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Visualising statistical models using dynamic nomograms

Author

Listed:
  • Amirhossein Jalali
  • Alberto Alvarez-Iglesias
  • Davood Roshan
  • John Newell

Abstract

Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models.

Suggested Citation

  • Amirhossein Jalali & Alberto Alvarez-Iglesias & Davood Roshan & John Newell, 2019. "Visualising statistical models using dynamic nomograms," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-15, November.
    • Handle: RePEc:plo:pone00:0225253
    DOI: 10.1371/journal.pone.0225253
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    References listed on IDEAS

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    1. Alexander Zlotnik & Victor Abraira, 2015. "A general-purpose nomogram generator for predictive logistic regression models," Stata Journal, StataCorp LP, vol. 15(2), pages 537-546, June.
    2. Vanya Van Belle & Ben Van Calster, 2015. "Visualizing Risk Prediction Models," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
    3. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    4. Simon Broadbent, 1954. "Some Uses of the Nomogram in Statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 3(1), pages 33-43, March.
    5. Bowman, Adrian & Crawford, Ewan & Alexander, Gavin & Bowman, Richard W, 2007. "rpanel: Simple Interactive Controls for R Functions Using the tcltk Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i09).
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