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首页> 外文期刊>Applied Psychological Measurement >FitPMM: An R Routine to Fit Finite Mixture of Piecewise Mixed-Effect Models With Unknown Random Knots
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FitPMM: An R Routine to Fit Finite Mixture of Piecewise Mixed-Effect Models With Unknown Random Knots

机译：FitPMM：R例程，用于拟合具有未知随机结的分段混合效应模型的有限混合

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摘要

Piecewise mixed-effect models are frequently used in education and psychology to model segmented growth over time. For data that exhibit distinct phases of growth, piecewise models are attractive alternatives to familiar quadratic and higher order polynomial models because the parameters in piecewise models provide more relevant information about the mechanism underlying the change process (Fitzmaurice, Laird, & Ware, 2011). Different types of trajectories can be specified in the different phases of a piecewise model; however, a linear--linear piecewise model seems to dominate practical applications. An interesting feature of a linear--linear piece-wise model is the knot, the change point on the time axis where two linear splines join (Cudeck & Harring, 2010). In many applications, practitioners locate the knot in piecewise models a priori based on the subject-matter knowledge, and the knot is assumed to be known in subsequent statistical analyses. Moreover, the knot is commonly treated as a fixed parameter, indicating that each individual's change point is assumed to be the same.
机译：在教育和心理学中经常使用分段混合效应模型来模拟随时间变化的分段增长。对于表现出不同增长阶段的数据，分段模型是熟悉的二次多项式和高阶多项式模型的有吸引力的替代方法，因为分段模型中的参数提供了有关变化过程潜在机制的更多相关信息（Fitzmaurice，Laird，＆Ware，2011）。可以在分段模型的不同阶段中指定不同类型的轨迹。但是，线性-线性分段模型似乎在实际应用中占主导地位。线性-线性分段模型的一个有趣特征是结，时间轴上的变化点是两个线性样条曲线的连接点（Cudeck＆Harring，2010）。在许多应用中，从业者根据主题知识在先验模型中按先后顺序确定结点，并假定在随后的统计分析中知道结点。而且，结通常被视为固定参数，表明每个人的变化点都假定为相同。

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