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

statsmodels.genmod.generalized_linear_model.GLM.initialize GLM.initialize() [source] Initialize a generalized linear model.

statsmodels.stats.outliers_influence.OLSInfluence.det_cov_params_not_obsi static OLSInfluence.det_cov_params_not_obsi() [source] (cached attribute) determinant of cov_params of all LOOO regressions uses results from leave-one-observation-out loop

statsmodels.regression.mixed_linear_model.MixedLMResults class statsmodels.regression.mixed_linear_model.MixedLMResults(model, params, cov_params) [source] Class to contain results of fitting a linear mixed effects model. MixedLMResults inherits from statsmodels.LikelihoodModelResults Parameters: See statsmodels.LikelihoodModelResults : Returns: **Attributes** : model : class instance Pointer to PHreg model instance that called fit. normalized_cov_params : array The sampling covariance

statsmodels.tsa.tsatools.lagmat2ds statsmodels.tsa.tsatools.lagmat2ds(x, maxlag0, maxlagex=None, dropex=0, trim='forward') [source] generate lagmatrix for 2d array, columns arranged by variables Parameters: x : array_like, 2d 2d data, observation in rows and variables in columns maxlag0 : int for first variable all lags from zero to maxlag are included maxlagex : None or int max lag for all other variables all lags from zero to maxlag are included dropex : int (default is 0) exclude