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r_workshop7 [2016/10/21 10:48]
vincent_fugere [QCBS R Workshops]
r_workshop7 [2019/02/25 09:04]
mariehbrice [Workshop 7: Generalized linear mixed models]
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 **Summary:​** A significant limitation of linear mixed models is that they cannot accommodate response variables that do not have a normal error distribution. Most biological data do not follow the assumption of normality. In this workshop, you will learn how to use **generalized** linear models, which are important tools to overcome the distributional assumptions of linear models. You will learn the major distributions used depending on the nature of the response variables, the concept of the link function, and how to verify assumptions of such models. We will also build on the previous workshop to combine knowledge on linear mixed models and extend it to generalized linear mixed effect models. **Summary:​** A significant limitation of linear mixed models is that they cannot accommodate response variables that do not have a normal error distribution. Most biological data do not follow the assumption of normality. In this workshop, you will learn how to use **generalized** linear models, which are important tools to overcome the distributional assumptions of linear models. You will learn the major distributions used depending on the nature of the response variables, the concept of the link function, and how to verify assumptions of such models. We will also build on the previous workshop to combine knowledge on linear mixed models and extend it to generalized linear mixed effect models.
  
-Link to associated Prezi: [[http://​prezi.com/​suxdvftadzl4/​|Prezi]]+**Link to new [[https://​qcbsrworkshops.github.io/​Workshops/​workshop07/​workshop07-en/​workshop07-en.html|Rmarkdown presentation]]** 
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 +Link to old [[http://​prezi.com/​suxdvftadzl4/​|Prezi ​presentation]]
  
 Download the R script and data for this lesson: Download the R script and data for this lesson:
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 B. Bolker (2009) [[http://​ms.mcmaster.ca/​~bolker/​emdbook/​|Ecological Models and Data in R]]. Princeton University Press.\\ B. Bolker (2009) [[http://​ms.mcmaster.ca/​~bolker/​emdbook/​|Ecological Models and Data in R]]. Princeton University Press.\\
 A. Zuur et al. (2009) [[http://​link.springer.com/​book/​10.1007/​978-0-387-87458-6|Mixed Effects Models and Extensions in Ecology with R]]. Springer.\\ A. Zuur et al. (2009) [[http://​link.springer.com/​book/​10.1007/​978-0-387-87458-6|Mixed Effects Models and Extensions in Ecology with R]]. Springer.\\
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 +**Articles **
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 +Harrison, X. A., L. Donaldson, M. E. Correa-Cano,​ J. Evans, D. N. Fisher, C. E. D. Goodwin, B. S. Robinson, D. J. Hodgson, and R. Inger. 2018. [[https://​peerj.com/​preprints/​3113/​|A brief introduction to mixed effects modelling and multi-model inference in ecology]]. ​ PeerJ 6:​e4794–32.
  
 **Websites** **Websites**