statsmodels.regression.dimred.PrincipalHessianDirections#

class statsmodels.regression.dimred.PrincipalHessianDirections(endog, exog, **kwargs)[source]#

Principal Hessian Directions (PHD)

Parameters:
endogarray_like (1d)

The dependent variable

exogarray_like (2d)

The covariates

Attributes:
endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

Methods

fit(**kwargs)

Estimate the EDR space using PHD

from_formula(formula, data[, subset, drop_cols])

Create a Model from a formula and dataframe

predict(params[, exog])

After a model has been fit, predict returns the fitted values

Notes

Call fit on the model instance to obtain a results instance, from which the estimated parameters can be obtained.

References

KC Li (1992). On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another application of Stein’s lemma. JASA 87:420.

Methods

fit(**kwargs)

Estimate the EDR space using PHD

from_formula(formula, data[, subset, drop_cols])

Create a Model from a formula and dataframe

predict(params[, exog])

After a model has been fit, predict returns the fitted values

Properties

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables