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
 resultsinstance 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 noncanonical 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 twopart models like ZeroInflatedPoisson.
 Attributes
 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 theChange in parameter estimates
 dbetaschange in parameters divided by the standard error of parameters
from the full model results,
bse
.cooks_distance
quadratic form for change in parameters weighted byCook’s distance and pvalues
resid_studentized
In the general MLE case resid_studentized areScore residual divided by sqrt of hessian factor
d_fittedvalues
local change of expected mean given the change in theChange in expected response, fittedvalues
d_fittedvalues_scaled
same as d_fittedvalues but scaled by the standardChange in fittedvalues scaled by standard errors
params_one
is the one step parameter estimate computed asparams
Parameter estimate based on onestep approximation
Methods
Cook’s distance and pvalues
Change in expected response, fittedvalues
d_params
()Change in parameter estimates
dfbetas
()Scaled change in parameter estimates
Diagonal of the generalized leverage
Parameter estimate based on onestep approximation
plot_index
([y_var, threshold, title, ax, idx])index plot for influence attributes
plot_influence
([external, alpha, criterion, …])Plot of influence in regression.
Score residual divided by sqrt of hessian factor
Creates a DataFrame with influence results.