The R Journal: accepted article

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mgee2: An R package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates PDF download
Yuliang Xu, Shuo Shuo Liu and Grace Y. Yi

Abstract Marginal methods have been widely used for analysing longitudinal ordinal data due to their simplicity in model assumptions, robustness in inference results, and easiness in the implementation. However, they are often inapplicable in the presence of measurement error in the variables. Under the setup of longitudinal studies with ordinal responses and covariates subject to misclassification, Chen et al. (2014) developed marginal methods for misclassification adjustments using the second-order estimating equations and proposed a two-stage estimation approach when the validation subsample is available. Parameter estimation is conducted through the Newton-Raphson algorithm and the asymptotic distribution of the estimators is established. While the methods of Chen et al. (2014) can successfully correct the misclassification effects, its implementation is not accessible to general users due to the lack of a software package. In this paper, we develop an R package, mgee2, to implement the marginal methods proposed by Chen et al. (2014). To evaluate the performance and illustrate the features of the package, we conduct numerical studies.

Received: 2021-01-15; online 2021-10-19, supplementary material, (796 bytes)
CRAN packages: misclassGLM, kml, kml3d, gee, wgeesel, swgee, MASS, Matrix, ggplot2
CRAN Task Views implied by cited CRAN packages: Econometrics, Multivariate, NumericalMathematics, SocialSciences, TeachingStatistics, Cluster, Distributions, Environmetrics, Graphics, MissingData, Phylogenetics, Psychometrics, Robust


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This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-093,
  author = {Yuliang Xu and Shuo Shuo Liu and Grace Y. Yi},
  title = {{mgee2: An R package for marginal analysis of longitudinal
          ordinal data with misclassified responses and
          covariates}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-093},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-093/index.html}
}