horizFPCA

This function calculates vertical functional principal component analysis
on aligned data

alignment

srvf

Performs alignment, PCA, and modeling of multidimensional and
unidimensional functions using the square-root velocity framework
(Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014
<DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic
analysis of functional data through phase and amplitude separation.

J Derek Tucker

fdasrvf

Elastic Functional Data Analysis

J. Derek Tucker

Aymeric Stamm

horizFPCA function

<dl><dt>warp_data</dt>
<dd>fdawarp object from time_warping of aligned data</dd>
<dt>no</dt>
<dd>number of principal components to extract</dd>
<dt>var_exp</dt>
<dd>compute no based on value percent variance explained (example: 0.95)
will override <code>no</code></dd>
<dt>ci</dt>
<dd>geodesic standard deviations (default = c(-1,0,1))</dd>
<dt>showplot</dt>
<dd>show plots of principal directions (default = T)</dd></dl>

Arguments

Horizontal Functional Principal Component Analysis — horizFPCA

<dl>

<dt>warp_data</dt>
<dd>fdawarp object from time_warping of aligned data</dd>


<dt>no</dt>
<dd>number of principal components to extract</dd>


<dt>var_exp</dt>
<dd>compute no based on value percent variance explained (example: 0.95)
will override <code>no</code></dd>


<dt>ci</dt>
<dd>geodesic standard deviations (default = c(-1,0,1))</dd>


<dt>showplot</dt>
<dd>show plots of principal directions (default = T)</dd>

</dl>

horizFPCA: Horizontal Functional Principal Component Analysis

Description

Usage

Value

Arguments

References

Examples