TIME ZONE – Western European Time – 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 overview:
This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions.
Email oliverhooker@prstatistics.com with any questions
Monday 19th – Classes from 09:30 to 17:30
Basic concepts.
Module 1: Introduction; why use a SIMM?
Module 2: An introduction to bayesian statistics.
Module 3: Differences between regression models and SIMMs.
Practical: Revision on using R to load data, create plots and fit statistical models.
Round table discussion: Understanding the output from a Bayesian model.
Tuesday 20th – Classes from 09:30 to 17:30
Understanding and using SIAR.
Module 4: Do’s and Don’ts of using SIAR.
Module 5: The statistical model behind SIAR.
Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots.
Round table discussion: Issues when using simple SIMMs.
Wednesday 21st – Classes from 09:30 to 17:30
SIBER and MixSIAR.
Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER).
Module 7: What are the differences between SIAR and MixSIAR?
Practical: Using MixSIAR on real world data sets; benefits over SIAR.
Round table discussion: When to use which type of SIMM.
Thursday 22nd – Classes from 09:30 to 17:30
Advanced SIMMs.
Module 8: Using MixSIAR for complex data sets: time series and mixed effects models.
Module 9: Source grouping: when and how?
Module 10: Building your own SIMM with JAGS.
Practical: Running advanced SIMMs with JAGS.
Round table discussion: Bring your own data set.
Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR
https://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm07/
TIME ZONE – Western European Time – 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 overview:
This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions.
Email oliverhooker@prstatistics.com with any questions
Monday 19th – Classes from 09:30 to 17:30
Basic concepts.
Module 1: Introduction; why use a SIMM?
Module 2: An introduction to bayesian statistics.
Module 3: Differences between regression models and SIMMs.
Practical: Revision on using R to load data, create plots and fit statistical models.
Round table discussion: Understanding the output from a Bayesian model.
Tuesday 20th – Classes from 09:30 to 17:30
Understanding and using SIAR.
Module 4: Do’s and Don’ts of using SIAR.
Module 5: The statistical model behind SIAR.
Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots.
Round table discussion: Issues when using simple SIMMs.
Wednesday 21st – Classes from 09:30 to 17:30
SIBER and MixSIAR.
Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER).
Module 7: What are the differences between SIAR and MixSIAR?
Practical: Using MixSIAR on real world data sets; benefits over SIAR.
Round table discussion: When to use which type of SIMM.
Thursday 22nd – Classes from 09:30 to 17:30
Advanced SIMMs.
Module 8: Using MixSIAR for complex data sets: time series and mixed effects models.
Module 9: Source grouping: when and how?
Module 10: Building your own SIMM with JAGS.
Practical: Running advanced SIMMs with JAGS.
Round table discussion: Bring your own data set.