This manual describes a Stata program gllamm that can estimate Generalized Linear Latent and Mixed Models (GLLAMMs). GLLAMMs are a class of multilevel latent variable models for (multivariate) responses of mixed type including continuous responses, counts, duration/survival data, dichotomous, ordered and unordered categorical responses and rankings. The latent variables (common factors or random effects) can be assumed to be discrete or to have a multivariate normal distribution. Examples of models in this class are multilevel generalized linear models or generalized linear mixed models, multilevel factor or latent trait models, item response models, latent class models and multilevel structural equation models. The program can be download...
The R package lcmm provides a series of functions to estimate statistical models based on linear mix...
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound mode...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
Abstract. Generalized linear models with covariate measurement error can be estimated by maximum lik...
Generalized Linear Models (GLMs) (McCullagh and Nelder, 1989) provide a unified framework for fixed...
A unifying framework for generalized multilevel structural equation modeling is introduced. The mode...
The generalized linear mixed model (GLMM) generalizes the standard linear model in three ways: accom...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
Generalized linear models with covariate measurement error can be estimated by maximum likelihood us...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
1.There has been rapid development in tools for multivariate analysis based on fully specified stati...
1.There has been rapid development in tools for multivariate analysis based on fully specified stati...
Generalized linear mixed models or multilevel regression models have become increasingly popular. Se...
Generalized Linear Latent Variable Models (GLLVM) is a complex statistical model with latent variabl...
This article reviews Multilevel and Longitudinal Modeling Using Stata, by Rabe-Hesketh and Skrondal....
The R package lcmm provides a series of functions to estimate statistical models based on linear mix...
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound mode...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
Abstract. Generalized linear models with covariate measurement error can be estimated by maximum lik...
Generalized Linear Models (GLMs) (McCullagh and Nelder, 1989) provide a unified framework for fixed...
A unifying framework for generalized multilevel structural equation modeling is introduced. The mode...
The generalized linear mixed model (GLMM) generalizes the standard linear model in three ways: accom...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
Generalized linear models with covariate measurement error can be estimated by maximum likelihood us...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
1.There has been rapid development in tools for multivariate analysis based on fully specified stati...
1.There has been rapid development in tools for multivariate analysis based on fully specified stati...
Generalized linear mixed models or multilevel regression models have become increasingly popular. Se...
Generalized Linear Latent Variable Models (GLLVM) is a complex statistical model with latent variabl...
This article reviews Multilevel and Longitudinal Modeling Using Stata, by Rabe-Hesketh and Skrondal....
The R package lcmm provides a series of functions to estimate statistical models based on linear mix...
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound mode...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...