Greg - the G-forge regression package

This package helps with building and conveying regression models. It has also a few functions for handling robust confidence intervals for the ols() regression in the rms-package. It is closely interconnected with the the Gmisc, htmlTable, and the forestplot packages.

Conveying regression models

Communicating statistical results is in my opinion just as important as performing the statistics. Often effect sizes may seem technical to clinicians but putting them into context often helps and makes you to get your point across.

Crude and adjusted estimates

The method that I most frequently use in this package is the printCrudeAndAdjustedModel. It generates a table that has the full model coefficients together with confidence intervals alongside a crude version using only that single variable. This allows the user to quickly gain insight into how strong each variable is and how it interacts with the full model, e.g. a variable that shows a large change when adding the other variables suggests that there is significant confounding. See the vignette for more details, vignette("Print_crude_and_adjusted_models").

Forest plots for regression models

I also like to use forest plots for conveying regression models. A common alternative to tables is to use a forest plot with estimates and confidence intervals displayed in a graphical manner. The actual numbers of the model may be better suited for text while the graphs quickly tell how different estimates relate.

Sometimes we also have situations where one needs to choose between two models, e.g. a Poisson regression and a Cox regression. This package provides a forestplotCombineRegrObj function that allows you to simultaneously show two models and how they behave in different settings. This is also useful when performing sensitivity analyses and comparing different selection criteria, e.g. only selecting the patients with high-quality data and see how that compares.

Plotting non-linear hazard ratios

The plotHR function was my first attempt at doing something more advanced version based upon Reinhard Seifert’s original adaptation of the stats::termplot function. It has some neat functionality although I must admit that I now often use ggplot2 for many of my plots as I like to have a consistent look throughout the plots. The function has though a neat way of displaying the density of the variable at the bottom.

Modeling helpers

Much of our modeling ends up a little repetitive and this package contains a set of functions that I’ve found useful. The approach that I have for modeling regressions is heavily influenced by Frank Harrell’s regression modeling strategies. The core idea consist of: