Summer School

Mixed Linear Models

Learn more about Mixed Linear Models in this Wageningen Summer School for PhD candidates and other academics.

Organisator Production Ecology and Resource Conservation (PE&RC)

do 21 juni 2018 tot vr 22 juni 2018

Course introduction

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.

What you will learn

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.

    Meet the lecturers

    Dr. Bas Engel (Biometris, Wageningen University)

    Course design

    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


    Knowledge of Basic Statistics and Linear Models and some experience with the software package R are assumed

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