multivariate_pca

Calculate principal directions of a set of curves

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

multivariate_pca function

<dl><dt>align_data</dt>
<dd>fdacurve object from multivariate_karcher_mean of aligned data</dd>
<dt>no</dt>
<dd>number of components</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>mode</dt>
<dd>Open (<code>"O"</code>) or Closed (<code>"C"</code>) curves</dd>
<dt>showplot</dt>
<dd>show plots of principal directions (default = TRUE)</dd></dl>

Arguments

Curve PCA — multivariate_pca

<dl>

<dt>align_data</dt>
<dd>fdacurve object from multivariate_karcher_mean of aligned data</dd>


<dt>no</dt>
<dd>number of components</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>mode</dt>
<dd>Open (<code>"O"</code>) or Closed (<code>"C"</code>) curves</dd>


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

</dl>

multivariate_pca: Curve PCA

Description

Usage

Value

Arguments

References

Examples