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ONLINE COURSE – Bayesian GLM’s For Ecologists (BGFE01) This course will be delivered live
20th June 2022 - 22nd June 2022
£350.00
Event Date
Monday, June 20th 2022
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.
Time Zone
TIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
Course Details
This short course is aimed at introducing researchers to analysing ecological and environmental data with Bayesian GLMs using R. Theory underpinning Bayesian inference will be discussed, as well as analytical methods and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, attendees will receive R script and datasets and a list of R packages to install.
By the end of the course, participants should be able to:
- Recognise the distinction between frequentist and Bayesian approaches to model fitting
- Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis
- Apply a 9-step protocol to fitting Bayesian GLMs
- Understand and apply alternative approaches to model selection
- Apply statistical modelling methods to ecological data using Bayesian GLMs
Intended Audiences
Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R
Applied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data
Venue
Delivered remotely
Course Information
Availability – 30 places
Duration – 3 days
Contact hours – Approx. 21 hours
ECT’s – Equal to 2 ECT’s
Language – English
Teaching Format
This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants, and R script will be presented and explained.
Ideally, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously.
Assumed quantitative knowledge
It will be assumed that participants will be familiar with general statistical concepts and fitting GLMs to ecological data. Participants will need experience of performing statistical analysis using R.
Assumed computer background
Experience with performing statistical analyses using R and R Studio 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
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.
COURSE PROGRAMME
Monday 09:00 – 16:00
Introduction to Bayesian inference
- Difference between Bayesian and frequentist approaches
- Bayes’ theorem
- A frequentist or Bayesian framework: Which is better?
- Fitting Bayesian GLMs
- Steps in fitting a Bayesian GLM
- Priors
- Non-informative priors
- Weakly informative priors
- Informative priors
- The posterior distribution
- Bayesian computational methods
- The advantages of Bayesian inference
- Criticism of Bayesian inference
Data exploration
- Six-step data exploration protocol
- Outliers
- Normality and homogeneity of the dependent variable
- Lots of zeros in the response variable
- Multicollinearity among covariates
- Relationships among dependent and independent variables
- Independence of response variable
- Results of data exploration
Gaussian GLM with INLA
- European bitterling territoriality
- State the question
- Selection of a statistical model
- Specification of priors
- Model fitting
- Obtain the posterior distribution
- Conduct model checks
- Interpret and present model output
- Visualise the results
- Presenting results
- Conclusions
Tuesday 09:00 – 16:00
Poisson GLM with INLA
- Stickleback lateral plate number
- State the question
- Selection of a statistical model
- Specification of priors
- Model fitting
- Obtain the posterior distribution
- Conduct model checks
- Interpret and present model output
- Visualise the results
- Presenting results
- Conclusions
Negative binomial GLM with INLA
- Coral abundance
- State the question
- Selection of a statistical model
- Specification of priors
- Model fitting
- Obtain the posterior distribution
- Conduct model checks
- Interpret and present model output
- Visualise the results
- Presenting results
- Conclusions
Bernoulli GLM with INLA
- Cuckoo parasitism of reed warbler nests
- State the question
- Selection of a statistical model
- Specification of priors
- Model fitting
- Obtain the posterior distribution
- Conduct model checks
- Interpret and present model output
- Visualise the results
- Presenting results
- Conclusions
Wednesday 09:00 – 16:00
Gamma GLM with INLA
- Stickleback lateral plate number
- State the question
- Selection of a statistical model
- Specification of priors
- Model fitting
- Obtain the posterior distribution
- Conduct model checks
- Interpret and present model output
- Visualise the results
- Presenting results
- Conclusions
Implementing and assessing Bayesian GLMs
- Prior information
- Presenting results of Bayesian GLMs
- Reviewing Bayesian GLMs
- Misuse of Bayesian GLMs
- Conclusions
Discussion & questions
Course Instructor
Dr. Carl Smith
Teaches:
- Bayesian GLMs for Ecologists (BGFE01)