Date: 9 – 13 May, 2022. Time: 08.30 – 16.00 each day. Location: NMBU, Ås, Norway
Mixed-effect modelling is important for modelling based on data with various kinds of grouped structures. Examples include data from clustered sample plots in forest inventories, longitudinal data where observations have been made on the same objects at repeated occasions, and hierarchical data structures where, e.g., trees on plots are the study objects. Similar data structures are also common in other disciplines than forest inventory. The effect of groups can be modelled either fixed or random. The core of expected learning outcomes is which effect to choose in the modelling approach. This course provides an introduction to the mixed-effect modelling theory starting with a theory on modelling with categorical variables through generalized linear mixed-effect modelling when non-gaussian assumptions are employed. The course gives examples of practical usage and intuition on to how the modelling methods are applied.
Upon successful completion of the course, participants will be able to understand general concepts of regression analysis with grouped data. The course is intended for students and researchers in ecology, natural resources, forestry, agriculture and environmental sciences.
The course will include the pre-course self-study through literature reading, and the post-course home exam.
- Pre-course assignment 10 hours
- Working in the classes 35 hours
- Home exam after the course 30 hours
Expected number of participants in the course is 25 students.
The language is English.
- Working with categorical data
- Linear mixed-effect models
- Mixed or random effect: which to apply
- Non-linear mixed-effect models
- Generalized linear mixed-effect models
9 - 13 May, 2022. 08.30 - 16.00 each day.
Knowledge and understanding
- outline general concepts of modelling with grouped data sets;
- explain a difference between random and fixed effects;
- perform regression analysis applying linear mixed-effect models, nonlinear mixed effect models, and generalized linear mixed-effect models.
Skills and abilities
4. regression analysis with grouped data.
The course will consist of lectures mixed with exercises. In the end of the course, students will be given the home exam tasks, which should be seen as a repetition of previously given material.
The grading for the course is Pass / Fail.
The course is primarily intended for PhD students, but post-doctoral researchers are also welcome to attend.
NMBU, Ås, Norway
Mehtätalo,L. & Lappi, J. (2020). Biometry for forestry and environmental data: With examples in R. CRC press.
The course will based on R statistical software.
The computer exercises will be done in the lecture room using private laptop computers.
Before the course begins, students are expected to spend time on self-study amounting to at least 10 hours through reading Chapters 4 and 5 in “Biometry for forestry and environmental data: With examples in R” by Lauri Mehtätalo,L. Juha Lappi.
It is recommended that participants have a basic knowledge in regression analysis and statistics, e.g. acquired through participation in a basic course at the Master level.
Hans Ole Ørka
The course is free for all NOVA-affiliated participants. Registration fee for non-NOVA-affiliated participants: 2000 NOK. All meals are included for all participants. To register for the course use the link: https://www.deltager.no/event/nova_-_mixed_effects_models_09052022
It is possible to book accommodation at the students' dorms for 585 NOK pr. day/person, including bed linen, towels and cleaning on departure. See registration link.
Hans Ole Ørka (course responsible) email@example.com