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

statsmodels.tsa.vector_ar.var_model.VARResults.fittedvalues static VARResults.fittedvalues() [source] The predicted insample values of the response variables of the model.

statsmodels.regression.linear_model.RegressionResults.condition_number static RegressionResults.condition_number() [source] Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

statsmodels.regression.linear_model.RegressionResults.mse_model static RegressionResults.mse_model() [source]

statsmodels.stats.diagnostic.compare_j statsmodels.stats.diagnostic.compare_j = J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached

statsmodels.regression.quantile_regression.QuantReg.initialize QuantReg.initialize()

Pitfalls This page lists issues which may arise while using statsmodels. These can be the result of data-related or statistical problems, software design, ?non-standard? use of models, or edge cases. statsmodels provides several warnings and helper functions for diagnostic checking (see this blog article for an example of misspecification checks in linear regression). The coverage is of course not comprehensive, but more warnings and diagnostic functions will be added over time. While the under

statsmodels.duration.hazard_regression.PHRegResults.schoenfeld_residuals static PHRegResults.schoenfeld_residuals() [source] A matrix containing the Schoenfeld residuals. Notes Schoenfeld residuals for censored observations are set to zero.

statsmodels.duration.hazard_regression.PHRegResults.martingale_residuals static PHRegResults.martingale_residuals() [source] The martingale residuals.

statsmodels.tsa.vector_ar.var_model.VARProcess.plotsim VARProcess.plotsim(steps=1000) [source] Plot a simulation from the VAR(p) process for the desired number of steps