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.
- results
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
- 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
.d_fittedvalues_scaled
same
as
d_fittedvalues
but
scaled
by
the
standard
Change in fittedvalues scaled by standard errors
- params_one
is
the
one
step
parameter
estimate
computed
as
params
from the full sample minus
d_params
.
- hat_matrix_diag (hii)
Methods
plot_index
([y_var, threshold, title, ax, idx])index plot for influence attributes
plot_influence
([external, alpha, criterion, ...])Plot of influence in regression.
Creates a DataFrame with influence results.
Properties
Cook's distance and p-values
Change in expected response, fittedvalues
Change in fittedvalues scaled by standard errors
Change in parameter estimates
Scaled change in parameter estimates
Diagonal of the generalized leverage
Parameter estimate based on one-step approximation
Score residual divided by sqrt of hessian factor