statsmodels.sandbox.regression.gmm.GMMResults.save GMMResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_data

statsmodels.genmod.families.family.NegativeBinomial.starting_mu NegativeBinomial.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

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.discrete.discrete_model.Poisson.fit_constrained Poisson.fit_constrained(constraints, start_params=None, **fit_kwds) [source] fit the model subject to linear equality constraints The constraints are of the form R params = q where R is the constraint_matrix and q is the vector of constraint_values. The estimation creates a new model with transformed design matrix, exog, and converts the results back to the original parameterization. Parameters: constraints : formula expression or

statsmodels.regression.linear_model.RegressionResults class statsmodels.regression.linear_model.RegressionResults(model, params, normalized_cov_params=None, scale=1.0, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] This class summarizes the fit of a linear regression model. It handles the output of contrasts, estimates of covariance, etc. Returns: **Attributes** : aic : Aikake?s information criteria. For a model with a constant . For a model without a constant . bic : Bayes? i

statsmodels.genmod.families.links.identity.deriv identity.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.regression.linear_model.GLS.fit GLS.fit(method='pinv', cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) Full fit of the model. The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale. Parameters: method : str Can be ?pinv?, ?qr?. ?pinv? uses the Moore-Penrose pseudoinverse to solve the least squares problem. ?qr? uses the QR factorization. Returns: A RegressionResults class instance. : See also regression.RegressionResu

statsmodels.genmod.generalized_linear_model.GLM.fit GLM.fit(start_params=None, maxiter=100, method='IRLS', tol=1e-08, scale=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) [source] Fits a generalized linear model for a given family. Parameters: maxiter : int, optional Default is 100. method : string Default is ?IRLS? for iteratively reweighted least squares. This is currently the only method available for GLM fit. scale : string or float, optional scale can be ?X2?, ?d

statsmodels.tsa.ar_model.ARResults.predict ARResults.predict(start=None, end=None, dynamic=False) [source] Returns in-sample and out-of-sample prediction. Parameters: start : int, str, or datetime Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. end : int, str, or datetime Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date s

statsmodels.discrete.discrete_model.MultinomialResults.llr static MultinomialResults.llr()