determine_npc

This internal function is called in <code>gfpca_twoStep</code>, <code>fpca_gauss</code>
and <code>bfpca</code> to determine the number of functional principal components
based on their share of explained variance.

A method for performing joint registration and functional principal
component analysis for curves (functional data) that are generated from exponential family distributions. This
mainly implements the algorithms described in 'Wrobel et al. (2019)' <doi:10.1111/biom.12963> and further adapts them to potentially
incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration
can be used to better understand patterns in functional data by separating curves into phase and amplitude variability.
This software handles both binary and continuous functional data, and is
especially applicable in accelerometry and wearable technology.

Julia Wrobel

registr

Curve Registration for Exponential Family Functional Data

Alexander Bauer

Erin McDonnell

Fabian Scheipl

Jeff Goldsmith

determine_npc function

<dl><dt>evalues</dt>
<dd>Vector of estimated variances of the FPC scores.</dd>
<dt>npc_criterion</dt>
<dd>Either (i) a share between 0 and 1, or (ii) a vector with
two elements for the targeted explained share of variance and a cut-off scree
plot criterion, both between 0 and 1. For the latter, e.g.,
<code>npc_criterion = c(0.9,0.02)</code> tries to choose a number of FPCs that
explains at least 90% of variation, but only includes FPCs that explain at
least 2% of variation (even if this means 90% explained variation is not reached).</dd></dl>

Arguments

Determine the number of FPCs based on the share of explained variance — determine_npc

<dl>

<dt>evalues</dt>
<dd>Vector of estimated variances of the FPC scores.</dd>


<dt>npc_criterion</dt>
<dd>Either (i) a share between 0 and 1, or (ii) a vector with
two elements for the targeted explained share of variance and a cut-off scree
plot criterion, both between 0 and 1. For the latter, e.g.,
<code>npc_criterion = c(0.9,0.02)</code> tries to choose a number of FPCs that
explains at least 90% of variation, but only includes FPCs that explain at
least 2% of variation (even if this means 90% explained variation is not reached).</dd>

</dl>

determine_npc: Determine the number of FPCs based on the share of explained variance

Description

Usage

Value

Arguments