deriv.inv.logit

Compute the derivative of the logit function for a given point <code>x</code>.

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

deriv.inv.logit function

<dl><dt>x</dt>
<dd>Value at which the derivative is computed</dd></dl>

Arguments

Alexander Bauer <a href="/link/alexander.bauer%40stat.uni-muenchen.de?package=registr&version=2.1.0" data-mini-rdoc="registr::alexander.bauer@stat.uni-muenchen.de">alexander.bauer@stat.uni-muenchen.de</a>

Author

Estimate the derivative of the logit function — deriv.inv.logit

<dl>

<dt>x</dt>
<dd>Value at which the derivative is computed</dd>

</dl>

Alexander Bauer <a href='mailto:alexander.bauer@stat.uni-muenchen.de'>alexander.bauer@stat.uni-muenchen.de</a>

deriv.inv.logit: Estimate the derivative of the logit function

Description

Usage

Arguments

Author