solveforc: Solve for Choice Coefficients in KRLS

In KRLS: Kernel-Based Regularized Least Squares

Description

Internal function that computes choice coefficients for KRLS given a fixed value for lambda (the parameter that governs the tradeoff between model fit and complexity in KRLS). This function is called internally by krls. It would normally not be called by the user directly.

Usage

solveforc(y = NULL, Eigenobject = NULL,
lambda = NULL,eigtrunc=NULL)

Arguments

y	n by 1 matrix of outcomes.
Eigenobject	Object from call to eigen that contains spectral decomposition of the n by n Kernel matrix.
lambda	Positive scalar value for lamnbda parameter.
eigtrunc	Positive scalar value that determines truncation of eigenvalues for lamnda search window. See krls for details. Default is NULL which means no truncation.

Details

Function relies on fast eigenvalue decomposition method described in method Rifkin and Lippert (2007).

Value

coeffs	n by 1 one matrix of choice coefficients for KRLS model.
Le	n by 1 matrix of errors from leave-one-out validation.

Author(s)

Jens Hainmueller (Stanford) and Chad Hazlett (MIT)

References

Rifkin, Ryan M. and Lippert, Ross A. (2007). Notes on Regularized Least Squares. MIT-CSAIL-TR-2007-025. CBCL-268

See Also

krls

KRLS documentation built on May 2, 2019, 5:51 a.m.