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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Sie sind hier: Startseite Nachrichten Winter Semester 2021/2022 “Mixed effect models with R”
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Winter Semester 2021/2022 “Mixed effect models with R”

 

In the winter semester 2021/2022, the Department of Biometry & Environmental System Analysis offers a M.Sc. module on “Mixed effect models with R”. The course will take part from the 21th of February, 2022 to the 11th of March, 2022.

Syllabus

The module focuses on mixed effects models and their application 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 analyze their data.

In the module, students will gain a deeper understanding of regression models. They will develop a critical knowledge on how to implement mixed effects models and use them for drawing inference from complex data. In addition to a Maximum Likelihood based approach, a simple Bayesian perspective will be acquired.

The module's goal is to give students the skills to analyze their own project data and draw biological conclusions from them using mixed effects models. Students are therefore encouraged to bring their own data sets. If this is not possible or data sets do not require mixed models, real-life data sets are available. This strategy allows students to get a feeling for the possibilities but also limitations of mixed models and the judgment calls made when building, analyzing and interpreting these 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:

                                     • Generalized estimation equations (GEE)

                                     • Linear mixed models (LMM)

                                     • Generalized linear mixed models (GLMM)

                                     • Generalized additive mixed models (GAMM)

All topics will be taught in the free software R, mainly using the packages lme4, gee, mgcv and their add-on packages. Further, the packages brms and rethinking are required. Teaching language will be English.

Prerequisites

• Basic statistical knowledge: distributions, maximum likelihood, (multiple) regressions (ANCOVA), Analysis of Variance (ANOVA), generalized 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 HISinOne. In case of questions, please send an email to the module coordinator Marieke Wesselkamp (marieke.wesselkamp (at) biom.uni-freiburg.de). The number of places is limited to 20 participants.

 

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