General Linear and Linear Mixed Models in R (UZH ECO331)
| 2024_FS_GenLinMixModR | |
| 03.06.2024 - 27.06.2024 | |
| 6 Tage | |
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Beginn Anmeldefrist: 01.11.2023 Ende Anmeldefrist: 01.06.2024 |
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| Extern | |
| 8 | |
| 8 | |
| Students enrolled in PSC PhD Programs: CHF 0 LSZGS PhD students: CHF 0 All others: CHF 300 | |
| Universität Zuerich, Irchelcampus | |
| In this 6-day blocked course, the participants will learn to analyse experimental and observational data with general linear and linear mixed models. The course will be held as workshop, with lecture-type parts introducing important concepts and exercises in which the participants will work on data sets provided or their own data. A key goal will be that the participants learn to recognize the essential structure of data sets and to implemented them adequately in statistical models with fixed and random effects. Specifically, the course will deal with issues of experimental design, analysis of variance, hypothesis testing, variance components, models with multiple error terms as well as balanced and unbalanced data. (Note: it is important to understand that this course is not about generalized linear mixed models [GLMM, non-normal data], although it is possible to deal with such data in the projects) | |
| Dr. Pascal Niklaus (UZH) | |
| 2 | |
| PhD Students. Priority will be given to PhD students registered in the PhD Program in Ecology, PhD Program Plant Sciences / Science & Policy, other PhD students and Postdocs if places available. Participating Master students will not be awarded ECTS credits unless agreed with the lecturer and the program coordinator prior to the course. | |
| The participants should have a reasonable prior knowledge of standard statistics and have completed an introductory course on analysis of variance or regression (or equivalent). This is an advanced course and experience with analysing own data is beneficial. The course participants also should be familiar with R and bring their own laptop with a working recent installation of the R software (http://www.r-project.org) including the libraries nlme, lme4, and lmerTest. | |
| English | |
| In order to obtain the ECTS point, each participant is required to actively participate in the case-study work, discussions, and presentations during the course days. | |
By registering you agree to the PSC course terms and conditions AGBs | |
| Arrange cancellation with the PSC coordination office (psc_phdprogram@ethz.ch): Up to 2 weeks prior to course start without a fine. Later cancellations and incomplete attendance without documented justification will incur a fee of 200 CHF. | |
| For full registration send an e-mail to phdecology@ieu.uzh.ch, include your student number, surname, name, email address and your PhD program (!), and a short statement about where you are in your studies, about the data you will use during the course and what basic stats courses you have attended. As this is an advanced course, participants will be selected based on sufficient knowledge and own data. | |
| Dr. Luisa Last (psc_phdprogram@ethz.ch) | |
| FS24_GLM and LMixed models in R.pdfvertical_align_bottom |