statsmodels.tsa.stattools.grangercausalitytests statsmodels.tsa.stattools.grangercausalitytests(x, maxlag, addconst=True, verbose=True) [source] four tests for granger non causality of 2 timeseries all four tests give similar results params_ftest and ssr_ftest are equivalent based on F test which is identical to lmtest:grangertest in R Parameters: x : array, 2d, (nobs,2) data for test whether the time series in the second column Granger causes the time series in the first column maxlag :

statsmodels.tsa.vector_ar.var_model.VARResults.roots static VARResults.roots() [source]

statsmodels.sandbox.distributions.transformed.LogTransf_gen.median LogTransf_gen.median(*args, **kwds) Median of the distribution. 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

statsmodels.stats.power.TTestPower class statsmodels.stats.power.TTestPower(**kwds) [source] Statistical Power calculations for one sample or paired sample t-test Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha[, df, ...]) Calculate the power of a t-test for one sample or paired samples. solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a one sample t

statsmodels.sandbox.descstats.sign_test statsmodels.sandbox.descstats.sign_test(samp, mu0=0) Signs test. Parameters: samp : array-like 1d array. The sample for which you want to perform the signs test. mu0 : float See Notes for the definition of the sign test. mu0 is 0 by default, but it is common to set it to the median. Returns: M, p-value : See also scipy.stats.wilcoxon Notes The signs test returns M = (N(+) - N(-))/2 where N(+) is the number of values above mu0, N(-) is the num

statsmodels.regression.quantile_regression.QuantRegResults.compare_f_test QuantRegResults.compare_f_test(restricted) use F test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. Returns: f_value : float test statistic, F distributed p_val

statsmodels.genmod.families.family.Gamma.resid_dev Gamma.resid_dev(endog, mu, scale=1.0) [source] Gamma deviance residuals Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional argument to divide the residuals by scale Returns: resid_dev : array Deviance residuals as defined below Notes resid_dev is defined

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.gradient_momcond NonlinearIVGMM.gradient_momcond(params, epsilon=0.0001, centered=True) gradient of moment conditions Parameters: params : ndarray parameter at which the moment conditions are evaluated epsilon : float stepsize for finite difference calculation centered : bool This refers to the finite difference calculation. If centered is true, then the centered finite difference calculation is used. Otherwise the one-sided forward dif

statsmodels.discrete.discrete_model.DiscreteResults class statsmodels.discrete.discrete_model.DiscreteResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for the discrete dependent variable models. Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : fl

statsmodels.discrete.discrete_model.BinaryModel.cov_params_func_l1 BinaryModel.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.