Linear Model Workshop Using Bayesian Methods

6-10 November 2017

Arnaud Barras, University of Bern (CH) Dr Caroline Betto-Colliard, CUSO (CH) Dr. Malie Lessard-Therrien, University of Stockholm (SE)

Dr. Fränzi Korner-Nievergelt, Swiss Ornithological Institute, Sempach (CH) Dr. Malie Lessard-Therrien, University of Stockhol (SE) Dr. Stefanie von Felten, Statistician, Oikostat GmBH, Ettiswil (CH)

Bayesian data analysis becomes more and more standard in the analyses of biological data, particularly when hierarchical and more complex models are used. They allow fitting models that are too complex to be fitted easily using frequentist methods, e.g. hierarchical ecological models. Furthermore, existing knowledge about a parameter can formally be used when analysing the

data, and the results have a natural interpretation such as the probability of a specific hypothesis.

The course introduces the principles of Bayesian data analyses together with a sound training in applying linear models. Today, life scientists, especially ecologists, are expected to be familiar with normal linear models (LM), linear mixed models (LMM), generalised linear models (GLM), and generalised linear mixed models (GLMM). These four types of models form the basis for a variety of more complicated models, such as hierarchical ecological models, mark-recapture and populations models.

Participants will apply linear models using Bayesian methods with the free statistical software R (www.r-project.org) and the add-on packages arm and rstanarm. The course follows Gelman & Hill 2007, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press and Korner-Nievergelt et al. 2015, Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS and Stan, Elsevier, New York.

Worked examples will include:

- graphical data exploration (various plotting functions)

- fit of the model to data (R-Functions lm, lmer, glm, glmer, stan_glm)

- assessment of model fit and model assumptions (diagnostic plots of residuals, predictive checking)

- visualization of the results and drawing conclusions (summary, anova, predict, sim)

During the last day of the course, participants analyse their own data. Every participant will shortly present the own data project at the end of the course (~8-10 min per participant).

University of Bern

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Information:

Students should bring their own laptop with the following R-packages installed before the workshop:

- lme4

- rstanarm

When? 6-10 November 2017
November, 6-8: 9am - 6pm November, 9: 9am - 5pm November, 10: 9am - 4 pm Where?
University of Bern
FBB Kursraum, Room D 002A, Baltzerstrasse 4, 3012 Bern

PhD students of the DPEE are eligible for reimbursement of incurred travel expenses by train (half-fare card, and 2nd class) and meals (up to 25 CHF). Please send the original tickets (no copies, except for the general abonnement) with the reimbursement form to:

Caroline Betto-Colliard

 Doctoral Program in Ecology and Evolution  DEE- Biophore Building  University of Lausanne  CH-1015 Lausanne Concerning reimbursement of accomodation, please contact Caroline Betto-Colliard (ecologie-evolution(at)cuso.ch) BEFORE the beginning of the course.

Priority is given to PhD students of the DPEE until 15th of October, 2017. After this deadline, first comes, first serves! Through your myCUSO account

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