A Latent Class Tree model is estimated with Latent GOLD 5.1. A 2-class split is performed on the data (can be increased by adjusting maxClassSplit1) and a new dataset is constructed for each class by multiplying the original weights with the posterior membership probabilities. Subsequently the procedure is repeated for the new classes and this goes on untill no classes are split further.
|Author||Mattis van den Bergh|
|Maintainer||Mattis van den Bergh <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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