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.
- 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.
- results
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.
- Attributes:
- dbetas
change in parameters divided by the standard error of parameters from the full model results,
bse
.d_fittedvalues_scaled
Change in fittedvalues scaled by standard errors.
d_linpred
Change in linear prediction
- d_linpred_scale
local change in linear prediction scaled by the standard errors for the prediction based on cov_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.
resid_score
([joint, index, studentize])Score observations scaled by inverse hessian.
Score residual divided by sqrt of hessian factor.
Creates a DataFrame with influence results.
Properties
Cook's distance
Change in expected response, fittedvalues.
Change in fittedvalues scaled by standard errors.
Change in linear prediction
Change in linpred scaled by standard errors
Change in parameter estimates
Scaled change in parameter estimates.
Diagonal of the hat_matrix for GLM
Diagonal of the hat_matrix using only exog as in OLS
Parameter estimate based on one-step approximation.
Internally studentized pearson residuals