Open AccessVisualising statistical models using dynamic nomograms
Author(s) -
Amirhossein Jalali,
Alberto AlvarezIglesias,
Davood Roshan,
John Newell
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0225253
Subject(s) - statistical model , computer science , nomogram , variety (cybernetics) , smoothing , machine learning , linear model , statistical graphics , graphical model , regression analysis , statistical hypothesis testing , data mining , artificial intelligence , data science , statistics , mathematics , medicine , graphics , computer graphics (images) , computer vision
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.