Mixed Linear Models

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Mixed Linear Models

To be announced


In this module we discuss how to analyse dependent data, that is, data for which the assumption of independence needed in Linear Models is violated. So: Do you have a nested experimental set-up? Like measurements on large plots, but also on smaller plots within the larger plots? Do you have repeated measurements? Like measurements on height of the same plant over time? Or weight of the same animal over time? Do you have pseudo-replication? Like measuring 3 plants from the same pot? In this sort of situations it is not reasonable to use ordinary ANOVA or regression to analyse your data. These methods are likely too optimistic, and you will get erroneous significant results. And your paper will be returned for, hopefully, a major revision! With mixed linear models a more appropriate model, allowing for dependence between observations, can be specified, which will lead to more reasonable conclusions.
In this module, you will learn about these models (also about the formulation in matrix notation, covariance matrices included), about the way to fit them to your data using software, and about the output produced by the software. In computer sessions participants can practice fitting models of this type, and gain an understanding of the output created by the software. You are encouraged to bring along your own data if you have any. The main statistical software used in this course is R.

  • Day 1, morning: Gentle introduction to mixed models
  • Day 1, afternoon: General theory of mixed models, examples of some variance components models with R
  • Day 2, morning: Estimation and testing in a mixed model
  • Day 2, afternoon: Repeated measurements with examples in R
General information
Target Group The course is aimed at PhD candidates and other academics
Group Size 24 participants
Course duration 2 days
Language of instruction English
Frequency of recurrence Once a year (Summer)
Number of credits 0.6 ECTS
Lecturers Dr. Gerrit Gort (Biometris, Wageningen University), Dr. Bas Engel (Biometris, Wageningen University)
Prior knowledge Knowledge of Basic Statistics and Linear Models and some experience with the software package R are assumed
Location Wageningen University Campus
Options for accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For information on B&B's and hotels in Wageningen please visit proefwageningen.nl. Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Note that besides the restaurants in Wageningen, there are also options to have dinner at Wageningen Campus.


Fees 1

Generally, the following fees apply for this course, but note that the actual fees may be somewhat different for the next edition of this course.

PE&RC / SENSE / WASS / EPS PhD candidates with an approved TSP € 120,- € 170,-
a) All other PhD candidates
b) Postdocs and staff of the above mentioned Graduate Schools
€ 280,- € 330,-
All others € 400,- € 450,-

1 The course fee includes a reader, coffee/tea, and lunches. It does not include accommodation (NB: options for accommodation are given above)
2 The Early-Bird Fee generally applies to anyone who REGISTERS AT LEAST 4 WEEKS PRIOR TO THE START OF THE COURSE

More information

Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116
Email: claudius.vandevijver@wur.nl

Lennart Suselbeek (PE&RC)
Phone: +31 (0) 317 485426
Email: lennart.suselbeek@wur.nl


This course is currently closed for registration, and will re-open once a new edition of this course is scheduled.