expectedXi

spline basis functions for the current subject.

theta

vector of spline coefficients for the population mean.

vector of expected mean scores for the current subject.

matrix of spline coefficients for the principal component basis functions.

expected covariance matrix of scores for the current subject.

Function calculates value of variational parameter using maximum likelihood.

A method for registering curves (functional data) that are generated from exponential family distributions. This
implements the algorithms described in 'Wrobel et al. (2019)' <doi:10.1111/biom.12963>. Curve registration is an active area of
research in functional data analysis, and 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

Jeff Goldsmith

Erin McDonnell

expectedXi function

<dl><dt>theta</dt>
<dd>spline basis functions for the current subject.</dd>
<dt>mu</dt>
<dd>vector of spline coefficients for the population mean.</dd>
<dt>mi</dt>
<dd>vector of expected mean scores for the current subject.</dd>
<dt>psi</dt>
<dd>matrix of spline coefficients for the principal component basis functions.</dd>
<dt>Ci</dt>
<dd>expected covariance matrix of scores for the current subject.</dd></dl>

Arguments

Estimate variational parameter for the current subject. — expectedXi

<dl>

<dt>theta</dt>
<dd>spline basis functions for the current subject.</dd>


<dt>mu</dt>
<dd>vector of spline coefficients for the population mean.</dd>


<dt>mi</dt>
<dd>vector of expected mean scores for the current subject.</dd>


<dt>psi</dt>
<dd>matrix of spline coefficients for the principal component basis functions.</dd>


<dt>Ci</dt>
<dd>expected covariance matrix of scores for the current subject.</dd>

</dl>

expectedXi: Estimate variational parameter for the current subject.

Description

Usage

Value

Arguments