statsmodels.stats.power.TTestPower.solve_power TTestPower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, alternative='two-sided') [source] solve for any one parameter of the power of a one sample t-test for the one sample t-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of p

statsmodels.genmod.generalized_linear_model.GLM.score GLM.score(params, scale=None) [source] score, first derivative of the loglikelihood function Parameters: params : ndarray parameter at which score is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. Returns: score : ndarray_1d The first derivative of the loglikelihood functi

statsmodels.duration.hazard_regression.PHRegResults.llf static PHRegResults.llf()

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fitstart NonlinearIVGMM.fitstart() [source]

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.var TransfTwo_gen.var(*args, **kwds) Variance 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=0) scale : array_like, optional scale parameter (default=1) Returns: var : float the variance of the distribution

statsmodels.sandbox.distributions.extras.SkewNorm_gen.fit SkewNorm_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape parameters)

statsmodels.tsa.arima_model.ARMA class statsmodels.tsa.arima_model.ARMA(endog, order, exog=None, dates=None, freq=None, missing='none') [source] Autoregressive Moving Average ARMA(p,q) Model Parameters: endog : array-like The endogenous variable. order : iterable The (p,q) order of the model for the number of AR parameters, differences, and MA parameters to use. exog : array-like, optional An optional arry of exogenous variables. This should not include a constant or trend. You can spe

statsmodels.tsa.arima_model.ARMAResults.wald_test ARMAResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 be given as a string. See the examples. tuple : A tuple of arr

statsmodels.discrete.discrete_model.DiscreteResults.normalized_cov_params DiscreteResults.normalized_cov_params()

statsmodels.regression.linear_model.GLSAR.information GLSAR.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.