Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Sarah Brockhaus, David Rügamer, Sonja Greven
Title: Boosting Functional Regression Models with FDboost
Abstract: The R add-on package FDboost is a flexible toolbox for the estimation of functional regression models by model-based boosting. It provides the possibility to fit regression models for scalar and functional response with effects of scalar as well as functional covariates, i.e., scalar-on-function, function-on-scalar and function-on-function regression models. In addition to mean regression, quantile regression models as well as generalized additive models for location scale and shape can be fitted with FDboost. Furthermore, boosting can be used in high-dimensional data settings with more covariates than observations. We provide a hands-on tutorial on model fitting and tuning, including the visualization of results. The methods for scalar-on-function regression are illustrated with spectrometric data of fossil fuels and those for functional response regression with a data set including bioelectrical signals for emotional episodes.

Page views:: 768. Submitted: 2017-05-30. Published: 2020-09-08.
Paper: Boosting Functional Regression Models with FDboost     Download PDF (Downloads: 242)
FDboost_1.0-0.tar.gz: R source package Download (Downloads: 9; 2MB) Replication materials Download (Downloads: 9; 292KB)

DOI: 10.18637/jss.v094.i10

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.