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軟件類型：軟件>行業軟件
軟件性質：破解軟件
操作系統：Windows
應用平台：Windows
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網站鏈接：http://www.statisticalinnovations.com/products/latentgold_v4.html
軟件介紹：

Latent GOLD 4.5是一套強大的潛在類別(latent class)和有限混合(finite mixture)程序包。Latent GOLD包含單獨的模塊來估計3種不同的模型結構-潛在聚類模型(LC Cluster models), 離散型因子模型(DFactor models)和潛在類別的回歸模型(LC Regression models)-這在有些不同的應用領域中很有用。Latent GOLD 4.5有基礎(Basic)和高級(Advanced)兩個版本。

Latent GOLD 4.5 特征

Known Class Indicator

This feature allows more control over the segment definitions by pre-assigning selected cases (not) to be in a particular class or classes.

For more information, see
Tutorial #5: Using Latent GOLD 4.5 with the Known Class Option .
In this tutorial, we illustrate the use of the ‘known class' feature in Latent GOLD 4.5 to take into account additional information on a subset of cases which allows us to classify them into a particular class with probability one. In this case, the information comes from a physician's diagnosis of the patient as ‘Depressed' or merely ‘Troubled', corresponding to 2 of the 3 latent classes.

Conditional Bootstrap p-value
Model difference bootstrap can be used to formally assess the significance in improvement associated with adding additional classes, additional DFactors and/or an additional DFactor levels to the model, or to relax any other model restriction.

Overdispersed (Count and Binomial Count in Regression)
Overdispersion is a common phenomenon in count data. It means that, as a result of unobserved heterogeneity, the variance of the count variable is larger than estimated by the Poisson (binomial) model. The overdispersed option makes it possible to account for unobserved heterogeneity by assuming that the rates (success probabilities) follow a gamma (beta) distribution. This yields a negative-binomial model for overdispersed Poisson counts and a negative-binomial model for overdispersed binomial counts. Note that this option is conceptually similar to including a normally distributed random intercept in a regression model for a count variable.

The overdispersion option is useful if one wishes to analyze count data using mixture or zero-inflated variants of (truncated) negative-binomial or beta-binomial models (Agresti, 2000; Long, 1997; Simonoff, 2003). The negative-binomial model is a Poisson model with an extra error term coming from a gamma distribution. The beta-binomial model is a variant of the binomial count model that assumes that the success probabilities come from a beta distribution. These models are common in fields such as criminology, political sciences, medicine, biology, and marketing.



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