The program gllamm runs in the statistical package Stata and estimates GLLAMMs (Generalized Linear Latent And Mixed Models) by maximum likelihood (see help gllamm after installation). gllamm maximises the marginal log-likelihood using Stata’s version of the Newton Raphson Algorithm (ml with method d0). In the case of discrete random effects or factors, the marginal log-likelihood is evaluated exactly whereas numerical integration is used for continuous (multivariate) normal random effects or factors. Two methods are available for numerical integration: Quadrature or adaptive quadrature. In both cases it is essential to make sure that a sufficient number of quadrature points has been used by comparing solutions with different numbers quadrature points. In most cases adaptive quadrature will perform better than ordinary quadrature. This is particularly the case if the cluster sizes are large and the responses include (large) counts and/or continuous variables. Even where ordinary quadrature performs well, adaptive quadrature often requires fewer quadrature points making it faster.

References in zbMATH (referenced in 34 articles )

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  1. Wang, Chun; Xu, Gongjun; Zhang, Xue: Correction for item response theory latent trait measurement error in linear mixed effects models (2019)
  2. Francesco Bartolucci and Claudia Pigini: cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models (2017) not zbMATH
  3. Sheldrake, Richard: Differential predictors of under-confidence and over-confidence for mathematics and science students in England (2016) MathEduc
  4. Zaloumis, Sophie G.; Scurrah, Katrina J.; Harrap, Stephen B.; Ellis, Justine A.; Gurrin, Lyle C.: Non-proportional odds multivariate logistic regression of ordinal family data (2015)
  5. Adam Loy; Heike Hofmann: HLMdiag: A Suite of Diagnostics for Hierarchical Linear Models in R (2014) not zbMATH
  6. Lucas, Jean-Paul; Sébille, Véronique; Le Tertre, Alain; Le Strat, Yann; Bellanger, Lise: Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood (2014)
  7. Feddag, M.-L.: Composite likelihood estimation for multivariate probit latent traits models (2013)
  8. George Leckie; Chris Charlton: runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata (2013) not zbMATH
  9. Hargreaves Heap, Shaun P.; Tan, Jonathan H. W.; Zizzo, Daniel John: Trust, inequality and the market (2013)
  10. Sulis, Isabella; Capursi, Vincenza: Building up adjusted indicators of students’ evaluation of university courses using generalized item response models (2013)
  11. Skrondal, Anders; Kuha, Jouni: Improved regression calibration (2012)
  12. Snijders, Tom A. B.; Bosker, Roel J.: Multilevel analysis. An introduction to basic and advanced multilevel modeling (2012)
  13. Bacci, Silvia; Caviezel, Valeria: Multilevel IRT models for the university teaching evaluation (2011)
  14. Paul De Boeck; Marjan Bakker; Robert Zwitser; Michel Nivard; Abe Hofman; Francis Tuerlinckx; Ivailo Partchev: The Estimation of Item Response Models with the lmer Function from the lme4 Package in R (2011) not zbMATH
  15. Cai, Li: High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm (2010)
  16. Feddag, M.-L.; Bacci, S.: Pairwise likelihood for the longitudinal mixed Rasch model (2009)
  17. Goldstein, Harvey; Carpenter, James; Kenward, Michael G.; Levin, Kate A.: Multilevel models with multivariate mixed response types (2009)
  18. Sánchez, Brisa N.; Budtz-Jørgensen, Esben; Ryan, Louise M.: An estimating equations approach to fitting latent exposure models with longitudinal health outcomes (2009)
  19. Grün, Bettina; Leisch, Friedrich: Identifiability of finite mixtures of multinomial logit models with varying and fixed effects (2008)
  20. Grilli, Leonardo; Rampichini, Carla: A multilevel multinomial logit model for the analysis of graduates’ skills (2007)

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