https://www.physalia-courses.org/
Most of the statistical methods you are likely to have encountered will have specified fixed functional forms for the relationships between covariates and the response, either implicitly or explicitly. These might be linear effects or involve polynomials, such as x + x2 + x3. Generalised additive models (GAMs) are different; they build upon the generalised linear model by allowing the shapes of the relationships between response and covariates to be learned from the data using splines. Modern GAMs, it turns out, are a very general framework for data analysis, encompassing many models as special cases, including GLMs and GLMMs, and the variety of types of splines available to users allows GAMs to be used in a surprisingly large number of situations. In this course we’ll show you how to leverage the power and flexibility of splines to go beyond parametric modelling techniques like GLMs.
The course is aimed at graduate students and researchers with limited statistical knowledge; ideally you’d know something about generalised linear models. But we’ll recap what GLMs are so if you’re a little rusty or not everything mentioned in the GLM course makes sense, we have you covered. From running the course previously, knowing the difference between "fixed" and "random" effects, and what the terms "random intercepts" and "random slopes" are, will be helpful for the Hierarchical GAM topic, but we don't expect you to be an expert in mixed effects or hierarchical models to take this course.
Participants should be familiar with RStudio and have some fluency in programming R code, including being able to import, manipulate (e.g. modify variables) and visualise data. There will be a mix of lectures, in-class discussion, and hands-on practical exercises along the course.
- Understand how GAMs work from a practical view point to learn relationships between covariates and response from the data
- Be able to fit GAMs in R using the mgcv and brms packages
- Know the differences between the types of splines and when to use them in your models
- Know how to visualise fitted GAMs and to check the assumptions of the model
Please be sure to have at least version 4.2 — and preferably version 4.3 — of R installed (the version of my gratia package we will be using depends on you having at least version 4.1 installed and some slides might contain code that requires version 4.3). Note that R and RStudio are two different things: it is not sufficient to just update RStudio, you also need to update R by installing new versions as they are release.
To download R go to the CRAN Download page and follow the links to download R for your operating system:
To check what version of R you have installed, you can run
versionin R and look at the version.string entry (or the major and minor entries).
We will make use of several R packages that you'll need to have installed. Prior to the start of the course, please run the following code to update your installed packages and then install the required packages:
# update any installed R packages
update.packages(ask = FALSE, checkBuilt = TRUE)
# packages to install
pkgs <- c("mgcv", "brms", "qgam", "gamm4", "tidyverse", "readxl",
"rstan", "mgcViz", "DHARMa")
# install those packages
install.packages(pkgs, Ncpus = 4) # set Ncpus to # of CPU cores you haveFinally, we will make use of the development version of the gratia package as it is not quite ready for CRAN. You can install this package using the binaries provided by the rOpenSci build service R-Universe. To install from my R-Universe, you need to tell R to also install packages from my R-Universe package repo:
# Download and install gratia
install.packages("gratia",
repos = c("https://gavinsimpson.r-universe.dev", "https://cloud.r-project.org"))Sessions from 14:00 to 20:00 (Monday to Thursday), 14:00 to 19:00 on Friday (Berlin time). From Tuesday to Friday, the first hour will be dedicated to Q&A and working through practical exercises or students’ own analyses over Slack and Zoom. Sessions will interweave mix lectures, in-class discussion/ Q&A, and practical exercises.
- Brief overview of R and the Tidyverse packages we’ll encounter throughout the course
- Recap generalised linear models
- Fitting your first GAM
- How do GAMs work?
- What are splines?
- How do GAMs learn from data without overfitting?
We’ll dig under the hood a bit to understand how GAMs work at a practical level and how to use the mgcv and gratia packages to estimate GAMs and visualise them.
- Model checking, selection, and visualisation.
- How do we do inference with GAMs?
- Go beyond simple GAMs to include smooth interactions and models with multiples smooths.
- Hierarchical GAMs; introducing random smooths and how to model data with both group and individual smooth effects.
- Doing more with your models; introducing posterior simulation.
- Going beyond the mean; fitting distributional models and quantile GAMs
- Fitting Bayesian GAMs with brms