g.X.RKHS: Generating RKHS smoothing mean of a dataset

In sxz155/PFDA:

Description

This function create a RKHS smoothing mean from an existing data set with known eigenvalues and eigenvectors

Usage

g.X.RKHS(N, n, e.val.x, e.vec.x, tau, phi, mu, m, e.val.z)

Arguments

N	real number, number of observations
n	real number, number of grid points
e.val.x	real vector n*1, eigenvalues
e.vec.x	real valued matrix n*N, eigenvectors
tau	range of the uniform distribution in KL expansion
phi	real number, penalty parameter
mu	real vector n*1, initial mean vector
m	positive integer, number of eigenvectors are going to be used
e.val.z	real valued matrix n*N, eigenvectors of noise

Value

a real valued matrix n*N

sxz155/PFDA documentation built on May 30, 2019, 10:40 p.m.