statsmodels.stats.outliers_influence.MLEInfluence¶
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class
statsmodels.stats.outliers_influence.
MLEInfluence
(results, resid=None, endog=None, exog=None, hat_matrix_diag=None, cov_params=None, scale=None)[source]¶ Local Influence and outlier measures (experimental)
This currently subclasses GLMInfluence instead of the other way. No common superclass yet. This is another version before checking what is common
Parameters: results : instance of results class
This only works for model and results classes that have the necessary helper methods.
other arguments are only to override default behavior and are used instead
of the corresponding attribute of the results class.
By default resid_pearson is used as resid.
Notes
MLEInfluence produces the same results as GLMInfluence (verified for GLM Binomial and Gaussian). There will be some differences for non-canonical links or if a robust cov_type is used.
Warning: This does currently not work for constrained or penalized models, e.g. models estimated with fit_constrained or fit_regularized.
This has not yet been tested for correctness when offset or exposure are used, although they should be supported by the code.
status: experimental, This class will need changes to support different kinds of models, e.g. extra parameters in discrete.NegativeBinomial or two-part models like ZeroInflatedPoisson.
Attributes
d_params
()Change in parameter estimates cooks_distance
()Cook’s distance and p-values resid_studentized
()Score residual divided by sqrt of hessian factor d_fittedvalues
()Change in expected response, fittedvalues d_fittedvalues_scaled
Change in fittedvalues scaled by standard errors params_one
()Parameter estimate based on one-step approximation hat_matrix_diag (hii) (This is the generalized leverage computed as the) local derivative of fittedvalues (predicted mean) with respect to the observed response for each observation. dbetas (change in parameters divided by the standard error of parameters) from the full model results, bse
.Methods
cooks_distance
()Cook’s distance and p-values d_fittedvalues
()Change in expected response, fittedvalues d_params
()Change in parameter estimates dfbetas
()Scaled change in parameter estimates hat_matrix_diag
()Diagonal of the generalized leverage params_one
()Parameter estimate based on one-step approximation plot_index
([y_var, threshold, title, ax, idx])index plot for influence attributes plot_influence
([external, alpha, criterion, …])Plot of influence in regression. resid_studentized
()Score residual divided by sqrt of hessian factor summary_frame
()Creates a DataFrame with influence results.