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首页> 外文期刊>Journal of applied statistics >Goodness-of-fit Measures Of R~2 For Repeated Measures Mixed Effect Models
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Goodness-of-fit Measures Of R~2 For Repeated Measures Mixed Effect Models

机译：重复测量混合效应模型的R〜2拟合优度

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摘要

Linear mixed effects model (LMEM) is efficient in modeling repeated measures longitudinal data. However, little research has been done in developing goodness-of-fit measures that can evaluate the models, particularly those that can be interpreted in an absolute sense without referencing a null model. This paper proposes three coefficient of determination (R~2) as goodness-of-fit measures for LMEM with repeated measures longitudinal data. Theorems are presented describing the properties of R~2 and relationships between the R~2 statistics. A simulation study was conducted to evaluate and compare the R~2 along with other criteria from literature. Finally, we applied the proposed R~2 to a real virologic response data of an HIV-patient cohort. We conclude that our proposed R~2 statistics have more advantages than other goodness-of-fit measures in the literature, in terms of robustness to sample size, intuitive interpretation, well-defined range, and unnecessary to determine a null model.
机译：线性混合效应模型（LMEM）可有效地对重复测量的纵向数据进行建模。但是，在开发可以评估模型的拟合优度度量标准方面，很少进行研究，尤其是那些可以在绝对意义上解释而不引用空模型的度量。提出了三个确定系数（R〜2）作为LMEM的拟合优度度量，并具有重复度量的纵向数据。给出了描述R〜2的性质以及R〜2统计量之间关系的定理。进行了仿真研究，以评估和比较R〜2与文献中的其他标准。最后，我们将建议的R〜2应用于HIV患者队列的真实病毒学应答数据。我们得出的结论是，就样本量的鲁棒性，直观的解释，明确的范围以及确定空模型的必要性而言，我们提出的R〜2统计量比文献中的其他拟合优度指标更具优势。

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