statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.geterrors classmethod KalmanFilter.geterrors(y, k, k_ar, k_ma, k_lags, nobs, Z_mat, m, R_mat, T_mat, paramsdtype) [source] Returns just the errors of the Kalman Filter

statsmodels.discrete.discrete_model.MNLogit.information MNLogit.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.sandbox.regression.gmm.GMMResults.load classmethod GMMResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.discrete.discrete_model.DiscreteModel.fit DiscreteModel.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str

statsmodels.miscmodels.count.PoissonGMLE.score PoissonGMLE.score(params) Gradient of log-likelihood evaluated at params

statsmodels.miscmodels.count.PoissonGMLE.reduceparams PoissonGMLE.reduceparams(params)

statsmodels.tsa.ar_model.ARResults.fittedvalues static ARResults.fittedvalues() [source]

statsmodels.tools.tools.add_constant statsmodels.tools.tools.add_constant(data, prepend=True, has_constant='skip') [source] This appends a column of ones to an array if prepend==False. Parameters: data : array-like data is the column-ordered design matrix prepend : bool True and the constant is prepended rather than appended. has_constant : str {?raise?, ?add?, ?skip?} Behavior if ``data?? already has a constant. The default will return data without adding another constant. If ?raise?,

statsmodels.discrete.discrete_model.NegativeBinomialResults class statsmodels.discrete.discrete_model.NegativeBinomialResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for NegativeBinomial 1 and 2 Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : f

statsmodels.discrete.discrete_model.ProbitResults.prsquared static ProbitResults.prsquared()