statsmodels.regression.dimred.SlicedInverseReg.fit_regularized¶
-
SlicedInverseReg.
fit_regularized
(ndim=1, pen_mat=None, slice_n=20, maxiter=100, gtol=0.001, **kwargs)[source]¶ Estimate the EDR space using regularized SIR.
- Parameters
- ndim: int
The number of EDR directions to estimate
- pen_matarray_like
A 2d array such that the squared Frobenius norm of dot(pen_mat, dirs)` is added to the objective function, where dirs is an orthogonal array whose columns span the estimated EDR space.
- slice_n
int
,optional
Target number of observations per slice
- maxiter :int
The maximum number of iterations for estimating the EDR space.
- gtol :float
If the norm of the gradient of the objective function falls below this value, the algorithm has converged.
- Returns
A
results
class
instance.
Notes
If each row of exog can be viewed as containing the values of a function evaluated at equally-spaced locations, then setting the rows of pen_mat to [[1, -2, 1, …], [0, 1, -2, 1, ..], …] will give smooth EDR coefficients. This is a form of “functional SIR” using the squared second derivative as a penalty.
References
L. Ferre, A.F. Yao (2003). Functional sliced inverse regression analysis. Statistics: a journal of theoretical and applied statistics 37(6) 475-488.