statsmodels.genmod.generalized_estimating_equations.GEEResults.plot_isotropic_dependence GEEResults.plot_isotropic_dependence(ax=None, xpoints=10, min_n=50) [source] Create a plot of the pairwise products of within-group residuals against the corresponding time differences. This plot can be used to assess the possible form of an isotropic covariance structure. Parameters: ax : Matplotlib axes instance An axes on which to draw the graph. If None, new figure and axes objects are created xpo

statsmodels.genmod.generalized_estimating_equations.GEEResults.params_sensitivity GEEResults.params_sensitivity(dep_params_first, dep_params_last, num_steps) Refits the GEE model using a sequence of values for the dependence parameters. Parameters: dep_params_first : array-like The first dep_params in the sequence dep_params_last : array-like The last dep_params in the sequence num_steps : int The number of dep_params in the sequence Returns: results : array-like The GEEResults obj

statsmodels.genmod.generalized_estimating_equations.GEEResults.normalized_cov_params GEEResults.normalized_cov_params()

statsmodels.genmod.generalized_estimating_equations.GEEResults.load classmethod GEEResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.genmod.generalized_estimating_equations.GEEResults.initialize GEEResults.initialize(model, params, **kwd)

statsmodels.genmod.generalized_estimating_equations.GEEResults.f_test GEEResults.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 hypothese

statsmodels.genmod.generalized_estimating_equations.GEEResults.cov_params GEEResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column :

statsmodels.genmod.generalized_estimating_equations.GEEResults.conf_int GEEResults.conf_int(alpha=0.05, cols=None, cov_type=None) [source] Returns confidence intervals for the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. i.e., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return cov_type : string The covariance type used for computing standard err

statsmodels.genmod.generalized_estimating_equations.GEEMargins.summary_frame GEEMargins.summary_frame(alpha=0.05) [source] Returns a DataFrame summarizing the marginal effects. Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: frame : DataFrames A DataFrame summarizing the marginal effects.

statsmodels.genmod.generalized_estimating_equations.GEEMargins.summary GEEMargins.summary(alpha=0.05) [source] Returns a summary table for marginal effects Parameters: alpha : float Number between 0 and 1. The confidence intervals have the probability 1-alpha. Returns: Summary : SummaryTable A SummaryTable instance