[R] Mixed Effects Model on Within-Subjects Design

Thu May 20 11:01:15 CEST 2010

Dear Dave,

I think you want this model.

lme(value~condition:diff - 1,random=~1|subject)

Note that I removed the replicate ID from the model. Include it in the model makes only sense if you can expect a similar replication effects the first/second/thirth time that a subject performs your test.

HTH,

Thierry

----------------------------------------------------------------------------
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
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Research Institute for Nature and Forest
team Biometrics & Quality Assurance
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tel. + 32 54/436 185
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> -----Oorspronkelijk bericht-----
> Van: r-help-bounces op r-project.org 
> [mailto:r-help-bounces op r-project.org] Namens Dave Deriso
> Verzonden: donderdag 20 mei 2010 9:27
> Aan: David Atkins
> CC: r-help op r-project.org
> Onderwerp: Re: [R] Mixed Effects Model on Within-Subjects Design
> 
> Hi Dave,
> 
> Thank you for your helpful advice. I will take a look at the 
> multicomp package.
> 
> I was wondering where the lme() function outputs the
> interaction between condition*difficulty?
> 
> Below is the output to the code I had originally sent. Which 
> one of these is condition*difficulty?
> 
> Fixed effects: value ~ condition * diff
>                       Value Std.Error  DF   t-value p-value
> (Intercept)       300109.95  9506.690 688 31.568289  0.0000
> condition2         27717.65  9071.048 688  3.055617  0.0023
> condition3        -23718.72  9071.048 688 -2.614772  0.0091
> diff50             56767.55  9071.048 688  6.258103  0.0000
> diff75            120031.80  9071.048 688 13.232408  0.0000
> condition2:diff50 -45481.21 12828.399 688 -3.545354  0.0004
> condition3:diff50   7333.37 12828.399 688  0.571651  0.5677
> condition2:diff75 -38765.77 12828.399 688 -3.021871  0.0026
> condition3:diff75  12919.59 12828.399 688  1.007109  0.3142
> 
> Also, why are diff25 and condition1 missing from the output??
> 
> Thanks again for your generous help!!!
> 
> Best,
> Dave Deriso
> 
> 
> On Wed, May 19, 2010 at 10:08 PM, David Atkins 
> <datkins op u.washington.edu> wrote:
> >
> > Dave--
> >
> > Given that you want all comparisons among all means in your 
> design, you won't get that directly in a call to lme (or lmer 
> in lme4 package). Take a look at multcomp package and its 
> vignettes, where I think you'll find what you're looking for.
> >
> > cheers, Dave
> >
> > --
> > Dave Atkins, PhD
> > Research Associate Professor
> > Department of Psychiatry and Behavioral Science University of 
> > Washington datkins op u.washington.edu
> >
> > Center for the Study of Health and Risk Behaviors (CSHRB) 
> 1100 NE 45th 
> > Street, Suite 300 Seattle, WA  98105
> > 206-616-3879
> > http://depts.washington.edu/cshrb/
> > (Mon-Wed)
> >
> > Center for Healthcare Improvement, for Addictions, Mental Illness,
> >  Medically Vulnerable Populations (CHAMMP)
> > 325 9th Avenue, 2HH-15
> > Box 359911
> > Seattle, WA 98104?
> > 206-897-4210
> > http://www.chammp.org
> > (Thurs)
> >
> > Dear R Experts,
> >
> > I am attempting to run a mixed effects model on a within-subjects 
> > repeated measures design, but I am unsure if I am doing it 
> properly. I 
> > was hoping that someone would be able to offer some guidance.
> >
> > There are 5 independent variables (subject, condition, difficulty,
> > repetition) and 1 dependent measure (value). Condition and 
> difficulty 
> > are fixed effects and have 3 levels each (1,2,3 and 25,50,75 
> > respectively), while subject and repetition are random 
> effects. Three 
> > repeated measurements
> > (repetitions) were taken for each condition x difficulty 
> pair for each 
> > subject, making this an entirely within-subject design.
> >
> >
> >
> > I would like an output that compares the significance of 
> the 3 levels 
> > of difficulty for each condition, as well as the overall 
> interaction 
> > of condition*difficulty. The ideal output would look like this:
> >
> > condition1:diff25 vs. condition1:diff50  p_value = ....
> > condition1:diff25 vs. condition1:diff75  p_value = ....
> > condition1:diff50 vs. condition1:diff75  p_value = ....
> >
> > condition2:diff25 vs. condition1:diff50  p_value = ....
> > condition2:diff25 vs. condition1:diff75  p_value = ....
> > condition2:diff50 vs. condition1:diff75  p_value = ....
> >
> > condition3:diff25 vs. condition1:diff50  p_value = ....
> > condition3:diff25 vs. condition1:diff75  p_value = ....
> > condition3:diff50 vs. condition1:diff75  p_value = ....
> >
> > condition*diff  p_value = ....
> >
> >
> >
> > Here is my code:
> >
> > #get the data
> > study.data 
> =read.csv("http://files.davidderiso.com/example_data.csv",
> > header=T)
> > attach(study.data)
> > subject = factor(subject)
> > condition = factor(condition)
> > diff = factor(diff)
> > rep = factor(rep)
> >
> > #visualize whats happening
> > interaction.plot(diff, condition, value, ylim=c(240000,
> > 450000),ylab="value", xlab="difficulty", trace.label="condition")
> >
> > #compute the significance
> > library(nlme)
> > study.lme = lme(value~condition*diff,random=~1|subject/rep)
> > summary(study.lme)
> >
> >
> >
> > Thank you so much for your generous help!!!
> >
> > Best,
> > Dave Deriso
> > UCSD Psychology
> >
> >        [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help op r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide 
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> 
> ______________________________________________
> R-help op r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide 
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> 

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