RMS Short Course Supplemental Material

rms Package

Purpose

• Make everyday statistical modeling easier to do
• Make modern statistical methods easy to incorporate into everyday work
• Make it easy to use the bootstrap to validate models
• Provide "model presentation graphics"

Chapter 1: Why Regression?

• Prediction, capitalizing on efficient estimation methods such as maximum likelihood and the predominant additivity in a variety of problems
  • E.g.: effects of age, smoking, and air quality add to predict lung capacity
  • When effects are predominantly additive, or when there aren't too many interactions and one knows the likely interacting variables in advance, regression can beat machine learning techniques that assume interaction effects are likely to be as strong as main effects
• Ability to separate effects of variables (especially exposure and treatment)
• Hypothesis testing
• Deep understanding of uncertainties associated with all model components
  • Simplest example: confidence interval for the slope of a predictor
  • Confidence intervals for predicted values; simultaneous confidence intervals for a series of predicted values
    • E.g.: confidence band for y over a series of x's