IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v064i08.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v064i08/v64i08.pdf
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v064i08/multgee_1.5.1.tar.gz
    Download Restriction: no

    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v064i08/v64i08.R
    Download Restriction: no

    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v064.i08?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Xianfeng & Packer, Frank, 2019. "Credit ratings of Chinese firms by domestic and global agencies: Assessing the determinants and impact," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 178-193.
    2. Xi Liu & Yiqiao Jin & Yifan Yang & Xiaoqing Pan, 2023. "Properties and Estimations of a Multivariate Folded Normal Distribution," Mathematics, MDPI, vol. 11(23), pages 1-15, December.
    3. Michail Tsagris & Christina Beneki & Hossein Hassani, 2014. "On the Folded Normal Distribution," Mathematics, MDPI, vol. 2(1), pages 1-17, February.
    4. Shuai Shao & Göran Kauermann, 2020. "Understanding price elasticity for airline ancillary services," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(1), pages 74-82, February.
    5. Shin Ji-Hyung & McNeney Brad & Graham Jinko & Infante-Rivard Claire, 2014. "A data-smoothing approach to explore and test gene-environment interaction in case-parent trios," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(2), pages 159-171, April.
    6. Thomas Yee & Alfian Hadi, 2014. "Row–column interaction models, with an R implementation," Computational Statistics, Springer, vol. 29(6), pages 1427-1445, December.
    7. Ioannis Kontoyiannis & Lambros Mertzanis & Athina Panotopoulou & Ioannis Papageorgiou & Maria Skoularidou, 2022. "Bayesian context trees: Modelling and exact inference for discrete time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1287-1323, September.
    8. Goedkoop, F. & Dijkstra, J. & Flache, A., 2022. "A social network perspective on involvement in community energy initiatives: The role of direct and extended social ties to initiators," Energy Policy, Elsevier, vol. 171(C).
    9. Jiajun Yan & Shitong Xie & Jeffrey A. Johnson & Eleanor Pullenayegum & Arto Ohinmaa & Stirling Bryan & Feng Xie, 2024. "Canada population norms for the EQ-5D-5L," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(1), pages 147-155, February.
    10. repec:jss:jstsof:39:i12 is not listed on IDEAS
    11. Mahdi Rezapour & F. Richard Ferraro & Sabrina Alsubaiei, 2022. "Behavioral and emotional adaptations of obese and underweight students in response to the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    12. Gerhard Tutz & Moritz Berger, 2022. "Sparser Ordinal Regression Models Based on Parametric and Additive Location‐Shift Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 306-327, August.
    13. Moritz Berger & Thomas Welchowski & Steffen Schmitz-Valckenberg & Matthias Schmid, 2019. "A classification tree approach for the modeling of competing risks in discrete time," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 965-990, December.
    14. Gerhard Tutz & Moritz Berger, 2016. "Response Styles in Rating Scales," Journal of Educational and Behavioral Statistics, , vol. 41(3), pages 239-268, June.
    15. Wei, Zheng & Kim, Daeyoung, 2021. "On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    16. Marie-Therese Puth & Gerhard Tutz & Nils Heim & Eva Münster & Matthias Schmid & Moritz Berger, 2020. "Tree-based modeling of time-varying coefficients in discrete time-to-event models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 545-572, July.
    17. Christian Dudel, 2021. "Expanding the Markov Chain Toolbox: Distributions of Occupation Times and Waiting Times," Sociological Methods & Research, , vol. 50(1), pages 401-428, February.
    18. Angelo Lorenti & Christian Dudel & Mikko Myrskylä, 2019. "The Legacy of the Great Recession in Italy: A Wider Geographical, Gender, and Generational Gap in Working Life Expectancy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 283-303, February.
    19. Hale, Jo Mhairi & Dudel, Christian & Lorenti, Angelo, 2020. "Cumulative disparities in the dynamics of working poverty for later-career U.S. workers (2002-2012)," SocArXiv xka5j, Center for Open Science.
    20. Diego Montano & Richard Peter, 2022. "Informal care-giving and the intention to give up employment: the role of perceived supervisor behaviour in a cohort of German employees," European Journal of Ageing, Springer, vol. 19(3), pages 575-585, September.
    21. P. Brighi & R. Patuelli & G. Torluccio, 2012. "Self-Financing of Traditional and R&D Investments: Evidence from Italian SMEs," Working Papers wp845, Dipartimento Scienze Economiche, Universita' di Bologna.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:064:i08. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.