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

statsmodels.discrete.discrete_model.CountResults.bse static CountResults.bse()

statsmodels.discrete.discrete_model.LogitResults.llnull static LogitResults.llnull()

statsmodels.graphics.regressionplots.influence_plot statsmodels.graphics.regressionplots.influence_plot(results, external=True, alpha=0.05, criterion='cooks', size=48, plot_alpha=0.75, ax=None, **kwargs) [source] Plot of influence in regression. Plots studentized resids vs. leverage. Parameters: results : results instance A fitted model. external : bool Whether to use externally or internally studentized residuals. It is recommended to leave external as True. alpha : float The alpha va

statsmodels.sandbox.distributions.transformed.SquareFunc.derivplus SquareFunc.derivplus(x) [source]

Input-Output iolib statsmodels offers some functions for input and output. These include a reader for STATA files, a class for generating tables for printing in several formats and two helper functions for pickling. Users can also leverage the powerful input/output functions provided by pandas.io. Among other things, pandas (a statsmodels dependency) allows reading and writing to Excel, CSV, and HDF5 (PyTables). Examples SimpleTable: Basic example Module Reference foreign.StataReader(f

statsmodels.genmod.families.links.Power.inverse_deriv Power.inverse_deriv(z) [source] Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :

statsmodels.genmod.generalized_linear_model.GLM.information GLM.information(params, scale=None) [source] Fisher information matrix.

statsmodels.genmod.generalized_estimating_equations.GEEResults.conf_int GEEResults.conf_int(alpha=0.05, cols=None, cov_type=None) [source] Returns confidence intervals for the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. i.e., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return cov_type : string The covariance type used for computing standard err

statsmodels.genmod.cov_struct.CovStruct.covariance_matrix_solve CovStruct.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each observatio