statsmodels.tsa.vector_ar.var_model.VARResults.test_causality VARResults.test_causality(equation, variables, kind='f', signif=0.05, verbose=True) [source] Compute test statistic for null hypothesis of Granger-noncausality, general function to test joint Granger-causality of multiple variables Parameters: equation : string or int Equation to test for causality variables : sequence (of strings or ints) List, tuple, etc. of variables to test for Granger-causality kind : {?f?, ?wald?} Perf

statsmodels.genmod.families.links.inverse_power.deriv inverse_power.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.genmod.families.links.Link.inverse_deriv Link.inverse_deriv(z) [source] Derivative of the inverse link function g^(-1)(z). Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the link function : Notes This reference implementation gives the correct result but it inefficient, so it can be overriden in subclasses.

statsmodels.sandbox.tsa.fftarma.ArmaFft.fftma ArmaFft.fftma(n) [source] Fourier transform of MA polynomial, zero-padded at end to n Parameters: n : int length of array after zero-padding Returns: fftar : ndarray fft of zero-padded ar polynomial

Fitting models using R-style formulas Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. A full description of the formula language can be found in the patsy docs: Patsy formula language description Loading modules and functions import statsmode

statsmodels.genmod.cov_struct.GlobalOddsRatio.initialize GlobalOddsRatio.initialize(model) [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.predict IVRegressionResults.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 p

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

statsmodels.tsa.arima_process.ArmaProcess.acf ArmaProcess.acf(nobs=None) [source] theoretical autocorrelation function of an ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acf Returns: acf : array autocorrelation of ARMA process given by ar, ma See also arma_aco

statsmodels.stats.diagnostic.CompareJ class statsmodels.stats.diagnostic.CompareJ J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached