As complex-survey data becomes more widely used in health and social-science research, there is increasing interest in fitting a wider range of regression models. We describe an implementation of two-level linear mixed models in R using the pairwise composite likelihood approach of Rao and co-workers. We discuss the computational efficiency of pairwise composite likelihood and compare the estimator to the existing stagewise pseudolikelihood estimator in simulations and in data from the PISA educational survey.

翻译：随着复杂调查数据在健康与社会科学研究中的广泛应用，对拟合更广泛回归模型的需求日益增长。本文描述了基于Rao及其合作者提出的成对复合似然方法，在R语言中实现两层线性混合模型的过程。我们探讨了成对复合似然法的计算效率，并通过仿真实验及PISA教育调查数据的实证比较，将该估计量与现有阶段伪似然估计量进行了对比。