statsmodels.stats.diagnostic.recursive_olsresiduals statsmodels.stats.diagnostic.recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95) calculate recursive ols with residuals and cusum test statistic Parameters: olsresults : instance of RegressionResults uses only endog and exog skip : int or None number of observations to use for initial OLS, if None then skip is set equal to the number of regressors (columns in exog) lamda : float weight for Ridge correction to initial

statsmodels.stats.outliers_influence.OLSInfluence.resid_studentized_external static OLSInfluence.resid_studentized_external() [source] (cached attribute) studentized residuals using LOOO variance this uses sigma from leave-one-out estimates requires leave one out loop for observations

statsmodels.regression.quantile_regression.QuantReg.information QuantReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.distributions.extras.SkewNorm_gen.ppf SkewNorm_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability 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: x : array_like qu

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.ppf TransfTwo_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability 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: x : array_l

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

statsmodels.regression.linear_model.OLSResults.condition_number static OLSResults.condition_number() Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

statsmodels.sandbox.distributions.extras.SkewNorm_gen.rvs SkewNorm_gen.rvs(*args, **kwds) Random variates of given type. Parameters: 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). size : int or tuple of ints, optional Defining number of random variates (default=1). Returns: r

statsmodels.regression.linear_model.OLSResults.rsquared static OLSResults.rsquared()

statsmodels.regression.mixed_linear_model.MixedLMResults.predict MixedLMResults.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