statsmodels.tsa.vector_ar.var_model.VAR.initialize VAR.initialize() Initialize (possibly re-initialize) a Model instance. For instance, the design matrix of a linear model may change and some things must be recomputed.

statsmodels.tsa.vector_ar.var_model.VAR.information VAR.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.tsa.vector_ar.var_model.VAR.hessian VAR.hessian(params) The Hessian matrix of the model

statsmodels.tsa.vector_ar.var_model.VAR.from_formula classmethod VAR.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.tsa.vector_ar.var_model.VAR.fit VAR.fit(maxlags=None, method='ols', ic=None, trend='c', verbose=False) [source] Fit the VAR model Parameters: maxlags : int Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4), see select_order function method : {?ols?} Estimation method to use ic : {?aic?, ?fpe?, ?hqic?, ?bic?, None} Information criterion to use for VAR order selection. aic : Akaike fpe : Final prediction error hqic : Hannan-Quinn bic :

statsmodels.sandbox.stats.multicomp.TukeyHSDResults.summary TukeyHSDResults.summary() [source] Summary table that can be printed

statsmodels.sandbox.stats.multicomp.TukeyHSDResults.plot_simultaneous TukeyHSDResults.plot_simultaneous(comparison_name=None, ax=None, figsize=(10, 6), xlabel=None, ylabel=None) [source] Plot a universal confidence interval of each group mean Visiualize significant differences in a plot with one confidence interval per group instead of all pairwise confidence intervals. Parameters: comparison_name : string, optional if provided, plot_intervals will color code all groups that are significan

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.robust.norms.TukeyBiweight.rho TukeyBiweight.rho(z) [source] The robust criterion function for Tukey?s biweight estimator Parameters: z : array-like 1d array Returns: rho : array rho(z) = -(1 - (z/c)**2)**3 * c**2/6. for |z| <= R rho(z) = 0 for |z| > R

statsmodels.robust.norms.TukeyBiweight.psi_deriv TukeyBiweight.psi_deriv(z) [source] The derivative of Tukey?s biweight psi function Notes Used to estimate the robust covariance matrix.