statsmodels.genmod.families.links.CDFLink.deriv CDFLink.deriv(p) [source] Derivative of CDF link Parameters: p : array-like mean parameters Returns: g?(p) : array The derivative of CDF transform at p Notes g?(p) = 1./ dbn.pdf(dbn.ppf(p))

statsmodels.genmod.cov_struct.CovStruct.initialize CovStruct.initialize(model) [source] Called by GEE, used by implementations that need additional setup prior to running fit. Parameters: model : GEE class A reference to the parent GEE class instance.

statsmodels.tools.numdiff.approx_fprime_cs statsmodels.tools.numdiff.approx_fprime_cs(x, f, epsilon=None, args=(), kwargs={}) [source] Calculate gradient or Jacobian with complex step derivative approximation Parameters: x : array parameters at which the derivative is evaluated f : function f(*((x,)+args), **kwargs) returning either one value or 1d array epsilon : float, optional Stepsize, if None, optimal stepsize is used. Optimal step-size is EPS*x. See note. args : tuple Tuple of

statsmodels.regression.linear_model.RegressionResults.scale static RegressionResults.scale() [source]

statsmodels.discrete.discrete_model.NegativeBinomialResults.t_test NegativeBinomialResults.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 example

statsmodels.regression.linear_model.OLSResults.ess static OLSResults.ess()

statsmodels.emplike.descriptive.DescStatUV.ci_skew DescStatUV.ci_skew(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence interval for skewness. Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value of skewness the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of skewness the lower limit can be. Default is .99 confidence level assuming normality. Returns:

statsmodels.emplike.descriptive.DescStatUV.test_skew DescStatUV.test_skew(skew0, return_weights=False) [source] Returns -2 x log-likelihood and p-value for the hypothesized skewness. Parameters: skew0 : float Skewness value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple The log-likelihood ratio and p_value of skew0

statsmodels.tsa.vector_ar.var_model.VARResults.cov_ybar VARResults.cov_ybar() [source] Asymptotically consistent estimate of covariance of the sample mean Notes Lutkepohl Proposition 3.3

statsmodels.genmod.generalized_linear_model.GLM.information GLM.information(params, scale=None) [source] Fisher information matrix.