Introduction to statistical estimation methods
This two day course introduces essential methods in statistical modelling for ecologists. The course focuses on the construction of biologically sound models and the estimation of parameter values and associated uncertainties. Both maximum-likelihood methods and Bayesian MCMC methods will be introduced. The course will be a mix of lectures and hands on computer exercises and tutoring.
Participants must bring their own laptop with R and OpenBUGS/WinBUGS installed. We will also make use of spread sheets, preferably Excel. We also recommend the TINN-R editor for R. Follow link below to install:
If you are not familiar with R, we ask you to run through this Introduction to R before attending the course. This file is a normal ASCII text file that you can open in TINN-R or the built-in editor in R.
Torbjørn Ergon, Trond Reitan, and T. Andreas Lindén, University of Oslo.
The course will take place in the course and conference unit of Finse Alpine Research Centre. Participants will be housed at the research centre in rooms with two or four beds. Accommodation and all meals are included.
There will be an evening meal at 9pm on Thursday September 9th.
- Lecture: What are models
- Trees - a linear regression exercise
- Basic Maximum Likelihood Estimation [Presentation][Exercise/lecture note]
- Lecture: Uncertainty and confidence intervals and associated R code
- Linear models using matrices [Presentation][Exercise/lecture note][R code]
- Material for day 2
This course is a pre-course to the Nordic Research Training Course Modelling Patterns and Dynamics of Species Occurrence, funded by NordForsk. The course is organised in collaboration between Finse Alpine Research Centre and Centre for Ecological and Evolutionary Synthesis at the University of Oslo.
- Torbjørn Ergon, Principle organizer, Finse Alpine Research Centre
- Jayne Lambrou, Administrator, Centre for Ecological and Evolutionary Synthesis, University of Oslo
- Erika Leslie, Accommodation and catering logistics, Finse Alpine Research Centre