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Materials for the "Generalised Additive Models in R" workshop for Forecasting for Social Good.

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Forecasting with Generalised Additive Models (GAMs) in R

About the workshop

  • Title: Forecasting with Generalised Additive Models (GAMs) in R

  • Date: 21 February 2024

  • Time: 14:00 - 16:00 (GMT)

  • Instructor: Dr Nicola Rennie (Lancaster University)

Resources

Who is the training for?

  • Anyone who is interested in extending their knowledge beyond simple regression models and learning more about generalised additive models.
  • Newcomers to the field of generalised additive models who want to understand their importance and relevance in forecasting.
  • Academics, students, data scientists, researchers, and practitioners in the field who are working with data containing complex nonlinear relationships.
  • People who want to learn how to implement generalised additive models using R.

Learning objectives

By the end of the workshop, participants will:

  • know what generalised additive models are;
  • understand why and when they might be appropriate for certain types of data;
  • be able to fit and evaluate GAMs using the {mgcv} package in R;
  • understand how to interpret the output from fitted models.

Prerequisites

  • Basic knowledge of R.
  • Basic knowledge of statistics and linear modelling.

Outline of the session:

  • This session will provide an overview of generalised additive models (GAMs), demonstrate the practical aspects of fitting such models, and describe how to evaluate and interpret different them.
  • Live demonstrations and hands-on coding exercises will give participants the opportunity to practice implementing models using R.

Outline of the lab sessions:

  • Introduction to the data and the {mgcv} package (15 minutes)
  • Fitting and evaluate GAMs using the {mgcv} package (15 minutes)
  • Forecasting using GAMs (15 minutes)

R packages

Required:

  • {mgcv}
  • {gratia}

Optional:

  • {COVID19}
  • {tidyverse}

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Materials for the "Generalised Additive Models in R" workshop for Forecasting for Social Good.

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