statsmodels.sandbox.regression.gmm.IVRegressionResults.fvalue static IVRegressionResults.fvalue() [source]

statsmodels.stats.power.TTestPower.plot_power TTestPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then one cur

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.mean TransfTwo_gen.mean(*args, **kwds) Mean 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: mean : float the mean of the distribution

statsmodels.discrete.discrete_model.LogitResults.resid_response static LogitResults.resid_response() The response residuals Notes Response residuals are defined to be where .

statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd(y, x) [source]

statsmodels.genmod.generalized_linear_model.GLM.score_factor GLM.score_factor(params, scale=None) [source] weights for score for each observation This can be considered as score residuals. Parameters: params : ndarray parameter at which Hessian 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_factor : ndarray_1

statsmodels.genmod.families.links.NegativeBinomial.inverse NegativeBinomial.inverse(z) [source] Inverse of the negative binomial transform Parameters: z : array-like The value of the inverse of the negative binomial link at p. Returns : ??- : p : array Mean parameters Notes g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.median ExpTransf_gen.median(*args, **kwds) Median 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

statsmodels.discrete.discrete_model.CountResults.summary2 CountResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alp

statsmodels.discrete.discrete_model.CountResults.get_margeff CountResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regressor. ?all?