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Extended Model Formulas in R : Multiple Parts and Multiple Responses

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  • Achim Zeileis

    (Universität Innsbruck [Innsbruck])

  • Yves Croissant

    (UR - Université de La Réunion)

Abstract

Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they have certain limitations including being confined to single responses and not providing convenient support for processing formulas with multiple parts. The latter is relevant for models with two or more sets of variables, e.g., different equations for different model parameters (such as mean and dispersion), regressors and instruments in instrumental variable regressions, two-part models such as hurdle models, or alternative-specific and individual-specific variables in choice models among many others. The R package Formula addresses these two problems by providing a new class Formula (inheriting from formula) that accepts an additional formula operator | separating multiple parts and by allowing all formula operators (including the new |) on the left-hand side to support multiple responses.

Suggested Citation

  • Achim Zeileis & Yves Croissant, 2010. "Extended Model Formulas in R : Multiple Parts and Multiple Responses," Post-Print hal-01245303, HAL.
  • Handle: RePEc:hal:journl:hal-01245303
    DOI: 10.18637/jss.v034.i01
    Note: View the original document on HAL open archive server: https://hal.univ-reunion.fr/hal-01245303
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    References listed on IDEAS

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    1. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    2. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    3. Hothorn, Torsten & Hornik, Kurt & van de Wiel, Mark A. & Zeileis, Achim, 2006. "A Lego System for Conditional Inference," The American Statistician, American Statistical Association, vol. 60, pages 257-263, August.
    4. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
    5. Hothorn, Torsten & Hornik, Kurt & van de Wiel, Mark A. & Zeileis, Achim, 2008. "Implementing a Class of Permutation Tests: The coin Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i08).
    6. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
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    1. repec:jss:jstsof:36:i03 is not listed on IDEAS
    2. Kevin Huynh, 2024. "Weighted-Average Least Squares for Negative Binomial Regression," Papers 2404.11324, arXiv.org.
    3. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    4. Andreea Cambir, 2015. "Factors of life quality material dimension," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 39-56, June.
    5. Sarrias, Mauricio & Daziano, Ricardo, 2017. "Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i02).
    6. Erni, Birgit & Bonnevie, Bo T. & Oschadleus, Hans-Dieter & Altwegg, Res & Underhill, Les G., 2013. "moult: An R Package to Analyze Moult in Birds," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i08).
    7. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
    8. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    9. Zhang, Peng & Qiu, Zhenguo & Shi, Chengchun, 2016. "simplexreg: an R package for regression analysis of proportional data using the simplex distribution," LSE Research Online Documents on Economics 102115, London School of Economics and Political Science, LSE Library.
    10. Silvia Pisica & Nicoleta Caragea, 2015. "Determinants of Labor Force Potential in Romania," Romanian Statistical Review, Romanian Statistical Review, vol. 63(2), pages 104-118, June.
    11. Andoria Ionita, 2015. "Computational consideration for selection of social classes in Romania," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 90-100, September.
    12. Felix Thoemmes & Wang Liao & Ze Jin, 2017. "The Analysis of the Regression-Discontinuity Design in R," Journal of Educational and Behavioral Statistics, , vol. 42(3), pages 341-360, June.
    13. Savitsky, Terrance & Paddock, Susan, 2014. "Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i03).
    14. repec:jss:jstsof:34:i02 is not listed on IDEAS

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