eigenFunction: Interpolated Eigen-function

In SpatPCA: Regularized Principal Component Analysis for Spatial Data

Interpolated Eigen-function

Description

Produce Eigen-function values based on new locations

Usage

eigenFunction(new_location, original_location, Phi)

Arguments

new_location	A location matrix
original_location	A location matrix
Phi	An eigenvector matrix

Value

A predictive estimate matrix

Examples

pesudo_sequence <- seq(-5, 5, length = 2)
original_location <- as.matrix(expand.grid(x = pesudo_sequence, y = pesudo_sequence))
new_location <- matrix(c(0.1, 0.2), nrow = 1, ncol = 2)
Phi <- matrix(c(1, 0, 0, 0), nrow = 4, ncol = 1)
thin_plate_matrix <- eigenFunction(new_location, original_location, Phi)

SpatPCA documentation built on Nov. 13, 2023, 5:06 p.m.