a The Ohio State University.
b The University of California, Los Angeles.
Multivariate Behav Res. 2019 Mar-Apr;54(2):288-306. doi: 10.1080/00273171.2018.1516541. Epub 2019 Apr 15.
Measurement models, such as factor analysis and item response theory models, are commonly implemented within educational, psychological, and behavioral science research to mitigate the negative effects of measurement error. These models can be formulated as an extension of generalized linear mixed models within a unifying framework that encompasses various kinds of multilevel models and longitudinal models, such as partially nonlinear latent growth models. We introduce the R package , which implements profile maximum likelihood estimation to estimate complex measurement and growth models that can be formulated within the general modeling framework using the existing R package and function optim. We demonstrate the use of through two examples before concluding with a brief overview of other possible models.
测量模型，如因子分析和项目反应理论模型，通常在教育、心理和行为科学研究中实施，以减轻测量误差的负面影响。这些模型可以作为广义线性混合模型的扩展，在一个统一的框架内，包含各种多层次模型和纵向模型，如部分非线性潜在增长模型。我们引入了 R 包 ，它实现了剖面最大似然估计，以估计可以使用现有的 R 包 和函数 optim 在一般建模框架内制定的复杂测量和增长模型。我们通过两个示例演示了 的使用，然后简要概述了其他可能的模型。
