fetchUpperBoundNumberEigenfunctions

Internal function: Fetch the upper bound of the number of eigenfunctions

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

fetchUpperBoundNumberEigenfunctions function

<dl><dt>Y</dt>
<dd>Data matrix</dd>
<dt>M</dt>
<dd>Number of folds for cross-validation</dd></dl>

Arguments

Internal function: Fetch the upper bound of the number of eigenfunctions — fetchUpperBoundNumberEigenfunctions

<dl>

<dt>Y</dt>
<dd>Data matrix</dd>


<dt>M</dt>
<dd>Number of folds for cross-validation</dd>

</dl>

fetchUpperBoundNumberEigenfunctions: Internal function: Fetch the upper bound of the number of eigenfunctions

Description

Usage

Value

Arguments