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Bayesian estimation of twolevel models with latent variable interactions using the XWITH option (Asparouhov & Muthén, 2019b). This is especially helpful for models with moderation where maximum-likelihood estimation is problematic due to the need for numerical integration.
The Mplus Modeling Framework
The purpose of modeling data is to describe the structure of data in a simple way so that it is understandable and interpretable. Essentially, the modeling of data amounts to specifying a set of relationships between variables. The figure below shows the types of relationships that can be modeled in Mplus. The rectangles represent observed variables. Observed variables can be outcome variables or background variables. Background variables are referred to as x; continuous and censored outcome variables are referred to as y; and binary, ordered categorical (ordinal), unordered categorical (nominal), and count outcome variables are referred to as u. The circles represent latent variables. Both continuous and categorical latent variables are allowed. Continuous latent variables are referred to as f. Categorical latent variables are referred to as c.
New DSEM features are available. They include random correlations and changes to TINTERVAL, SAVEDATA, MONTECARLO and plots. See Mplus Web Talk No. 6.
Mplus的建模框架借鉴了潜变量的统一主题。而且一般的建模框架来自连续和分类潜变量的使用。连续潜变量用于表示与未观测到的构造相对应的因素,随机效应与发展中的个体差异相对应,随机效应与分层数据中各组间系数变化相对应,弱点对应于生存时间的异质性,责任与疾病遗传易感性相对应,潜在响应变量值与缺失数据相对应。分类潜变量对应于均质个体群,潜在的轨迹分类对应于未观测种群的发展类型,混合组件对应于未观测种群的有限混合,潜在响应变量类别对应于缺失数据。
Mplus Base Program and Mixture Add-On
包含了所有Mplus Base Program的功能。此外,估计回归混合模型;路径分析混合模型;潜在类别分析;具有多分类潜变量的潜类分析;对数线性模型;有限混合模型;编译器的平均因果关系(CACE)模型;潜在类增长分析;潜在转移分析;隐马尔可夫模型以及离散和连续时间生存混合分析。观测到的因变量可以是连续的、删失的、二元的、有序的(序数)、无序的分类(名词)、计数或这些变量类型的组合。其他功能包括单组或多组分析;缺失数据估计;复杂的调查数据分析,包括分层、聚类和不平等的选择概率(抽样权重);用极大似然法分析潜在变量相互作用和非线性因素;随机斜率;个体变化的观测次数;非线性参数约束;所有结果类型的极大似然估计。引导的标准误差和置信区间;贝叶斯分析与多重归责原则;蒙特卡罗模拟功能以及后处理图形模型。
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