gllamm can estimate both conventional and unconventional latent class models. Models are specified using discrete latent variables whose values determine the conditional response distributions for the classes. A new feature of gllamm is that latent class probabilities can depend on covariates. We will first discuss the conventional exploratory latent class model. When a number of fallible diagnoses of some disease are available, this model can be used to estimate the prevalence of the disease as well as the sensitivities and specificities of the tests in the absence of a gold standard. After estimating the model in gllamm, gllapred can be used to diagnose individual subjects based on their posterior class probabilities. An advantage of using gllamm is that a wide range of response types can be accommodated. To illustrate this, we consider the analysis of rankings of political goals in the study of value orientations. We will also discuss confirmatory models such as latent class factor models and apply them to attitudes to abortion data, taking the survey design into account by using probability weighting and robust standard errors. Finally, we consider latent trajectory models for investigating distinct patterns of change in longitudinal data.
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