statsmodels.sandbox.stats.runs.Runs class statsmodels.sandbox.stats.runs.Runs(x) [source] class for runs in a binary sequence Parameters: x : array_like, 1d data array, Notes This was written as a more general class for runs. This has some redundant calculations when only the runs_test is used. TODO: make it lazy The runs test could be generalized to more than 1d if there is a use case for it. This should be extended once I figure out what the distribution of runs of any length k is. The

statsmodels.genmod.families.links.CLogLog.inverse CLogLog.inverse(z) [source] Inverse of C-Log-Log transform link function Parameters: z : array-like The value of the inverse of the CLogLog link function at p Returns: p : array Mean parameters Notes g^(-1)(z) = 1-exp(-exp(z))

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

statsmodels.sandbox.regression.gmm.LinearIVGMM.gmmobjective_cu LinearIVGMM.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.discrete.discrete_model.MultinomialResults.get_margeff MultinomialResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regr

statsmodels.discrete.discrete_model.DiscreteResults.save DiscreteResults.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

statsmodels.genmod.generalized_linear_model.GLM.from_formula classmethod GLM.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :

statsmodels.miscmodels.count.PoissonGMLE.predict PoissonGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.tsa.stattools.pacf_yw statsmodels.tsa.stattools.pacf_yw(x, nlags=40, method='unbiased') [source] Partial autocorrelation estimated with non-recursive yule_walker Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : ?unbiased? (default) or ?mle? method for the autocovariance calculations in yule walker Returns: pacf : 1d array partial autocorrelations, maxlag+1 elements Notes This

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.imse KDEMultivariateConditional.imse(bw) [source] The integrated mean square error for the conditional KDE. Parameters: bw: array_like : The bandwidth parameter(s). Returns: CV: float : The cross-validation objective function. Notes For more details see pp. 156-166 in [R15]. For details on how to handle the mixed variable types see [R16]. The formula for the cross-validation objective function for mixed variable type