statsmodels.tsa.arima_process.ArmaProcess.from_estimation classmethod ArmaProcess.from_estimation(model_results, nobs=None) [source] Create ArmaProcess instance from ARMA estimation results Parameters: model_results : ARMAResults instance A fitted model nobs : int, optional If None, nobs is taken from the results

statsmodels.robust.norms.LeastSquares.weights LeastSquares.weights(z) [source] The least squares estimator weighting function for the IRLS algorithm. The psi function scaled by the input z Parameters: z : array-like 1d array Returns: weights : array weights(z) = np.ones(z.shape)

statsmodels.sandbox.regression.gmm.IVRegressionResults.summary IVRegressionResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Retu

statsmodels.robust.robust_linear_model.RLMResults.pvalues static RLMResults.pvalues() [source]

statsmodels.stats.weightstats.DescrStatsW.cov static DescrStatsW.cov() [source] weighted covariance of data if data is 2 dimensional assumes variables in columns and observations in rows uses default ddof

statsmodels.tsa.stattools.pacf_yw statsmodels.tsa.stattools.pacf_yw(x, nlags=40, method='unbiased') [source] Partial autocorrelation estimated with non-recursive yule_walker Parameters: x : 1d array observations of time series for which pacf is calculated nlags : int largest lag for which pacf is returned method : ?unbiased? (default) or ?mle? method for the autocovariance calculations in yule walker Returns: pacf : 1d array partial autocorrelations, maxlag+1 elements Notes This

statsmodels.discrete.discrete_model.ProbitResults.save ProbitResults.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 remo

statsmodels.discrete.discrete_model.ProbitResults.remove_data ProbitResults.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 an attribu

statsmodels.genmod.families.links.Logit class statsmodels.genmod.families.links.Logit [source] The logit transform Notes call and derivative use a private method _clean to make trim p by machine epsilon so that p is in (0,1) Alias of Logit: logit = Logit() Methods deriv(p) Derivative of the logit transform deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of the logit transform inverse_deriv(z) Derivative of the inverse of the logit transform

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.interval TransfTwo_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parame