- This event has passed.
ONLINE COURSE – A Non Mathematical Introduction To Ordination Methods Using R (ORDM01) Registration deadline 27th February – This course will be delivered live
27 March 2023 - 30 March 2023
£400.00
Event Date
Monday, March 27th, 2023
COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructors 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 – EST – 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.
About This Course
By the end of the course, participants should be able to:
- Understand how each method works and the assumptions inherent in each;
- Choose the most appropriate method relative to their data and goals;
- Carry out the analyses in the R statistical environment
- Interpret their results
Intended Audiences
- Graduate or post-doctoral level researchers who wish to learn how to perform ordination techniques in R;
- Applied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data
Venue
Delivered remotely
Course Details
Time Zone – EST
Availability – TBC
Duration – 4 days
Contact hours – Approx. 30 hours
ECT’s – Equal to 3 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
I assume that participants have a basic knowledge of general statistical concepts and of linear models.
Assumed computer background
Experience with performing statistical analyses using R and R Studio will be assumed.
Equipment and software requirements
A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers.
A full list of required packages will be made available to participants prior to the course.
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.
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 oliverhooker@prstatistics.com
COURSE PROGRAMME
Monday 27th
Principal components analysis (PCA)
- A graphical explanation of how PCA works
- Data preparation and basic assumptions
- Dealing with proportions
- Using a covariance matrix or a correlation matrix?
- Steps in fitting a PCA
- Evaluating the importance of each axis
- Relating variables to the axes
- Relating observations to the axes
- Choosing which axes to use
- Graphical visualizations using biplots
- Direct gradient analysis in ecology
- CA as a form of direct gradient analysis
- Steps in fitting a CA
- Bias due to the “arch effect” and its correction by detrending
- An empirical example
- Interpreting the output and graphical presentation
Correspondence analysis (CA) & detrended correspondence analysis (DCA)
Tuesday 28th
Principal coordinates analysis (PCoA)
- Distance and dissimilarity measures
- Measures for nominal categorical, binary, ordinal and quantitative variables
- Gower’s distance
- PCA and CA as special cases of multidimensional scaling
- A graphical explanation of how PCoA works
- Steps in fitting a CA
- Performing PCoA in R
- What is multidimensional scaling and how does it work?
- What is non-metric multidimensional scaling?
- Performing NMDS in R.
- Graphical methods for evaluating and interpreting NMDS results
- Procrustes analysis
- An empirical example
Metric (MDS) and non-metric multidimensional scaling (NMDS)
Wednesday 29th
Day 3 09:00 – 16:00
Constrained ordinations
Thursday 30th
Day 4 09:00 – 12h00
Course Instructor
Prof. John (Bill) Shipley
Bill Shipley is an experienced researcher and teacher in plant ecology and statistical ecology. He has published four scientific monographs and over 170 peer-reviewed papers.