I'm looking at developing a mixed effect model for repeated measures of blood pressure using two different techniques. I have been discussing the benefits of mixed models over repeated measures ANOVA on SE previously. Only problem is that I'm finding the literature on mixed models in R is based on a starting point quite a bit higher than my feeble understanding. Does anyone know of a good introduction to mixed modelling in R or can they suggest a good sequence of topics that will lead to a thorough understanding of how to construct and (more importantly) interpret the output of a mixed model in R?
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Where are you starting from? That is, how much do you know already? – Aaron left Stack Overflow Sep 09 '11 at 14:15
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1Here are some recommendations I made a while back. I still think Gelman and Hill is a good one - it builds its way up to mixed models from a pretty basic level. I still refer to it pretty frequently. – Matt Parker Sep 09 '11 at 16:45
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I am a biologist and I used Linear Mixed Models - A practical guide using statistical software. It takes you through the code for real examples - explaining the decision making process and how to intrepret it!! I was so relieved to find it.
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2Hi Natasha, welcome to the site. I added a link to your reference to the google book site, and removed the signature line. Generally we do not need signature lines for posts here, as you can leave this information in your profile. Thank you for the reference. – Andy W Nov 15 '11 at 14:09