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The traditional latent class model can be used to handle measurement and classification errors in categorical variables, and can accomodate avriables that are nominal, ordinal, continuous, counts, or any combination of these. Covariates can be included directly in the model as well for improved cluster description.
Latent GOLD 潜在类和有限混合软件
Latent GOLD是一个功能强大的潜在类和有限混合程序，具有友好的点击式界面（GUI）。有两个附加选项可用于扩展基本版本的程序。
Advanced / Syntax add-on通过使用包括直观的LG-equations的Syntax命令语言，为用户提供更多控制。此附加组件还包含更的GUI建模功能，如Latent（Hidden）Markov和Multilevel模型。
Latent class (LC) choice models analyze these data in a way that accounts for heterogeneity by allowing different population segments (latent classes) to express different preferences in making their choices.
Group-level variation may also be accounted for by specifying group-level latent classes (GClasses) and/or group-level CFactors (GCFactors). In addition, when 2 or more GClasses are specified, group-level covariates (GCovariates) can be included in the model for improved description/ prediction.
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