DIY fractional polynomials

Abstract

Fractional polynomial models are a simple yet very useful extension of ordinary polynomials. They greatly increase the available range of nonlinear functions and are often used in regression modeling, both in univariate format (using Stata's fracpoly command) and in multivariable modeling (using mfp). The standard implementation in fracpoly supports a wide range of single-equation regression models but can not cope with the more complex and varied syntaxes of other types of multi-equation models. In this talk, I show that if you are willing to do some straightforward do-file programming, you can apply fractional polynomials in a bespoke manner to more complex Stata regression commands and get useful results. I illustrate the approach in multilevel modeling of longitudinal fetal-size data using xtmixed and in a seemingly unrelated regression analysis of a dataset of academic achievement using sureg.