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
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Authors: Kofi Placid Adragni, Andrew M. Raim
Title: ldr: An R Software Package for Likelihood-Based Sufficient Dimension Reduction
Abstract: In regression settings, a sufficient dimension reduction (SDR) method seeks the core information in a p-vector predictor that completely captures its relationship with a response. The reduced predictor may reside in a lower dimension d < p, improving ability to visualize data and predict future observations, and mitigating dimensionality issues when carrying out further analysis. We introduce ldr, a new R software package that implements three recently proposed likelihood-based methods for SDR: covariance reduction, likelihood acquired directions, and principal fitted components. All three methods reduce the dimensionality of the data by pro jection into lower dimensional subspaces. The package also implements a variable screening method built upon principal fitted components which makes use of flexible basis functions to capture the dependencies between the predictors and the response. Examples are given to demonstrate likelihood-based SDR analyses using ldr, including estimation of the dimension of reduction subspaces and selection of basis functions. The ldr package provides a framework that we hope to grow into a comprehensive library of likelihood-based SDR methodologies.

Page views:: 3610. Submitted: 2012-11-13. Published: 2014-11-03.
Paper: ldr: An R Software Package for Likelihood-Based Sufficient Dimension Reduction     Download PDF (Downloads: 2842)
ldr_1.3.3.tar.gz: R source package Download (Downloads: 242; 451KB)
v61i03.R: R example code from the paper Download (Downloads: 280; 2KB)

DOI: 10.18637/jss.v061.i03

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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.