Institute for Mathematical Research & Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia.
Comput Methods Programs Biomed. 2011 Dec;104(3):e122-32. doi: 10.1016/j.cmpb.2011.06.003. Epub 2011 Jul 20.
Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML.
相关有序数据在许多研究领域中都很常见。这些数据可能来自生物学、医学或临床领域的纵向研究。这些数据的突出特点是，个体内观察值是相关的，而个体间观察值是独立的。已经提出了许多方法来分析相关有序数据。一种评估所提出模型的性能或小或中等规模数据集的性能的方法是通过模拟研究。因此，提供一种用于在模拟研究中使用的生成相关有序数据的工具是很重要的。在本文中，我们描述了一个基于 R 语言和 SAS IML 生成相关有序数据的宏程序。
