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

statsmodels.genmod.generalized_estimating_equations.GEEResults.predict GEEResults.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 pa

statsmodels.sandbox.stats.multicomp.maxzero statsmodels.sandbox.stats.multicomp.maxzero(x) [source] find all up zero crossings and return the index of the highest Not used anymore >>> np.random.seed(12345) >>> x = np.random.randn(8) >>> x array([-0.20470766, 0.47894334, -0.51943872, -0.5557303 , 1.96578057, 1.39340583, 0.09290788, 0.28174615]) >>> maxzero(x) (4, array([1, 4])) no up-zero-crossing at end >>> np.random.seed(0) >>&

Empirical Likelihood emplike Introduction Empirical likelihood is a method of nonparametric inference and estimation that lifts the obligation of having to specify a family of underlying distributions. Moreover, empirical likelihood methods do not require re-sampling but still uniquely determine confidence regions whose shape mirrors the shape of the data. In essence, empirical likelihood attempts to combine the benefits of parametric and nonparametric methods while limiting their shortcomings

statsmodels.stats.multicomp.pairwise_tukeyhsd statsmodels.stats.multicomp.pairwise_tukeyhsd(endog, groups, alpha=0.05) [source] calculate all pairwise comparisons with TukeyHSD confidence intervals this is just a wrapper around tukeyhsd method of MultiComparison Parameters: endog : ndarray, float, 1d response variable groups : ndarray, 1d array with groups, can be string or integers alpha : float significance level for the test Returns: results : TukeyHSDResults instance A results

Patsy: Contrast Coding Systems for categorical variables Note This document is based heavily on this excellent resource from UCLA. A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these variables amounts to testing whether the mean for that level is statistically significantly different from the mean of the base category. This dummy codin

statsmodels.sandbox.regression.gmm.IVGMM.gmmobjective_cu IVGMM.gmmobjective_cu(params, weights_method='cov', wargs=()) objective function for continuously updating GMM minimization Parameters: params : array parameter values at which objective is evaluated Returns: jval : float value of objective function

statsmodels.robust.norms.TukeyBiweight.weights TukeyBiweight.weights(z) [source] Tukey?s biweight weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array psi(z) = (1 - (z/c)**2)**2 for |z| <= R psi(z) = 0 for |z| > R

statsmodels.iolib.summary2.Summary.add_df Summary.add_df(df, index=True, header=True, float_format='%.4f', align='r') [source] Add the contents of a DataFrame to summary table Parameters: df : DataFrame header: bool : Reproduce the DataFrame column labels in summary table index: bool : Reproduce the DataFrame row labels in summary table float_format: string : Formatting to float data columns align : string Data alignment (l/c/r)

statsmodels.genmod.generalized_linear_model.GLMResults.save GLMResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If re