spatialPrediction

Internal function: Spatial prediction

internal

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions
by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The
method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Wen-Ting Wang

SpatPCA

Regularized Principal Component Analysis for Spatial Data

Hsin-Cheng Huang

spatialPrediction function

<dl><dt>phir</dt>
<dd>A matrix of estimated eigenfunctions based on original locations</dd>
<dt>Yr</dt>
<dd>A data matrix</dd>
<dt>gamma</dt>
<dd>A gamma value</dd>
<dt>predicted_eignefunction</dt>
<dd>A vector of values of an eigenfunction on new locations</dd></dl>

Arguments

Internal function: Spatial prediction — spatialPrediction

<dl>

<dt>phir</dt>
<dd>A matrix of estimated eigenfunctions based on original locations</dd>


<dt>Yr</dt>
<dd>A data matrix</dd>


<dt>gamma</dt>
<dd>A gamma value</dd>


<dt>predicted_eignefunction</dt>
<dd>A vector of values of an eigenfunction on new locations</dd>

</dl>

spatialPrediction: Internal function: Spatial prediction

Description

Usage

Value

Arguments