statsmodels.regression.dimred.SlicedInverseReg#

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

Sliced Inverse Regression (SIR)

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([slice_n])

Estimate the EDR space using Sliced Inverse Regression

fit_regularized([ndim, pen_mat, slice_n, ...])

Estimate the EDR space using regularized SIR

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

References

KC Li (1991). Sliced inverse regression for dimension reduction. JASA 86, 316-342.

Methods

fit([slice_n])

Estimate the EDR space using Sliced Inverse Regression

fit_regularized([ndim, pen_mat, slice_n, ...])

Estimate the EDR space using regularized SIR

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