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Editors-in-chief: Bettina Grün, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models – The R Package pbkrtest | Halekoh | Journal of Statistical Software
Authors: Ulrich Halekoh, Søren Højsgaard
Title: A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models The R Package pbkrtest
Abstract: When testing for reduction of the mean value structure in linear mixed models, it is common to use an asymptotic ?2 test. Such tests can, however, be very poor for small and moderate sample sizes. The pbkrtest package implements two alternatives to such approximate ?2 tests: The package implements (1) a Kenward-Roger approximation for performing F tests for reduction of the mean structure and (2) parametric bootstrap methods for achieving the same goal. The implementation is focused on linear mixed models with independent residual errors. In addition to describing the methods and aspects of their implementation, the paper also contains several examples and a comparison of the various methods.

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Paper: A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models The R Package pbkrtest     Download PDF (Downloads: 4037)
Supplements:
pbkrtest_0.4-0.tar.gz: R source package Download (Downloads: 169; 126KB)
v59i09.R: R example code from the paper Download (Downloads: 224; 7KB)
v59i09-supplements.zip: R and SAS code for simulations and results Download (Downloads: 188; 4KB)

DOI: 10.18637/jss.v059.i09

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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.