{"title":"MGLM: An R Package for Multivariate Categorical Data Analysis.","authors":"Juhyun Kim, Yiwen Zhang, Joshua Day, Hua Zhou","doi":"10.32614/rj-2018-015","DOIUrl":null,"url":null,"abstract":"<p><p>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 <b>MGLM</b>, 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.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286576/pdf/nihms-1562404.pdf","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"R Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.32614/rj-2018-015","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 14
Abstract
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.
具有多重响应的数据在现代应用中无处不在。然而，很少有工具可用于多元计数的回归分析。最流行的多项式-logit模型具有非常严格的均值-方差结构，限制了它对许多数据集的适用性。本文介绍了一个R包MGLM，即多元响应广义线性模型(multivariate response generalized linear models)的缩写，它扩展了当前用于多元数据回归分析的工具。分布拟合、随机数生成、回归和稀疏回归在一个统一的框架中处理。讨论了算法、用法和实现细节。
R JournalCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍：
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.
The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to:
- put their contribution in context, in particular discuss related R functions or packages;
- explain the motivation for their contribution;
- provide code examples that are reproducible.
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