Pre Recorded
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.
Last Up-Dated – 11:12:2020
Duration – Approx. 30 hours
ECT’s – Equal to 2 ECT’s
Language – English
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
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.
A basic understanding of regression methods and generalised linear models.
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
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
Approx 8 hours
Module 1: Simple hierarchical regression models
Module 2: Hierarchical models for non-Gaussian data
Practical: Fitting hierarchical models
Approx 8 hours
Module 3: Simple hierarchical regression models
Module 4: Hierarchical models for non-Gaussian data
Practical: Fitting hierarchical models
Approx 8 hours
Module 5: Hierarchical models vs mixed effects models
Module 6: Multivariate and multi-layer hierarchical models
Practical: Advanced examples of hierarchical models
Approx 8 hours
Module 7: Shrinkage and variable selection
Module 8: Hierarchical models and partial pooling
Practical: Shrinkage modelling
Works at - Hamilton Institute, Maynooth University
Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science, Nature Communications, and Proceedings of the National Academy of Sciences, and has methodological publications in journals such as Statistics and Computing, Journal of Computational and Graphical Statistics, The Annals of Applied Statistics, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics, time series modelling, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change, 3D printing, and bioinformatics.
Classes from 09:00 to 17:00
Theory – Introduction to GIS.
Practical – Introduction to GIS with R: Import and plot data.
Theory – Coordinate systems.
Practical – Projecting vectorial & raster files.
Stable Isotope MIxing Models Using R (SIMM)
Introduction to Bayesian Hierarchical Modelling (IBHM)
Time Series Data Analysis Using R (TSDA)
Missing Data Analytics Using R (MDAR)