Nonlinear Regression Using Generalized Additive Models (GAMR01R)
5th May 2025 - 6th May 2025
£220.00
Course Format
Pre Recorded
About This Course
This course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Intended Audiences
Course Details
Last Up-Dated – 17:12:2020
Duration  – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English
Teaching Format
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.
Assumed quantitative knowledge
We assume familiarity with linear regression analysis, and the major concepts of classical inferential statistics (p-values, hypothesis testing, confidence intervals, model comparison, etc). Some familiarity with common generalized linear models such as logistic or Poisson regression will also be assumed.
Assumed computer background
R experience is desirable but not essential. Although we will be using R extensively, all the code that we use will be made available, and so attendees will just to add minor modifications to this code. Attendees should install R and RStudio on their own computers before the workshops, and have some minimal familiarity with the R environment.
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Day 1 – approx. 6 hours
Topic 1: Regression modelling overview. We begin with a brief overview and summary of regression modelling in general. The purpose of this is to provide a brief recap of general and generalized linear models, and to show how nonlinear regression fits into this very widely practiced framework.
Topic 2: Polynomial regression. Polynomial regression is both a conceptually and practically simple extension of linear modelling and so provides a straightforward and simple means to perform nonlinear regression. Polynomial regression also leads naturally to the concept of basis function function regression and thus is bridge between the general or generalized linear models and nonlinear regression modelling using generalized additive models.
Topic 3: Spline and basis function regression: Nonlinear regression using splines is a powerful and flexible non-parametric or semi-parametric nonlinear regression method. It is also an example of a basis function regression method. Here, we will cover spline regression using the splines::bs and splines::ns functions that can be used with lm, glm, etc. We also look at regression using radial basis functions, which is closely related to spline regression. Understanding basis functions is vital for understanding Generalized Additive Models.
Day 2 – approx. 6 hours
Topic 4: Generalized additive models. We now turn to the major topic of generalized additive models (GAMs). GAMs generalize many of concepts and topics covered so far and represent a powerful and flexible framework for nonlinear modelling. In R, the mgcv package provides a extensive set of tools for working with GAMs. Here, we will provide an in-depth coverage of mgcv including choosing smooth terms, controlling overfitting and complexity, prediction, model evaluation, and so on.
Topic 5: Interaction nonlinear regression: A powerful feature of GAMs is the ability to model nonlinear interactions, whether between two continuous variables, or between one continuous and one categorical variable. Amongst other things, interactions between continuous variables allow us to do spatial and spatio-temporal modelling. Interactions between categorical and continuous variables allow us to model how nonlinear relationships between a predictor and outcome change as a function of the value of different categorical variables.
Topic 6: Generalized additive mixed models. GAMs can also be used in linear mixed effects, aka multilevel, models where they are known as generalized additive mixed models (GAMMs). GAMMs can also be used with the mgcv package.
Day 1
Approx. 6 Hours
Topic 1: Regression modelling overview. We begin with a brief overview and summary of regression modelling in general. The purpose of this is to provide a brief recap of general and generalized linear models, and to show how nonlinear regression fits into this very widely practiced framework.
Topic 2: Polynomial regression. Polynomial regression is both a conceptually and practically simple extension of linear modelling and so provides a straightforward and simple means to perform nonlinear regression. Polynomial regression also leads naturally to the concept of basis function function regression and thus is bridge between the general or generalized linear models and nonlinear regression modelling using generalized additive models.
Topic 3: Spline and basis function regression: Nonlinear regression using splines is a powerful and flexible non-parametric or semi-parametric nonlinear regression method. It is also an example of a basis function regression method. Here, we will cover spline regression using the splines::bs and splines::ns functions that can be used with lm, glm, etc. We also look at regression using radial basis functions, which is closely related to spline regression. Understanding basis functions is vital for understanding Generalized Additive Models.
Day 2
Approx. 6 Hours
Topic 4: Generalized additive models. We now turn to the major topic of generalized additive models (GAMs). GAMs generalize many of concepts and topics covered so far and represent a powerful and flexible framework for nonlinear modelling. In R, the mgcv package provides a extensive set of tools for working with GAMs. Here, we will provide an in-depth coverage of mgcv including choosing smooth terms, controlling overfitting and complexity, prediction, model evaluation, and so on.
Topic 5: Interaction nonlinear regression: A powerful feature of GAMs is the ability to model nonlinear interactions, whether between two continuous variables, or between one continuous and one categorical variable. Amongst other things, interactions between continuous variables allow us to do spatial and spatio-temporal modelling. Interactions between categorical and continuous variables allow us to model how nonlinear relationships between a predictor and outcome change as a function of the value of different categorical variables.
Topic 6: Generalized additive mixed models. GAMs can also be used in linear mixed effects, aka multilevel, models where they are known as generalized additive mixed models (GAMMs). GAMMs can also be used with the mgcv package.
Course Instructor
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- Dr. Mark Andrews
Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England- Teaches
- Free 1 day intro to r and r studio (FIRR)
- Introduction To Statistics Using R And Rstudio (IRRS03)
- Introduction to generalised linear models using r and rstudio (IGLM)
- Introduction to mixed models using r and rstudio (IMMR)
- Nonlinear regression using generalized additive models (GAMR)
- Introduction to hidden markov and state space models (HMSS)
- Introduction to machine learning and deep learning using r (IMDL)
- Model selection and model simplification (MSMS)
- Data visualization using gg plot 2 (r and rstudio) (DVGG)
- Data wrangling using r and rstudio (DWRS)
- Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
- Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
- Bayesian data analysis (BADA)
- Bayesian approaches to regression and mixed effects models using r and brms (BARM)
- Introduction to stan for bayesian data analysis (ISBD)
- Introduction to unix (UNIX01)
- Introduction to python (PYIN03)
- Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
- Machine learning and deep learning using python (PYML03)
- Python for data science, machine learning, and scientific computing (PDMS02)
Personal website
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.