Visualising statistical models using dynamic nomograms


Autoři: Amirhossein Jalali aff001;  Alberto Alvarez-Iglesias aff002;  Davood Roshan aff001;  John Newell aff001
Působiště autorů: School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland aff001;  HRB Clinical Research Facility, National University of Ireland, Galway, Ireland aff002;  CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225253

Souhrn

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.

Klíčová slova:

Crabs – Chi square tests – Lung and intrathoracic tumors – Pollen – Statistical models – Wind – Epidemiological statistics


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Článek vyšel v časopise

PLOS One


2019 Číslo 11