mfp2 implements multivariable fractional polynomial (MFP) models and various
extensions. It allows the selection of variables and functional forms when
modelling the relationship of a data matrix x and some outcome y. Currently, it
supports generalized linear models and Cox proportional hazards models.
Additionally, it has the ability to model a sigmoid relationship between covariate x and an outcome variable y
using approximate cumulative distribution (ACD) transformation- a feature that a standard fractional polynomial function cannot achieve.
mfp2 closely emulates the functionality of the mfp and mfpa package in Stata.
It augments the functionality of the existing mfp package in R by:
- a matrix and a formula interface for input
- sigmoid transformations via the ACD transformation
- estimation and plotting of contrasts and partial linear predictors to investigate and visualize non-linear effects
- various optimizations to increase speed and user friendliness
# Install the development version from GitHub
# install.packages("pak")
pak::pak("EdwinKipruto/mfp2")
# or
# install.packages("remotes")
remotes::install_github("EdwinKipruto/mfp2")
To learn more about the MFP algorithm, a good place to start is the book by Royston, P. and Sauerbrei, W., 2008. Multivariable Model - Building: A Pragmatic Approach to Regression Analysis based on Fractional Polynomials for Modelling Continuous Variables. John Wiley & Sons.
For insights into the ACD transformation, please refer to Royston (2014). A smooth covariate rank transformation for use in regression models with a sigmoid dose–response function. The Stata Journal