statsmodels.sandbox.stats.multicomp.TukeyHSDResults.summary TukeyHSDResults.summary() [source] Summary table that can be printed

statsmodels.genmod.generalized_linear_model.GLMResults.conf_int GLMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_inter

statsmodels.stats.power.FTestAnovaPower.solve_power FTestAnovaPower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, k_groups=2) [source] solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, nobs, 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 positive. nobs : int or floa

statsmodels.discrete.discrete_model.MultinomialModel class statsmodels.discrete.discrete_model.MultinomialModel(endog, exog, **kwargs) [source] Methods cdf(X) The cumulative distribution function of the model. cov_params_func_l1(likelihood_model, xopt, ...) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood. fit_regularized([start_p

statsmodels.genmod.cov_struct.Independence.update Independence.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.discrete.discrete_model.BinaryModel class statsmodels.discrete.discrete_model.BinaryModel(endog, exog, **kwargs) [source] Methods cdf(X) The cumulative distribution function of the model. cov_params_func_l1(likelihood_model, xopt, ...) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. fit([start_params, method, maxiter, ...]) Fit the model using maximum likelihood. fit_regularized([start_params, met

statsmodels.genmod.families.links.Logit.deriv2 Logit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.imse KDEMultivariateConditional.imse(bw) [source] The integrated mean square error for the conditional KDE. Parameters: bw: array_like : The bandwidth parameter(s). Returns: CV: float : The cross-validation objective function. Notes For more details see pp. 156-166 in [R15]. For details on how to handle the mixed variable types see [R16]. The formula for the cross-validation objective function for mixed variable type

statsmodels.robust.norms.RamsayE.psi RamsayE.psi(z) [source] The psi function for Ramsay?s Ea estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z*exp(-a*|z|)

statsmodels.sandbox.regression.gmm.GMMResults.get_bse GMMResults.get_bse(**kwds) [source] standard error of the parameter estimates with options Parameters: kwds : optional keywords options for calculating cov_params Returns: bse : ndarray estimated standard error of parameter estimates