lossFunction.looe: Computation of the leave one out error (LOOE) in kernel semi...

In KSPM: Kernel Semi-Parametric Models

Description

internal function to optimize model for estimating hyperparameters based on LOOE

Usage

lossFunction.looe(param. = NULL, Y. = NULL, X. = NULL,
  kernelList. = NULL, n. = NULL, not.missing. = NULL,
  compute.kernel. = NULL, print.lambda. = FALSE)

Arguments

param.	initial parameter values.
Y.	response matrix.
X.	X matrix (linear part).
kernelList.	list of kernels (kernel part).
n.	nb of samples.
not.missing.	nb of non missing samples.
compute.kernel.	boolean. If TRUE, the kernel matrix is computed at each iteration. Should be TRUE when hyperparameters of kernel functions should be estimated by the model.
print.lambda.	boolean. If TRUE, values of tunning parameters (lambda) are printed at each iteration.

Author(s)

Catherine Schramm, Aurelie Labbe, Celia Greenwood

KSPM documentation built on Aug. 10, 2020, 5:07 p.m.