Kontakt

 

Leitung

Prof. Dr. Carsten F. Dormann

Telefon: +49 761 203-3749
Telefax: +49 761 203-3751
eMail: carsten.dormann@biom.uni-freiburg.de 

 

Sekretariat:

 

Frau Eva Meier

Telefon: +49 761 203-3749 
Telefax: +49 761 203-3751 
eMail: eva.meier@biom.uni-freiburg.de 

 

Anschrift:

 

Biometrie und Umweltsystemanalyse

Albert-Ludwigs-Universität Freiburg

Tennenbacher Straße 4 
79106 Freiburg i. Br.

 

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Summer Semester 2019 “Mixed Effects Models in R”

 

In the summer semester 2019, the Department of Biometry & Environmental System Analysis offers a M.Sc. elective track module on “Mixed Effects Models in R”. The course will take part from the 8th of July 2019 to the 26th of July 2019. 

Syllabus

The module focuses on mixed effects models and their implementation in R. Mixed effects models are powerful tools to deal with structure and heterogeneity in environmental data arising from such common practices as multiple sampling of units, grouping units at various hierarchical levels, or spatial sampling. A rough estimation shows that 80-90 % of environmental studies require mixed effects models to analyse their data. However, mixed effects models are also complex and sometimes difficult to apply and interpret. More, they are developing. The module’s goal is to give students the basics of mixed effects models on which to build on when analysing their own data and draw biological conclusions from them using mixed effects models. The module builds on and extends statistical knowledge and its application as conveyed by other courses at the faculty. The course is intended for Master students but when place permits, PhD students may join, too.  

Topics covered will be:

          Generalised least squares (GLS)

          Linear and non-linear mixed models (LMM)

          Generalised linear mixed models (GLMM)

          possibly Generalised additive mixed models (GAMM)

All topics will be taught in the free software R, mainly using the packages lme4, nlme, mgcv and their add-on packages. Teaching language will be English.

 

Prerequisites

          Basic statistical knowledge: distributions, maximum likelihood, (multiple) regressions (ANCOVA), Analysis of Variance (ANOVA), generalised linear and additive models (GLMs, GAMs).

          Data import, handling, und basic statistical analyses in R (www.r-project.org) using the above tools (lm, glm, gam, aov).

          Knowledge of content of “R for Beginners” (https://cran.r-project.org/doc/ contrib/Paradis-rdebuts_en.pdf).

 

If you are interested:

Please apply to the course via email to the module coordinator Dr. Arne Schröder (arne.schroeder(at)biom.uni-freiburg.de). Deadline for application is  June 17th 2019. The number of places is limited to 20 participants. The course will take part when 5 or more students fulfilling the requirements participate.

 

 

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