elastic_amp_change_ff

This function identifies a amplitude changepoint using a fully functional
approach

alignment

changepoint

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

elastic_amp_change_ff function

<dl><dt>f</dt>
<dd>matrix (\(N\) x \(M\)) of \(M\) functions with \(N\) samples</dd>
<dt>time</dt>
<dd>vector of size \(N\) describing the sample points</dd>
<dt>d</dt>
<dd>number of monte carlo iterations of Brownian Bridge (default = 1000)</dd>
<dt>h</dt>
<dd>window selection of long range covariance function (default = 0)</dd>
<dt>smooth_data</dt>
<dd>smooth data using box filter (default = F)</dd>
<dt>sparam</dt>
<dd>number of times to apply box filter (default = 25)</dd>
<dt>showplot</dt>
<dd>show results plots (default = T)</dd></dl>

Arguments

Elastic Amplitude Changepoint Detection — elastic_amp_change_ff

<dl>

<dt>f</dt>
<dd>matrix (\(N\) x \(M\)) of \(M\) functions with \(N\) samples</dd>


<dt>time</dt>
<dd>vector of size \(N\) describing the sample points</dd>


<dt>d</dt>
<dd>number of monte carlo iterations of Brownian Bridge (default = 1000)</dd>


<dt>h</dt>
<dd>window selection of long range covariance function (default = 0)</dd>


<dt>smooth_data</dt>
<dd>smooth data using box filter (default = F)</dd>


<dt>sparam</dt>
<dd>number of times to apply box filter (default = 25)</dd>


<dt>showplot</dt>
<dd>show results plots (default = T)</dd>

</dl>

elastic_amp_change_ff: Elastic Amplitude Changepoint Detection

Description

Usage

Value

Arguments

References