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Behavioural data analysis using maximum likelihood in R (BDML02)
4 November 2019 - 8 November 2019
This 5-day course will involve a combination of lectures and practical sessions. Students will learn to build and fit custom models for analysing behavioural data using maximum likelihood techniques in R. This flexible approach allows a researcher to a) use a statistical model that directly represents their hypothesis, in cases where standard models are not appropriate and b) better understand how standard statistical models (e.g. GLMs) are fitted, many of which are fitted by maximum likelihood. Students will learn how to deal with binary, count and continuous data, including time-to-event data which is commonly encountered in behavioural analysis.
After successfully completing this course students should be able to:
- fit a multi-parameter maximum likelihood model in R
- derive likelihood functions for binary, count and continuous data
- deal with time-to-event data
- build custom models to test specific behavioural hypotheses
- conduct hypothesis tests and construct confidence intervals
- use Akaike’s information criterion (AIC) and model averaging
- understand how maximum likelihood relates to Bayesian techniques
To find out more or to book online via our sister company (PS statistics) use the link below…
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)