statsmodels.sandbox.regression.gmm.IVGMM.predict IVGMM.predict(params, exog=None) [source]

statsmodels.sandbox.distributions.transformed.Transf_gen.isf Transf_gen.isf(q, *args, **kwds) Inverse survival function at q of the given RV. Parameters: q : array_like upper tail probability 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: x : ndarray or scalar Quantile

statsmodels.discrete.discrete_model.ProbitResults.t_test ProbitResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple o

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.nnlf SkewNorm2_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.sandbox.regression.gmm.IVGMMResults.f_test IVGMMResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can

statsmodels.tsa.vector_ar.var_model.VARResults.irf_resim VARResults.irf_resim(orth=False, repl=1000, T=10, seed=None, burn=100, cum=False) [source] Simulates impulse response function, returning an array of simulations. Used for Sims-Zha error band calculation. Parameters: orth: bool, default False : Compute orthoganalized impulse response error bands repl: int : number of Monte Carlo replications to perform T: int, default 10 : number of impulse response periods signif: float (0 <

statsmodels.tools.eval_measures.aic_sigma statsmodels.tools.eval_measures.aic_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Akaike information criterion Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant Returns: aic : float inform

statsmodels.genmod.families.links.inverse_power.inverse_deriv inverse_power.inverse_deriv(z) Derivative of the inverse of the power transform 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 power transform : function :

statsmodels.sandbox.tsa.movstat.movmean statsmodels.sandbox.tsa.movstat.movmean(x, windowsize=3, lag='lagged') [source] moving window mean Parameters: x : array time series data windsize : int window size lag : ?lagged?, ?centered?, or ?leading? location of window relative to current position Returns: mk : array moving mean, with same shape as x Notes for leading and lagging the data array x is extended by the closest value of the array

statsmodels.sandbox.stats.multicomp.StepDown.get_distance_matrix StepDown.get_distance_matrix() [source] studentized range statistic