statsmodels.stats.outliers_influence.MLEInfluence¶
-
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_fittedvalues_scaled
Change in fittedvalues scaled by standard errors 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. d_params (Change in parameters computed with one Newton step using the) full Hessian corrected by division by (1 - hii). dbetas (change in parameters divided by the standard error of parameters) from the full model results, bse
.cooks_distance (quadratic form for change in parameters weighted by) cov_params
from the full model divided by the number of variables. It includes p-values based on the F-distribution which are only approximate outside of linear Gaussian models.resid_studentized (In the general MLE case resid_studentized are) computed from the score residuals scaled by hessian factor and leverage. This does not use cov_params
.d_fittedvalues (local change of expected mean given the change in the) parameters as computed in d_params
.params_one (is the one step parameter estimate computed as params
) from the full sample minusd_params
.Methods
plot_index
([y_var, threshold, title, ax, idx])index plot for influence attributes plot_influence
([external, alpha, criterion, …])Plot of influence in regression. summary_frame
()Creates a DataFrame with influence results. Methods
plot_index
([y_var, threshold, title, ax, idx])index plot for influence attributes plot_influence
([external, alpha, criterion, …])Plot of influence in regression. summary_frame
()Creates a DataFrame with influence results. Properties
cooks_distance
Cook’s distance and p-values d_fittedvalues
Change in expected response, fittedvalues d_fittedvalues_scaled
Change in fittedvalues scaled by standard errors 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 resid_studentized
Score residual divided by sqrt of hessian factor