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ONLINE COURSE – Bayesian Approaches to Regression and Mixed Effects Models using R and brms (BARM01) This course will be delivered live
26 May 2021 - 27 May 2021£275.00
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
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 – UK local time (GMT+0) – 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 firstname.lastname@example.org for full details or to discuss how we can accommodate you).
Bayesian methods are now increasingly widely used for data analysis based on linear and generalized linear models,
and multilevel and mixed effects models. The aim of this course is to provide a solid introduction to Bayesian
approaches to these topics using R and the brms package. Ultimately, in this course, we aim to show how Bayesian
methods provide a very powerful, flexible, and extensible approach to general statistical data analysis. We begin by
covering Bayesian approaches to linear regression. We will compare and contrast, in both practical and theoretical
terms, the Bayesian approach and classical approach to linear regression. This will allow us to easily identify the major
similarities and major differences, both in terms of concepts and practice, between the Bayesian and classical
approaches. We will then proceed to Bayesian approaches to generalized linear models, including binary logistic
regression, ordinal logistic regression, Poisson regression, zero-inflated models, etc. In this coverage, we will see the
very wide range of models to which Bayesian methods can be easily applied. Finally, we will cover Bayesian approaches
to multilevel and mixed effects models. Here again, we will see how Bayesian methods allow us to easily extend
traditionally used methods like linear and generalized linear mixed effects models. We will also see how Bayesian
methods allow us to control model complexity and solve algorithmic problems (e.g. model convergence problems) that
can plague classical approaches to multilevel and mixed effects models. Throughout this course, we will be using, via
the brms package, Markov Chain Monte Carlo (MCMC) methods. However, full technical details of MCMC will will be
described here, but will be provided in subsequent Bayesian data analysis courses.
THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES
This course is aimed at anyone who is in interested in using Bayesian approaches to regression, multilevel, and mixed
effects models in any area of science, including the social sciences, life sciences, physical sciences. No prior experience
or familiarity with Bayesian statistics is required.
Venue – Delivered remotely
Time zone – GMT+0
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
Dr. Mark Andrews
Works at – Senior Lecturer, Psychology Department, Nottingham Trent University, England
Teaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R; Generalised Linear, Nonlinear and General Additive Models; Python for data science, machine learning, and scientific computing
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.
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.
The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time (GMT+0) at:
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session.
If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible. For example, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
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 inferential statistics concepts like hypothesis testing and statistical significance, and some practical experience with linear regression, logistic regression, mixed effects models.
Assumed computer background
Some experience and familiarity with R is required. However, although we will be using R extensively, all the code that we use will be made available, and so attendees will usually just need to copy and paste and add minor modifications to this code.
Equipment and software requirements
A 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. In addition to R and RStudio, some R packages, particularly brms, are required. Instructions on how to install R/RStudio and all required R packages will be provided before the course begins.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Wednesday 26th – Classes from 12:00 to 20:00
Topic 1: Bayesian linear models. We begin by covering Bayesian linear regression. For this, we will use the brm
command from the brms package, and we will compare and contrast the results with the standard lm command.
By comparing and contrasting brm with lm we will see all the major similarities and differences between the
Bayesian and classical approach to linear regression. We will, for example, see how Bayesian inference and
model comparison works in practice and how it differs conceptually and practically from inference and model
comparison in classical regression. As part of this coverage of linear models, we will also use categorical
predictor variables and explore varying intercept and varying slope linear models.
Topic 2: Extending Bayesian linear models. Classical normal linear models are based on strong assumptions that
do not always hold in practice. For example, they assume a normal distribution of the residuals, and assume
homogeneity of variance of this distribution across all values of the predictors. In Bayesian models, these
assumptions are easily relaxed. For example, we will see how we can easily replace the normal distribution of
the residuals with a t-distribution, which will allow for a regression model that is robust to outliers. Likewise, we can model the variance of the residuals as being dependent on values of predictor variables.
Thursday 27th – Classes from 12:00 to 20:00
Topic 3: Bayesian generalized linear models. Generalized linear models include models such as logistic
regression, including multinomial and ordinal logistic regression, Poisson regression, negative binomial
regression, zero-inflated models, and other models. Again, for these analyses we will use the brms package and
explore this wide range of models using real world data-sets. In our coverage of this topic, we will see how
powerful Bayesian methods are, allowing us to easily extend our models in different ways in order to handle a
variety of problems and to use assumptions that are most appropriate for the data being modelled.
Topic 4: Multilevel and mixed models. In this section, we will cover the multilevel and mixed effects variants of
the regression models, i.e. linear, logistic, Poisson etc, that we have covered so far. In general, multilevel and mixed effects models arise whenever data are correlated due to membership of a group (or group of groups, and
so on). For this, we use a wide range of real-world data-sets and problems, and move between linear, logistic,
etc., models are we explore these analyses. We will pay particular attention to considering when and how to use
varying slope and varying intercept models, and how to choose between maximal and minimal models. We will
also see how Bayesian approaches to multilevel and mixed effects models can overcome some of the technical
problems (e.g. lack of model convergence) that beset classical approaches.