statsmodels.sandbox.stats.runs.Runs class statsmodels.sandbox.stats.runs.Runs(x) [source] class for runs in a binary sequence Parameters: x : array_like, 1d data array, Notes This was written as a more general class for runs. This has some redundant calculations when only the runs_test is used. TODO: make it lazy The runs test could be generalized to more than 1d if there is a use case for it. This should be extended once I figure out what the distribution of runs of any length k is. The

statsmodels.sandbox.distributions.transformed.LogTransf_gen.stats LogTransf_gen.stats(*args, **kwds) Some statistics of the given RV 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which mo

statsmodels.genmod.families.family.NegativeBinomial.deviance NegativeBinomial.deviance(endog, mu, scale=1.0) [source] Returns the value of the deviance function. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float Deviance function as defined below Notes deviance = sum(piecewise) where piecewise is defined as If : If :

statsmodels.sandbox.distributions.transformed.LogTransf_gen.fit LogTransf_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape param

statsmodels.robust.norms.RamsayE class statsmodels.robust.norms.RamsayE(a=0.3) [source] Ramsay?s Ea for M estimation. Parameters: a : float, optional The tuning constant for Ramsay?s Ea function. The default value is 0.3. See also statsmodels.robust.norms.RobustNorm Methods psi(z) The psi function for Ramsay?s Ea estimator psi_deriv(z) The derivative of Ramsay?s Ea psi function. rho(z) The robust criterion function for Ramsay?s Ea. weights(z) Ramsay?s Ea weighting function for the

statsmodels.stats.diagnostic.recursive_olsresiduals statsmodels.stats.diagnostic.recursive_olsresiduals(olsresults, skip=None, lamda=0.0, alpha=0.95) calculate recursive ols with residuals and cusum test statistic Parameters: olsresults : instance of RegressionResults uses only endog and exog skip : int or None number of observations to use for initial OLS, if None then skip is set equal to the number of regressors (columns in exog) lamda : float weight for Ridge correction to initial

statsmodels.discrete.discrete_model.MNLogit.initialize MNLogit.initialize() Preprocesses the data for MNLogit.

statsmodels.discrete.discrete_model.Poisson.jac Poisson.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Poisson model Jacobian of the log-likelihood for each observation Parameters: params : array-like The parameters of the model Returns: score : ndarray (nobs, k_vars) The score vector of the model evaluated at params Notes for observations where the loglinear model is assumed

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.logcdf ExpTransf_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution function at x of the given RV. Parameters: x : array_like quantiles 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: logcdf : array

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_centered_split static GEEResults.resid_centered_split() [source] Returns the residuals centered within each group. The residuals are returned as a list of arrays containing the centered residuals for each cluster.