statsmodels.nonparametric.bandwidths.bw_silverman statsmodels.nonparametric.bandwidths.bw_silverman(x, kernel=None) [source] Silverman?s Rule of Thumb Parameters: x : array-like Array for which to get the bandwidth kernel : CustomKernel object Unused Returns: bw : float The estimate of the bandwidth Notes Returns .9 * A * n ** (-1/5.) where A = min(std(x, ddof=1), IQR/1.349) IQR = np.subtract.reduce(np.percentile(x, [75,25])) References Silverman, B.W. (1986) Density Estimation.

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.GEE.predict GEE.predict(params, exog=None, offset=None, exposure=None, linear=False) [source] Return predicted values for a marginal regression model fit using GEE. Parameters: params : array-like Parameters / coefficients of a marginal regression model. exog : array-like, optional Design / exogenous data. If exog is None, model exog is used. offset : array-like, optional Offset for exog if provided. If offset is None, model offset is

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

statsmodels.robust.robust_linear_model.RLMResults.predict RLMResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data str

statsmodels.robust.scale.HuberScale class statsmodels.robust.scale.HuberScale(d=2.5, tol=1e-08, maxiter=30) [source] Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations

statsmodels.emplike.descriptive.DescStatMV.test_corr DescStatMV.test_corr(corr0, return_weights=0) [source] Returns -2 x log-likelihood ratio and p-value for the correlation coefficient between 2 variables Parameters: corr0 : float Hypothesized value to be tested return_weights : bool If true, returns the weights that maximize the log-likelihood at the hypothesized value

statsmodels.genmod.generalized_estimating_equations.GEEResults.summary GEEResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the GEE regression results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals co

statsmodels.genmod.generalized_linear_model.GLMResults.load classmethod GLMResults.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.tsa.vector_ar.var_model.VARResults.cov_ybar VARResults.cov_ybar() [source] Asymptotically consistent estimate of covariance of the sample mean Notes Lutkepohl Proposition 3.3