varTestnlme: An R Package for Variance Components Testing in Linear and Nonlinear Mixed-Effects Models

被引:1
作者
Baey, Charlotte [1 ]
Kuhn, Estelle [2 ]
机构
[1] Univ Lille, CNRS, Lab Paul Painleve, UMR 8524, Lille, France
[2] Univ Paris Saclay, INRAE, UR140 MaIAGE, Paris, France
来源
JOURNAL OF STATISTICAL SOFTWARE | 2023年 / 107卷 / 06期
关键词
generalized mixed-effects models; nonlinear mixed-effects models; variance components; likelihood ratio test; random effects; R; LIKELIHOOD RATIO; ASYMPTOTIC-DISTRIBUTION; TEST STATISTICS; SELECTION; GROWTH;
D O I
10.18637/jss.v107.i06
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The issue of variance components testing arises naturally when building mixed-effects models, to decide which effects should be modeled as fixed or random or to build parsimonious models. While tests for fixed effects are available in R for models fitted with lme4, tools are missing when it comes to random effects. The varTestnlme package for R aims at filling this gap. It allows to test whether a subset of the variances and covariances corresponding to a subset of the random effects, are equal to zero using asymptotic property of the likelihood ratio test statistic. It also offers the possibility to test simultaneously for fixed effects and variance components. It can be used for linear, generalized linear or nonlinear mixed-effects models fitted via lme4, nlme or saemix. Numerical methods used to implement the test procedure are detailed and examples based on different real datasets using different mixed models are provided. Theoretical properties of the used likelihood ratio test are recalled.
引用
收藏
页码:1 / 32
页数:32
相关论文
共 42 条
  • [1] [Anonymous], 2011, SAS Version 9.3
  • [2] Baey Charlotte, 2023, CRAN
  • [3] Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models
    Baey, Charlotte
    Cournede, Paul-Henry
    Kuhn, Estelle
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2019, 135 : 107 - 122
  • [4] Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability
    Baey, Charlotte
    Mathieu, Amelie
    Jullien, Alexandra
    Trevezas, Samis
    Cournede, Paul-Henry
    [J]. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2018, 23 (02) : 208 - 232
  • [5] Fitting Linear Mixed-Effects Models Using lme4
    Bates, Douglas
    Maechler, Martin
    Bolker, Benjamin M.
    Walker, Steven C.
    [J]. JOURNAL OF STATISTICAL SOFTWARE, 2015, 67 (01): : 1 - 48
  • [6] Random effects selection in linear mixed models
    Chen, Z
    Dunson, DB
    [J]. BIOMETRICS, 2003, 59 (04) : 762 - 769
  • [7] Comets E, 2017, J STAT SOFTW, V80, P1
  • [8] Likelihood ratio tests in linear mixed models with one variance component
    Crainiceanu, CM
    Ruppert, D
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2004, 66 : 165 - 185
  • [9] Davidian M., 2017, NONLINEAR MODELS REP, DOI [10.1201/9780203745502, DOI 10.1201/9780203745502]
  • [10] Delattre M, 2023, Arxiv, DOI [arXiv:1909.06094, DOI 10.48550/ARXIV.1909.06094]