A Second-Order Conditionally Linear Mixed Effects Model With Observed and Latent Variable Covariates.
A Second-Order Conditionally Linear Mixed Effects Model With Observed and Latent Variable Covariates.
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具有观测变量和潜变量协变量的二阶条件线性混合效应模型。
DOI：
10.1080/10705511.2012.634729
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Speece,DeborahL
中科院分区：
文献类型：
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作者：
Harring,JeffreyR;Kohli,Nidhi;Silverman,RebeccaD;Speece,DeborahL
条件线性混合效应模型是一个合适的框架，用于研究随时间重复测量的连续潜变量的非线性变化。该模型的功效在于，它允许进入指定的非线性时间响应函数的参数是随机的，而以非线性方式进入的那些参数对所有受试者是共同的。在本文中，我们将描述如何使用Mplus6.0将Michaelis-Menten（M-M）函数的变体拟合到该建模框架中。我们演示了如何观察到的和潜在的协变量可以被纳入，以帮助解释生长特征的个体差异。该模型的特点，包括一个关键的分析决策点的解释说明使用纵向阅读数据。为了帮助实现这类模型的可访问性，提供了带注释的Mpluscode。
A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a nonlinear manner are common to all subjects. In this article we describe how a variant of the Michaelis–Menten (M–M) function can be fit within this modeling framework using Mplus6.0. We demonstrate how observed and latent covariates can be incorporated to help explain individual differences in growth characteristics. Features of the model including an explication of key analytic decision points are illustrated using longitudinal reading data. To aid in making this class of models accessible, annotated Mpluscode is provided.