statsmodels.regression.linear_model.WLS.from_formula classmethod WLS.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 : extra a

statsmodels.genmod.families.family.Family.resid_dev Family.resid_dev(endog, mu, scale=1.0) [source] The deviance residuals Parameters: endog : array The endogenous response variable mu : array The inverse of the link function at the linear predicted values. scale : float, optional An optional argument to divide the residuals by scale Returns: Deviance residuals. : Notes The deviance residuals are defined for each family.

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.tsa.vector_ar.var_model.VARResults.mse VARResults.mse(steps) Compute theoretical forecast error variance matrices Parameters: steps : int Number of steps ahead Returns: forc_covs : ndarray (steps x neqs x neqs) Notes

statsmodels.sandbox.regression.gmm.GMM.from_formula classmethod GMM.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 : extra ar

statsmodels.discrete.discrete_model.CountResults.save CountResults.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 remove

statsmodels.stats.power.TTestIndPower.solve_power TTestIndPower.solve_power(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='two-sided') [source] solve for any one parameter of the power of a two sample t-test for t-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be None, all others need numeric values Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation.

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast_interval VARProcess.forecast_interval(y, steps, alpha=0.05) [source] Construct forecast interval estimates assuming the y are Gaussian Returns: (lower, mid, upper) : (ndarray, ndarray, ndarray) Notes Lutkepohl pp. 39-40

statsmodels.stats.outliers_influence.OLSInfluence.cooks_distance static OLSInfluence.cooks_distance() [source] (cached attribute) Cooks distance uses original results, no nobs loop

statsmodels.sandbox.distributions.extras.NormExpan_gen.pdf NormExpan_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 funct