statsmodels.miscmodels.count.PoissonZiGMLE.jac PoissonZiGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_centered static GEEResults.resid_centered() [source] Returns the residuals centered within each group.

statsmodels.genmod.generalized_estimating_equations.GEEResults.resid_split static GEEResults.resid_split() [source] Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster.

statsmodels.genmod.generalized_estimating_equations.GEEResults.remove_data GEEResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

statsmodels.tsa.ar_model.ARResults.initialize ARResults.initialize(model, params, **kwd)

statsmodels.regression.linear_model.RegressionResults.HC0_se static RegressionResults.HC0_se() [source] See statsmodels.RegressionResults

statsmodels.stats.moment_helpers.cov2corr statsmodels.stats.moment_helpers.cov2corr(cov, return_std=False) [source] convert covariance matrix to correlation matrix Parameters: cov : array_like, 2d covariance matrix, see Notes Returns: corr : ndarray (subclass) correlation matrix return_std : bool If this is true then the standard deviation is also returned. By default only the correlation matrix is returned. Notes This function does not convert subclasses of ndarrays. This requires

statsmodels.sandbox.distributions.extras.ACSkewT_gen.moment ACSkewT_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.regression.linear_model.OLS.initialize OLS.initialize()

statsmodels.miscmodels.tmodel.TLinearModel.from_formula classmethod TLinearModel.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame ar