Here's my understanding of what this model would do (not necessarily
correct): allow calculation of mean 'Reaction' conditional on a combination
of 'Days' and 'Subject'. The random effect of 'Subject' would allow
calculation of distance of each 'Subject' away from mean 'Reaction' when
conditional on a combination of 'Days' and 'Subject'.

Thanks
Ross

From:  Kevin Wright <kw.stat@gmail.com>
Date:  Sunday, 7 April 2013 16:49
To:  Ross Ahmed <rossahmed@googlemail.com>
Subject:  Re: [R-sig-ME] Visualising mixed effects model

I don't understand why you are trying to fit that model...can you give a
verbal interpretation of it?

KW


On Sun, Apr 7, 2013 at 7:55 AM, Ross Ahmed <rossahmed@googlemail.com> wrote:
> Using the Śsleepstudyą dataset, I find this plotŠ
> 
> library(ggplot2)
> ggplot(sleepstudy, aes(Days, Reaction, group=Subject, colour=Subject)) +
> geom_point() + geom_path()
> 
> Šreally help me understand this modelŠ
> 
> library(lme4)
> (m1 <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy))
> 
> However, I cannot wrap my head round how to visualise this modelŠ
> 
> (m2 <- lmer(Reaction ~ Days + Subject + (1 | Subject), sleep study))
> 
> Šin which ąSubjectą is specified as both a fixed effect and mixed effect.
> Any clues as to how to plot this model?
> 
> Thanks
> Ross
> 
> 
> 
> 
> 
>         [[alternative HTML version deleted]]
> 
> 
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 



-- 
Kevin Wright



	[[alternative HTML version deleted]]

