expectedScores

Calculations derived using maximum likelihood estimation.

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

expectedScores function

<dl><dt>Y</dt>
<dd>vector of observations for the current subject.</dd>
<dt>mu</dt>
<dd>vector of spline coefficients for the population mean.</dd>
<dt>psi</dt>
<dd>matrix of spline coefficients for the principal component basis functions.</dd>
<dt>theta</dt>
<dd>spline basis functions for the current subject.</dd>
<dt>theta_quad</dt>
<dd>quadratic form of theta for the current subject.</dd></dl>

Arguments

Calculate expected score and score variance for the current subject. — expectedScores

<dl>

<dt>Y</dt>
<dd>vector of observations for the current subject.</dd>


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


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


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


<dt>theta_quad</dt>
<dd>quadratic form of theta for the current subject.</dd>

</dl>

expectedScores: Calculate expected score and score variance for the current subject.

Description

Usage

Value

Arguments