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 minus d_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