Behavioural data analysis using maximum likelihood in R (BDML01)
19 March 2018 - 23 March 2018£260 - £475
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
Any researchers (from postgraduate students to senior investigators) interested in analysing behavioural data. Examples will be primarily from non-human animal behaviour studies, but the methods will also be applicable to many researchers studying human behaviour. The course is intended for those wishing to construct custom statistical models and for those wishing to better understand the workings of standard statistical techniques that use maximum likelihood methods (e.g. GLMs).
We offer two packages
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE – (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments, minibus to and from meeting point and accommodation. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms.
Arrival Sunday 18th March and departure Friday 23rd March PM.
Other payment options are available please email email@example.com
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 firstname.lastname@example.org Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PS statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PS statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
There will be a combination of lectures and practicals. Practicals will be based on the topics covered in the preceding lectures. Data sets for computer practicals will be provided by the instructors
Assumed quantitative knowledge
A basic understanding of statistical concepts (mean, variance, correlation, regression, ANOVA etc.) and probability.
Assumed computer background
Some familiarity with R. Ability to import/export and manipulate data, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version of R or RStudio. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) as internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Meet at the Tullie Inn, Balloch at approximately 18:30 before being taken by minibus to SCENE (Download directions PDF).
Monday 19th – Classes from 09:00 to 17:00
Module 1: The process of statistical inference and the role of statistical models. Why learn likelihood techniques? Course outline
Module 2: Maximum likelihood estimation: single parameter models for binary data
Tuesday 20th – Classes from 09:00 to 17:00
Module 3: Models with several parameters for binary data, optimization algorithms
Module 4: Testing hypotheses and constructing confidence intervals
Wednesday 21st – Classes from 09:00 to 17:00
Module 5: Modelling count data and the Poisson distribution
Module 6: Modelling continuous data, the normal distribution and the relationship of maximum likelihood to least squares
Thursday 22nd – Classes from 09:00 to 17:00
Module 7: Modelling time to event data and the exponential distribution
Module 8: Akaike’s information criterion (AIC) and model averaging
Friday 23rd – Classes from 09:00 to 16:00
Module 9: A brief introduction to Bayesian analysis, the practical advantages, and its relationship to maximum likelihood
Afternoon: Trouble shooting and final summary