Monday, December 12, 2011. 14h-16hZofia Ecaterina TaranuGeneralized
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).
- Relax the assumptions of linear mixed-effects models and extend to Generalized LMM (i.e., GLMM).
PrerequisitesWorkshop participants should familiarize themselves with last week's GLM (Generalized Linear Model) workshop. It is recommended that you review the presentation material from the workshop if you did not attend. Please install the lme4 package 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. |

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