The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allow the interpretation of the defined parameters to understand longitudinal trajectories. Currently, there are no interface R packages that can easily fit the Sigmoidal Mixed Model allowing the inclusion of covariates or incorporate recent developments to fit the Piecewise Mixed Model with random change. To facilitate the modeling of the Sigmoidal Mixed Model, and Piecewise Mixed Model with abrupt or smooth random change, we have created an R package called nlive. All needed pieces such as functions, covariance matrices, and initials generation were programmed. The package was implemented with recent developments such as the polynomial smooth transition of piecewise mixed model with improved properties over Bacon-Watts, and the stochastic approximation expectation-maximization (SAEM) for efficient estimation. It was designed to help interpretation of the output by providing features such as annotated output, warnings, and graphs. Functionality, including time and convergence, was tested using simulations. We provided a data example to illustrate the package use and output features and interpretation. The package implemented in the R software is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=nlive. The nlive package for R fits the Sigmoidal Mixed Model and the Piecewise Mixed: abrupt and smooth. The nlive allows fitting these models with only five mandatory arguments that are intuitive enough to the less sophisticated users.
翻译:使用具有特定功能形式的混合效应模型,如Sigmodal混合模型和Pieflefine混合模型(或变相点混合模型)的混合效应模型,突变或平滑随机变化,使得能够对定义参数进行解释,以理解纵向轨迹。目前,没有可以很容易地适应Sigmodal混合模型的接口 R 包件,以便纳入共变或纳入最新动态,以适应Piegmodle混合模型,并随机随机随机调整的混合模型;为了便利模拟Sigmodle混合模型,以及突变或平滑随机变的混合混合模型,我们创建了一个名为Nlifer的R组合组合组合组合。所有需要的部件,如功能、变异式组合和初始生成都已经编程。执行该套件与最近的发展相匹配,如在Bacon-Watts上改进了特性的纸质混合模型平稳转换,以及用于高效估算的随机组合(SAEM),目的是帮助解释输出结果,例如说明性输出、警告和图表。功能性模型,包括时间和趋汇,在IMRIM IML 中,我们提供了一个数据模型,用来解释。