Linear mixed-effects models are commonly used to analyze clustered data structures. There are numerous packages to fit these models in R and conduct likelihood-based inference. The implementation of resampling-based procedures for inference are more limited. In this paper, we introduce the lmeresampler package for bootstrapping nested linear mixed-effects models fit via lme4 or nlme. Bootstrap estimation allows for bias correction, adjusted standard errors and confidence intervals for small samples sizes and when distributional assumptions break down. We will also illustrate how bootstrap resampling can be used to diagnose this model class. In addition, lmeresampler makes it easy to construct interval estimates of functions of model parameters.
翻译:通常使用线性混合效应模型来分析集束数据结构。 有许多包件可以将这些模型纳入R, 并进行基于概率的推断。 执行基于重新采样的推断程序较为有限 。 在本文件中, 我们引入了适合 lme4 或 nlme 的套件套件套件套件, 用于套用套件套件套件套件的嵌套式线性线性混合效应模型 。 诱杀装置估计允许对小样本大小和分布性假设破裂时的偏差、 调整标准错误和置信间隔进行纠正。 我们还将说明如何使用靴式套件重新采样来诊断这个模型类别。 此外, 模样器便于构建模型参数的间隔估计 。