Fitting correlated residual error structures in nonlinear mixed-effects models using SAS PROC NLMIXED
Fitting correlated residual error structures in nonlinear mixed-effects models using SAS PROC NLMIXED
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DOI：
10.3758/s13428-013-0397-z
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发表时间：
2014-06-01
影响因子：
5.4
通讯作者：
Blozis, Shelley A.
中科院分区：
文献类型：
--
作者：
Harring, Jeffrey R.;Blozis, Shelley A.
非线性混合效应（NLME）模型在从业者中仍然很受欢迎，用于分析当兴趣集中于表征个体特定变化时对多个个体中的每个个体进行的连续重复测量数据。在这个框架内，重复测量之间的变化和相关性可以被划分为个体间变化和个体内变化分量。在许多应用中，残差的协方差结构与齐次方差σ I-2（ni）无关，这不是因为人们认为个体内变异符合这种结构，而是因为许多估计这种模型参数的软件程序没有很好地处理其他可能更现实的模式。在这篇文章中，我们描述了如何在SAS的编程环境可以利用模型残差结构的序列相关性和方差异质性。最后通过一个实例说明了该模块的功能。
Nonlinear mixed-effects (NLME) models remain popular among practitioners for analyzing continuous repeated measures data taken on each of a number of individuals when interest centers on characterizing individual-specific change. Within this framework, variation and correlation among the repeated measurements may be partitioned into interindividual variation and intraindividual variation components. The co-variance structure of the residuals are, in many applications, consigned to be independent with homogeneous variances, sigma I-2(ni), not because it is believed that intraindividual variation adheres to this structure, but because many software programs that estimate parameters of such models are not well-equipped to handle other, possibly more realistic, patterns. In this article, we describe how the programmatic environment within SAS may be utilized to model residual structures for serial correlation and variance heterogeneity. An empirical example is used to illustrate the capabilities of the module.