Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection | Zambom | Journal of Statistical Software
Authors: Adriano Zanin Zambom, Michael G. Akritas
Title: NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection
Abstract: We describe the R package NonpModelCheck for hypothesis testing and variable selection in nonparametric regression. This package implements functions to perform hypothesis testing for the significance of a predictor or a group of predictors in a fully nonparametric heteroscedastic regression model using high-dimensional one-way ANOVA. Based on the p values from the test of each covariate, three different algorithms allow the user to perform variable selection using false discovery rate corrections. A function for classical local polynomial regression is implemented for the multivariate context, where the degree of the polynomial can be as large as needed and bandwidth selection strategies are built in.

Page views:: 421. Submitted: 2015-03-18. Published: 2017-05-03.
Paper: NonpModelCheck: An R Package for Nonparametric Lack-of-Fit Testing and Variable Selection     Download PDF (Downloads: 98)
Supplements:
NonpModelCheck_3.0.tar.gz: R source package Download (Downloads: 3; 20KB)
v77i10.R: R replication code Download (Downloads: 7; 17KB)
v77i10.m: MATLAB replication code Download (Downloads: 4; 3KB)
prostate.txt: Supplementary data Download (Downloads: 5; 2MB)

DOI: 10.18637/jss.v077.i10

by
This work is licensed under the licenses
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.