statsmodels.stats.outliers_influence.GLMInfluence¶

class statsmodels.stats.outliers_influence.GLMInfluence(results, resid=None, endog=None, exog=None, hat_matrix_diag=None, cov_params=None, scale=None)[source]¶

Influence and outlier measures (experimental)

This uses partly formulas specific to GLM, specifically cooks_distance is based on the hessian, i.e. observed or expected information matrix and not on cov_params, in contrast to MLEInfluence. Standardization for changes in parameters, in fittedvalues and in the linear predictor are based on cov_params.

Parameters:¶

results : instance of results class¶: This only works for model and results classes that have the necessary helper methods.
instead : other arguments are only to override default behavior and are used
class. : of the corresponding attribute of the results
resid. : By default resid_pearson is used as

dbetas¶: change in parameters divided by the standard error of parameters from the full model results, bse.

d_fittedvalues_scaled¶: same as d_fittedvalues but scaled by the standard errors of a predicted mean of the response.

d_linpred¶: local change in linear prediction.

d_linpred_scale¶: local change in linear prediction scaled by the standard errors for the prediction based on cov_params.

Notes

This has not yet been tested for correctness when offset or exposure are used, although they should be supported by the code.

Some GLM specific measures like d_deviance are still missing.

Computing an explicit leave-one-observation-out (LOOO) loop is included but no influence measures are currently computed from it.

Methods

`plot_index`([y_var, threshold, title, ax, idx])	index plot for influence attributes
`plot_influence`([external, alpha, criterion, ...])	Plot of influence in regression.
`resid_score`([joint, index, studentize])	Score observations scaled by inverse hessian.
`resid_score_factor`()	Score residual divided by sqrt of hessian factor.
`summary_frame`()	Creates a DataFrame with influence results.

Properties

`cooks_distance`	Cook's distance
`d_fittedvalues`	Change in expected response, fittedvalues.
`d_fittedvalues_scaled`	Change in fittedvalues scaled by standard errors.
`d_linpred`	Change in linear prediction
`d_linpred_scaled`	Change in linpred scaled by standard errors
`d_params`	Change in parameter estimates
`dfbetas`	Scaled change in parameter estimates.
`hat_matrix_diag`	Diagonal of the hat_matrix for GLM
`hat_matrix_exog_diag`	Diagonal of the hat_matrix using only exog as in OLS
`params_one`	Parameter estimate based on one-step approximation.
`resid_studentized`	Internally studentized pearson residuals