statsmodels.stats.sandwich_covariance.cov_hc3 statsmodels.stats.sandwich_covariance.cov_hc3(results) [source] See statsmodels.RegressionResults

statsmodels.sandbox.distributions.transformed.LogTransf_gen.std LogTransf_gen.std(*args, **kwds) Standard deviation 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: std : float standard deviation of the distribution

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.fit_loc_scale ExpTransf_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.stats.power.FTestPower.solve_power FTestPower.solve_power(effect_size=None, df_num=None, df_denom=None, nobs=None, alpha=None, power=None, ncc=1) [source] solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, df_num, df_denom, alpha, power Exactly one needs to be None, all others need numeric values. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be posi

statsmodels.sandbox.distributions.transformed.Transf_gen.expect Transf_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one argume

statsmodels.regression.mixed_linear_model.MixedLMResults.f_test MixedLMResults.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

statsmodels.nonparametric.kernel_density.EstimatorSettings class statsmodels.nonparametric.kernel_density.EstimatorSettings(efficient=False, randomize=False, n_res=25, n_sub=50, return_median=True, return_only_bw=False, n_jobs=-1) Object to specify settings for density estimation or regression. EstimatorSettings has several proporties related to how bandwidth estimation for the KDEMultivariate, KDEMultivariateConditional, KernelReg and CensoredKernelReg classes behaves. Parameters: efficien

statsmodels.tsa.stattools.periodogram statsmodels.tsa.stattools.periodogram(X) [source] Returns the periodogram for the natural frequency of X Parameters: X : array-like Array for which the periodogram is desired. Returns: pgram : array 1./len(X) * np.abs(np.fft.fft(X))**2 References Brockwell and Davis.

statsmodels.discrete.discrete_model.Poisson.predict Poisson.predict(params, exog=None, exposure=None, offset=None, linear=False) Predict response variable of a count model given exogenous variables. Notes If exposure is specified, then it will be logged by the method. The user does not need to log it first.

statsmodels.sandbox.stats.multicomp.set_partition statsmodels.sandbox.stats.multicomp.set_partition(ssli) [source] extract a partition from a list of tuples this should be correctly called select largest disjoint sets. Begun and Gabriel 1981 don?t seem to be bothered by sets of accepted hypothesis with joint elements, e.g. maximal_accepted_sets = { {1,2,3}, {2,3,4} } This creates a set partition from a list of sets given as tuples. It tries to find the partition with the largest sets. That i