Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models - The R Package pbkrtest

被引：0

作者：

Halekoh, Ulrich ^{[1
]}

Hojsgaard, Soren ^{[2
]}

机构：

[1] Univ Southern Denmark, Dept Epidemiol Biostat & Biodemog, DK-5000 Odense C, Denmark

[2] Aalborg Univ, Dept Math Sci, DK-9220 Aalborg O, Denmark

来源：

JOURNAL OF STATISTICAL SOFTWARE | 2014年 / 59卷 / 09期

关键词：

adjusted degree of freedom; denominator degree of freedom; F test; linear mixed model; lme4; R; parametric bootstrap; Bartlett correction;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

When testing for reduction of the mean value structure in linear mixed models, it is common to use an asymptotic chi(2) test. Such tests can, however, be very poor for small and moderate sample sizes. The pbkrtest package implements two alternatives to such approximate chi(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.

引用

页码：1 / 32

页数：32