looloss

Object from call to <code>eigen</code> that contains spectral decomposition of the n by n Kernel matrix.

Eigenobject

Positive scalar value for lamnbda parameter.

lambda

Positive scalar value that determines truncation of eigenvalues for lamnda search window. See <code><a rd-options="" href="/link/krls?package=KRLS&version=1.0-0" data-mini-rdoc="KRLS::krls">krls</a></code> for details. Default is <code>NULL</code> which means no truncation.

eigtrunc

Internal function that computes Leave-On-Out (LOO) Error 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 <code>krls</code> to find value of lambda that minimizes the LOO error. It would normally not be called by the user directly.

Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).

Jens Hainmueller

KRLS

Kernel-Based Regularized Least Squares

looloss function

Positive scalar value that determines truncation of eigenvalues for lamnda search window. See <code><a rd-options='' href='krls'>krls</a></code> for details. Default is <code>NULL</code> which means no truncation.

looloss: Loss Function for Leave One Out Error

Description

Usage

Arguments

Value

See Also