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The VGAM Package for Categorical Data Analysis

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  • Thomas W. Yee
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    Abstract

    Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such as reduced-rank VGLMs for dimension reduction, and allowing covariates that have values specific to each linear/additive predictor, e.g., for consumer choice modeling. This article describes some of the framework behind the VGAM R package, its usage and implementation details.

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    File URL: http://www.jstatsoft.org/v32/i10/paper
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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 32 ()
    Issue (Month): i10 ()
    Pages:

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    Handle: RePEc:jss:jstsof:32:i10

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    Web page: http://www.jstatsoft.org/

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    References

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    1. David Meyer & Achim Zeileis & Kurt Hornik, . "The Strucplot Framework: Visualizing Multi-way Contingency Tables with vcd," Journal of Statistical Software, American Statistical Association, vol. 17(i03).
    2. David Firth, . "Bradley-Terry Models in R," Journal of Statistical Software, American Statistical Association, vol. 12(i01).
    3. Ioannis Kosmidis & David Firth, 2009. "Bias reduction in exponential family nonlinear models," Biometrika, Biometrika Trust, vol. 96(4), pages 793-804.
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    Cited by:
    1. Oh, Man-Suk, 2014. "Bayesian test on equality of score parameters in the order restricted RC association model," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 147-157.
    2. Paola Brighi & Roberto Patuelli & Giuseppe Torluccio, 2012. "Self-Financing of Traditional and R&D Investments: Evidence from Italian SMEs," Working Paper Series 61_12, The Rimini Centre for Economic Analysis.
    3. Kaza, Nikhil & Towe, Charles A. & Ye, Xin, 2011. "A Hybrid Land Conversion Model Incorporating Multiple End Uses," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 40(3), December.
    4. Stoklosa, Jakub & Huggins, Richard M., 2012. "A robust P-spline approach to closed population capture–recapture models with time dependence and heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 408-417.
    5. Carolyn Anderson, 2013. "Multidimensional Item Response Theory Models with Collateral Information as Poisson Regression Models," Journal of Classification, Springer, vol. 30(2), pages 276-303, July.
    6. Tsagris, Michail & Beneki, Christina & Hassani, Hossein, 2013. "On the Folded Normal Distribution," MPRA Paper 53748, University Library of Munich, Germany.
    7. Krämer, Nicole & Brechmann, Eike C. & Silvestrini, Daniel & Czado, Claudia, 2013. "Total loss estimation using copula-based regression models," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 829-839.
    8. Yee, Thomas W., 2014. "Reduced-rank vector generalized linear models with two linear predictors," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 889-902.

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