Visualisation of non-linear modelling: A shiny app developed by Daniela Dunkler and Georg Heinze

A shiny app to visualize the possible different functional forms which can be modelled with fractional polynomials, natural (restricted cubic) splines, or linear B-splines. For eleven different explanatory variables, you can visualise non-linear effects applying these popular methods of non-linear modeling. You can visualise first- or second-degree fractional polynomials, linear B-splines with up to 4 degrees of freedom and natural splines with up to 3 degrees of freedom.

R packages on CRAN

  • Regression Modeling Strategies: rms Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling.

  • Augmented Backward Elimination: abe Performs augmented backward elimination and checks the stability of the obtained model. Augmented backward elimination combines significance or information based criteria with the change in estimate to either select the optimal model for prediction purposes or to serve as a tool to obtain a practically sound, highly interpretable model. More details can be found in Dunkler et al. (2014)

  • Multivariable Fractional Polynomials mfp Fractional polynomials are used to represent curvature in regression models. Software page: