARResults.acf(nlags=None) Compute theoretical autocovariance function Returns: acf : ndarray (p x k x k)

statsmodels.genmod.families.links.Log.deriv Log.deriv(p) [source] Derivative of log transform link function Parameters: p : array-like Mean parameters Returns: g?(p) : array derivative of log transform of x Notes g(x) = 1/x