We discuss prediction of random effects and of expected responses in multilevel generalized linear models. Prediction of random effects is useful for instance in small area estimation and disease mapping, effectiveness studies and model diagnostics. Prediction of expected responses is useful for...

This presentation focuses on predicted probabilities for multilevel models for dichotomous or ordinal responses. In a three-level model, for instance with patients nested in doctors nested in hospitals, predictions for patients could be for new or existing doctors and, in the latter case, for...

gllamm is a program to fit generalised linear latent and mixed models. Since gllamm6 appeared in the STB (sg129), a large number of new features have been added. Two important extensions will be discussed: 1) More response processes can now be modelled including ordered and unordered categorical...

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....

Generalized linear mixed models are generalized linear models that include random effects varying between clusters or 'higher-level' units of hierarchically structured data. Such models can be estimated using gllamm. The prediction command gllapred can be used to obtain empirical Bayes...

Latent variable modelling has gradually become an integral part of mainstream statistics and is currently used for a multitude of applications in different subject areas. Examples of 'traditional' latent variable models include latent class models, item-response models, common factor models,...

Generalized linear mixed models or multilevel regression models have become increasingly popular. Several methods have been proposed for estimating such models. However,to date there is no single method that can be assumed to work well in all circumstances in terms of both parameter recovery and...

This text is a Stata-specific treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are "mixed" in the sense that they allow fixed and random effects and are "generalized" in the sense that they are appropriate not only for continuous...

Generalized linear models with covariate measurement error can be estimated by maximum likelihood using gllamm, a program that fits a large class of multilevel latent variable models (Rabe-Hesketh, Skrondal, and Pickles 2004). The program uses adaptive quadrature to evaluate the log likelihood,...