Statistics Module 2: Linear Models and Linear Mixed Models with R

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Statistics Module 2: Linear Models and Linear Mixed Models with R

  • Beginn: 05.11.2018
  • Ende: 08.11.2018
  • Vortragender: Dr. Fränzi Korner-Nievergelt
  • Oikostat
  • Ort: MPIO Möggingen
  • Raum: MaxLounge
  • Gastgeber: IMPRS for Organismal Biology
  • Kontakt: mhieber@orn.mpg.de
Linear models (LM) and linear mixed models (LME): Linear Regression, multiple Regression, ANOVA, ANCOVA, model selection (group work), linear mixed models, work on own data
Day 1 LM: Linear Regression, multiple Regression, ANOVA, ANCOVA
  • least-square method
  • parameterisation
  • interactions
  • tests (marginal and sequential)
  • model assumptions
  • predictions
  • introduction to Bayesian data analysis
  • posterior distributions
Day 2 LME: model selection (group work), linear mixed models
  • maximum likelihood, restricted maximum likelihood
  • random and fixed effects
  • likelihood ratio test / bootstrap
  • random slopes-random intercept models
  • possibly further model types depending on the participants wishes
Day 3 LME:
  • model matrix
  • predictions, posterior probabilities of hypotheses
  • preparing data for work on own data
Day 4 projects: work on own data and presentations

Prerequisite for participation: basic knowledge in statistics

Course material book: Korner-Nievergelt, F., T. Roth, S. Von Felten, J. Guélat, B. Almasi, and P. Korner-Nievergelt. 2015. Bayesian Data Analysis in Ecolog Using Linear Models with R, BUGS, and Stan. Elsevier, New York.

 
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