statsmodels.sandbox.sysreg.SUR.predict SUR.predict(design) [source]

statsmodels.sandbox.sysreg.SUR.initialize SUR.initialize() [source]

statsmodels.sandbox.sysreg.SUR.fit SUR.fit(igls=False, tol=1e-05, maxiter=100) [source] igls : bool Iterate until estimates converge if sigma is None instead of two-step GLS, which is the default is sigma is None. tol : float maxiter : int Notes This ia naive implementation that does not exploit the block diagonal structure. It should work for ill-conditioned sigma but this is untested.

statsmodels.iolib.summary.Summary.as_text Summary.as_text() [source] return tables as string Returns: txt : string summary tables and extra text as one string

statsmodels.iolib.summary2.Summary.as_text Summary.as_text() [source] Generate ASCII Summary Table

statsmodels.iolib.summary2.Summary.as_latex Summary.as_latex() [source] Generate LaTeX Summary Table

statsmodels.iolib.summary.Summary.as_latex Summary.as_latex() [source] return tables as string Returns: latex : string summary tables and extra text as string of Latex Notes This currently merges tables with different number of columns. It is recommended to use as_latex_tabular directly on the individual tables.

statsmodels.iolib.summary2.Summary.as_html Summary.as_html() [source] Generate HTML Summary Table

statsmodels.iolib.summary.Summary.as_html Summary.as_html() [source] return tables as string Returns: html : string concatenated summary tables in HTML format

statsmodels.iolib.summary.Summary.as_csv Summary.as_csv() [source] return tables as string Returns: csv : string concatenated summary tables in comma delimited format