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.genmod.cov_struct.Nested.update Nested.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.sandbox.distributions.transformed.LogTransf_gen.ppf LogTransf_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability 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: x : array_l

statsmodels.sandbox.distributions.extras.NormExpan_gen.fit NormExpan_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 parameters

statsmodels.genmod.cov_struct.Nested.covariance_matrix Nested.covariance_matrix(expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The covariance or correlatio

statsmodels.robust.norms.LeastSquares.rho LeastSquares.rho(z) [source] The least squares estimator rho function Parameters: z : array 1d array Returns: rho : array rho(z) = (1/2.)*z**2

statsmodels.emplike.descriptive.DescStatUV.ci_var DescStatUV.ci_var(lower_bound=None, upper_bound=None, sig=0.05) [source] Returns the confidence interval for the variance. Parameters: lower_bound : float The minimum value the lower confidence interval can take. The p-value from test_var(lower_bound) must be lower than 1 - significance level. Default is .99 confidence limit assuming normality upper_bound : float The maximum value the upper confidence interval can take. The p-value from t

statsmodels.iolib.table.csv2st statsmodels.iolib.table.csv2st(csvfile, headers=False, stubs=False, title=None) [source] Return SimpleTable instance, created from the data in csvfile, which is in comma separated values format. The first row may contain headers: set headers=True. The first column may contain stubs: set stubs=True. Can also supply headers and stubs as tuples of strings.

statsmodels.sandbox.distributions.extras.SkewNorm_gen.logcdf SkewNorm_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_like

statsmodels.regression.linear_model.GLSAR.from_formula classmethod GLSAR.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 args : ext