Data 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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-015.zip
MGLM, VGAM, glmnet, dirmult, parallel, isoform, glmc
Distributions, Survival, Econometrics, Environmetrics, ExtremeValue, MachineLearning, Multivariate, Psychometrics, SocialSciences
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For attribution, please cite this work as
Kim, et al., "The R Journal: MGLM: An R Package for Multivariate Categorical Data Analysis", The R Journal, 2018
BibTeX citation
@article{RJ-2018-015,
author = {Kim, Juhyun and Zhang, Yiwen and Day, Joshua and Zhou, Hua},
title = {The R Journal: MGLM: An R Package for Multivariate Categorical Data Analysis},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-015},
doi = {10.32614/RJ-2018-015},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {73-90}
}