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The New Statistics with RAn Introduction for Biologists$
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Andy Hector

Print publication date: 2015

Print ISBN-13: 9780198729051

Published to Oxford Scholarship Online: March 2015

DOI: 10.1093/acprof:oso/9780198729051.001.0001

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Generalized Linear Mixed-Effects Models

Generalized Linear Mixed-Effects Models

Chapter:
(p.165) 11 Generalized Linear Mixed-Effects Models
Source:
The New Statistics with R
Author(s):

Andy Hector

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198729051.003.0011

Generalized linear mixed-effects models (GLMMs) are introduced as a combination of the mixed-effects models and GLMs met in earlier chapters. The additional challenges of the analysis are explored and the differences in the R software functions and its output are explained. Model comparison and selection can be done using information criteria or likelihood ratio tests. In addition to the AIC and BIC the DIC was devised for use with multilevel models. The options for assessing how well the model assumptions are met are reduced relative to linear models and GLMs but the most accessible current options are demonstrated.

Keywords:   GLMMs, deviance information criterion, DIC, AIC, BIC, multilevel models, model assumptions

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