statsmodels.stats.proportion.proportions_chisquare_pairscontrol statsmodels.stats.proportion.proportions_chisquare_pairscontrol(count, nobs, value=None, multitest_method='hs', alternative='two-sided') [source] chisquare test of proportions for pairs of k samples compared to control Performs a chisquare test for proportions for pairwise comparisons with a control (Dunnet?s test). The control is assumed to be the first element of count and nobs. The alternative is two-sided, larger or smaller.

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.pdf TransfTwo_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: pdf : ndarray Probability density

statsmodels.discrete.discrete_model.NegativeBinomial.fit_regularized NegativeBinomial.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source]

statsmodels.tsa.arima_model.ARMAResults.predict ARMAResults.predict(start=None, end=None, exog=None, dynamic=False) [source] ARMA model in-sample and out-of-sample prediction Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start.

statsmodels.stats.diagnostic.acorr_breush_godfrey statsmodels.stats.diagnostic.acorr_breush_godfrey(results, nlags=None, store=False) Breush Godfrey Lagrange Multiplier tests for residual autocorrelation Parameters: results : Result instance Estimation results for which the residuals are tested for serial correlation nlags : int Number of lags to include in the auxiliary regression. (nlags is highest lag) store : bool If store is true, then an additional class instance that contains in

statsmodels.robust.robust_linear_model.RLMResults.load classmethod RLMResults.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.stats.sandwich_covariance.cov_cluster statsmodels.stats.sandwich_covariance.cov_cluster(results, group, use_correction=True) [source] cluster robust covariance matrix Calculates sandwich covariance matrix for a single cluster, i.e. grouped variables. Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead use_correction : bool If true (default), then the small sample correction factor is used.

statsmodels.tools.numdiff.approx_hess1 statsmodels.tools.numdiff.approx_hess1(x, f, epsilon=None, args=(), kwargs={}, return_grad=False) [source] Calculate Hessian with finite difference derivative approximation Parameters: x : array_like value at which function derivative is evaluated f : function function of one array f(x, *args, **kwargs) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chosen according to EPS**(1/3)*x. args : tuple Ar

statsmodels.regression.linear_model.RegressionResults.scale static RegressionResults.scale() [source]

statsmodels.discrete.discrete_model.ProbitResults.resid_response static ProbitResults.resid_response() The response residuals Notes Response residuals are defined to be where .