Linear transformations of linear mixed-effects models
Linear transformations of linear mixed-effects models
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DOI：
10.2307/2685902
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发表时间：
1997-11-01
影响因子：
1.8
通讯作者：
Brant, LJ
中科院分区：
文献类型：
--
作者：
Morrell, CH;Pearson, JD;Brant, LJ
许多文章讨论了在线性回归模型中如何处理低阶多项式和交互作用项。只有当所有低阶项都包含在模型中时，回归模型才会相对于变量的编码变换保持不变。如果忽略低阶项，则回归模型将不能很好地公式化。在本文中，我们将这项工作扩展到研究线性混合效应模型中变量排序的含义。我们演示了变量的线性变换如何影响模型和模型中固定效应的显著性检验。我们展示了转换如何修改模型中的随机效应，以及它们的协方差矩阵和受限对数似然的值。我们提出了一个线性混合效应模型的变量选择策略。
A number of articles have discussed the way lower order polynomial and interaction terms should be handled in linear regression models. Only if all lower order terms are included in the model will the regression model be invariant with respect to coding transformations of the variables. If lower order terms are omitted, the regression model will not be well formulated. In this paper, we extend this work to examine the implications of the ordering of variables in the linear mixed-effects model. We demonstrate how linear transformations of the variables affect the model and tests of significance of fixed effects in the model. We show how the transformations modify the random effects in the model, as well as their covariance matrix and the value of the restricted log-likelihood. We suggest a variable selection strategy for the linear mixed-effects model.