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Linear Mixed-effects Models

Monday, November 21, 2011.  14h-16h
Cristian Correa

Description

Linear mixed-effects models describe the relationship between a continuous response variable and some covariates where at least one of the covariates in the model is a categorical variable. This categorical variable may represent the subject or study location, or more generally, the observational unit. If the discrete levels of this categorical variable are chosen at random this represents a random effects.

Objectives
  • Learn the difference between fixed and random effects and when data should be modelled with a combination of these effects, namely, mixed-effects models.
  • Use an example dataset to learn the R code needed to run a LMM (i.e, Linear Mixed-effects).
Prerequisites

Please install the lme4ggplot2, arm, and MASS packages for R before the start of the workshop.  If you are having trouble with this, please come at least 15 minutes early and we will be happy to assist you.

Slides

Download slides here.

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CladoceraData.csv
(20k)
Corey Chivers,
Nov 21, 2011, 10:59 AM
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LMM_code.R
(22k)
Corey Chivers,
Nov 21, 2011, 10:59 AM
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TL_Data.csv
(6k)
Corey Chivers,
Nov 21, 2011, 11:00 AM
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