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ONLINE COURSE – Spatial and Spatial-Temporal Modelling Using R-INLA (SSTM01) This course will be delivered live
29 January 2024 - 2 February 2024
£500.00
Event Date
Monday, January 29th, 2024
Course Format
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.
Course Program
TIME ZONE – UTC+2 – 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).
Course Details
The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata.
By the end of the course participants should:
- Know the different types of spatial and spatio-temporal data available and how to work with them in R.
- Know the different modelling approaches for spatial and spatio-temporal data.
- Know how to visualize and produce maps of spatial and spatio-temporal data.
- Be able to fit models with the R-INLA package.
- Know how to interpret the output from model fitting.
- Be confident with the use of INLA for data analysis.
- Understand the different models that can be fit with INLA to spatial and spatio-temporal data.
- Know how to define the different parts of a model with INLA.
- Have the confidence to use INLA for their own projects.
Intended Audiences
Academics and post-graduate students working on projects related to spatial and spatio-temporal data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox.
Applied researchers and analysts in public, private or third-sector organizations who need the reproducibility, speed and flexibility of a command-line language such as R.
The course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally, they should have some background on probability, statistics and data analysis.
Venue
Course Information
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 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 oliverhooker@prstatistics.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.
Teaching Format
he course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data.
Assumed quantitative knowledge
The course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference.
Assumed computer background
Attendees should already have experience with R and be familiar with data from different formats (csv, tab, etc.), create simple plots, and manipulate data frames. Furthermore, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful.
Equipment and software requirements
A laptop/personal computer with any operating system (Linux, Windows, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed.
https://cran.r-project.org/
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com
Assumed quantitative knowledge
Although an introduction to the INLA method will be given, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors, MCMC methods, etc.).
Assumed computer background
Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed.
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
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 oliverhooker@prstatistics.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.
If you are unsure about course suitability, please get in touch by email to find out more
Course Programme
Monday 29th
Classes from 14:00 to 21:00 CET
DAY 1
LECTURE 1 – Intro to INLA
PRACTICAL 1 – Intro to INLA
LECTURE 2 – Model fitting with INLA
PRACTICAL 2 – Model fitting with INLA
LECTURE 3 – GLMM’s with INLA
PRACTICAL 3 – GLMM’s with INLA
Q and A and end of day summary
Tuesday 30th
Classes from 14:00 to 21:00 CET
DAY 2
LECTURE 4 – Spatial Data
PRACTICAL 4 – Spatial Data
LECTURE 5 – Spatio-Temporal Data
PRACTICAL 5 – Spatio-Temporal Data
LECTURE 6 – Advanced Visualisation
PRACTICAL 6 – Advanced Visualisation
Q and A and end of day summary
Wednesday 31st
Classes from 14:00 to 21:00 CET
DAY 3
LECTURE 7 – Spatial Models for Lattice Data
PRACTICAL 7 – Spatial Models for Lattice Data
LECTURE 8 – Spatial Models for Continuous Data
PRACTICAL 8 – Spatial Models for Continuous Data
LECTURE 9 – Spatial Models for Point Patterns
PRACTICAL 9 – Spatial Models for Point Patterns
Q and A and end of day summary
Thursday 1st
Classes from 14:00 to 21:00 CET
DAY 4
LECTURE 10 – Spatio-Temporal Models for Lattice Data
PRACTICAL 10 – Spatio-Temporal Models for Lattice Data
LECTURE 11 – Spatio-Temporal Models for Continuous Data
PRACTICAL 11 – Spatio-Temporal Models for Continuous Data
LECTURE 12 – Spatio-Temporal Models for Point Patterns
PRACTICAL 12 – Spatio-Temporal Models for Point Patterns
Q and A and end of day summary
Friday 2nd
Classes from 14:00 to 21:00 CET
DAY 5
Case studies, own data and problem solving.
