Add to ORKGData with multiple responses is ubiquitous in modern applications. However, few tools are available for regression analysis of multivariate counts. The most popular multinomial-logit model has a very restrictive mean-variance structure, limiting its applicability to many data sets. This article introduces an R package MGLM, short for multivariate response generalized linear models, that expands the current tools for regression analysis of polytomous data. Distribution fitting, random number generation, regression, and sparse regression are treated in a unifying framework. The algorithm, usage, and implementation details are discussed
Count data can be analyzed using generalized linear mixed models when observations are correlated in...
This textbook presents an introduction to multiple linear regression, providing real-world data sets...
Classical categorical regression models such as the multinomial logit and proportional odds models a...
This article describes the R package mcglm implemented for fitting multivariate covariance generaliz...
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 ...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
ABSTRACT Objective To demonstrate the efficiency and efficacy of the new freeware MGLMM in the st...
This book is concerned with the use of generalized linear models for univariate and multivariate reg...
This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the gl...
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computa...
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computa...
In the paper we present an R package MNM dedicated to multivariate data analysis based on the L1 nor...
Classical categorical regression models such as the multinomial logit and proportional odds models a...
The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous dat...
Count data can be analyzed using generalized linear mixed models when observations are correlated in...
This textbook presents an introduction to multiple linear regression, providing real-world data sets...
Classical categorical regression models such as the multinomial logit and proportional odds models a...
This article describes the R package mcglm implemented for fitting multivariate covariance generaliz...
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 ...
Generalized linear mixed models provide a flexible framework for modeling a range of data, although ...
ABSTRACT Objective To demonstrate the efficiency and efficacy of the new freeware MGLMM in the st...
This book is concerned with the use of generalized linear models for univariate and multivariate reg...
This paper aims to approach the estimation of generalized linear models (GLM) on the basis of the gl...
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computa...
Modeling multivariate longitudinal data has many challenges in terms of both statistical and computa...
In the paper we present an R package MNM dedicated to multivariate data analysis based on the L1 nor...
Classical categorical regression models such as the multinomial logit and proportional odds models a...
The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous dat...
Count data can be analyzed using generalized linear mixed models when observations are correlated in...
This textbook presents an introduction to multiple linear regression, providing real-world data sets...
Classical categorical regression models such as the multinomial logit and proportional odds models a...