• Generalized Additive Models
  • Preface
  • Part I: Concepts
  • Introduction
    • Beyond the General Linear Model I
      • General Linear Model
      • Generalized Linear Model
      • Generalized Additive Model
    • Beyond the General Linear Model II
      • Fitting the Standard Linear Model
      • Polynomial Regression
      • Scatterplot Smoothing
      • Generalized Additive Models
    • Summary
  • The case for GAMs
    • Why not just use standard methods?
    • Heteroscedasticity, non-normality etc.
    • Polynomial Regression
      • A more complex relationship
  • Building up to GAMs
    • Piecewise polynomial
    • What is a GAM?
    • Polynomial spline
  • Part II: Praxis
  • Application Using R
    • Initial Examination
    • Single Feature
      • Linear Fit
      • GAM
      • Visualization
      • Model Comparison
    • Multiple Features
      • Linear Fit
      • GAM
      • Visualization
      • Model Comparison
  • Issues
    • Estimation
      • Shrinkage & Variable Selection
    • Choice of Smoothing Function
    • Diagnostics
      • Concurvity
    • Prediction
    • Model Comparison Revisited
    • Big Data
  • Other Approaches
    • Other Nonlinear Modeling Approaches
      • Known Functional Form
      • Response Transformation
      • The Black Box
    • Bayesian Estimation
    • Extensions
      • Other GAMs
      • Reproducing Kernel Hilbert Space
      • Gaussian Processes
  • Concluding remarks
  • Part III: Addendum
  • Technical details
    • GAM
      • Penalized regression
      • Effective degrees of freedom again
    • A detailed example
      • Preview of other bases
    • The number of knots and where to put them
    • Interpreting output for smooth terms
      • Effective degrees of freedom
      • Deviance explained
      • Visual depiction
      • Examining first derivatives
  • Appendix
    • R packages
    • A comparison to mixed models
    • Time and Space
      • Time
      • Space
  • References
  • MC logo

Generalized Additive Models

Generalized Additive Models

Michael Clark
m-clark.github.io