statsmodels.sandbox.stats.multicomp.compare_ordered statsmodels.sandbox.stats.multicomp.compare_ordered(vals, alpha) [source] simple ordered sequential comparison of means vals : array_like means or rankmeans for independent groups incomplete, no return, not used yet

statsmodels.regression.mixed_linear_model.MixedLMResults.bse static MixedLMResults.bse()

statsmodels.genmod.families.family.InverseGaussian.resid_dev InverseGaussian.resid_dev(endog, mu, scale=1.0) [source] Returns the deviance residuals for the inverse Gaussian family. 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 dev_resid = sign(endog-mu)*sqrt((endog-mu)**2/(en

statsmodels.sandbox.regression.gmm.IVGMMResults class statsmodels.sandbox.regression.gmm.IVGMMResults(*args, **kwds) [source] Methods calc_cov_params(moms, gradmoms[, weights, ...]) calculate covariance of parameter estimates compare_j(other) overidentification test for comparing two nested gmm estimates conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params(**kwds) f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint lin

statsmodels.genmod.cov_struct.CovStruct.covariance_matrix_solve CovStruct.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each observatio

statsmodels.tsa.varma_process.VarmaPoly class statsmodels.tsa.varma_process.VarmaPoly(ar, ma=None) [source] class to keep track of Varma polynomial format Examples ar23 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.6, 0. ], [ 0.2, -0.6]], [[-0.1, 0. ], [ 0.1, -0.1]]]) ma22 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[ 0.4, 0. ], [ 0.2, 0.3]]]) Methods getisinvertible([a]) check whether the auto-regressive lag-polynomial is stationary getisstationary([a]) check whether the auto-regressive l

statsmodels.tools.eval_measures.stde statsmodels.tools.eval_measures.stde(x1, x2, ddof=0, axis=0) [source] standard deviation of error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: stde : ndarray or float standard deviation of difference along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarr

statsmodels.tools.eval_measures.hqic_sigma statsmodels.tools.eval_measures.hqic_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Hannan-Quinn information criterion (HQC) 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: hqic

statsmodels.robust.norms.TrimmedMean.psi TrimmedMean.psi(z) [source] The psi function for least trimmed mean The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z for |z| <= c psi(z) = 0 for |z| > c

statsmodels.robust.norms.TrimmedMean.weights TrimmedMean.weights(z) [source] Least trimmed mean weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = 1 for |z| <= c weights(z) = 0 for |z| > c