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R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses

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  • Touloumis, Anestis

Abstract

The R package multgee implements the local odds ratios generalized estimating equations (GEE) approach proposed by Touloumis, Agresti, and Kateri (2013), a GEE approach for correlated multinomial responses that circumvents theoretical and practical limitations of the GEE method. A main strength of multgee is that it provides GEE routines for both ordinal (ordLORgee) and nominal (nomLORgee) responses, while relevant other softwares in R and SAS are restricted to ordinal responses under a marginal cumulative link model specification. In addition, multgee offers a marginal adjacent categories logit model for ordinal responses and a marginal baseline category logit model for nominal responses. Further, utility functions are available to ease the local odds ratios structure selection (intrinsic.pars) and to perform a Wald type goodness-of-fit test between two nested GEE models (waldts). We demonstrate the application of multgee through a clinical trial with clustered ordinal multinomial responses.

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  • Touloumis, Anestis, 2015. "R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i08).
    • Handle: RePEc:jss:jstsof:v:064:i08
    DOI: http://hdl.handle.net/10.18637/jss.v064.i08
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    1. N. R. Parsons & R. N. Edmondson & S. G. Gilmour, 2006. "A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 507-524, August.
    2. Yee, Thomas W., 2010. "The VGAM Package for Categorical Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i10).
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    1. Kemmawadee Preedalikit & Daniel Fernández & Ivy Liu & Louise McMillan & Marta Nai Ruscone & Roy Costilla, 2024. "Row mixture-based clustering with covariates for ordinal responses," Computational Statistics, Springer, vol. 39(5), pages 2511-2555, July.
    2. Daniel Fernández & Louise McMillan & Richard Arnold & Martin Spiess & Ivy Liu, 2022. "Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses Based on the Stereotype Model," Stats, MDPI, vol. 5(2), pages 1-14, June.

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