[R] Mixed Effects Model on Within-Subjects Design
Thierry.ONKELINX at inbo.be
Thu May 20 11:01:15 CEST 2010
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.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Research Institute for Nature and Forest
team Biometrics & Quality Assurance
tel. + 32 54/436 185
Thierry.Onkelinx op inbo.be
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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!!!
> 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
> > 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
> > 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
> > 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]]
> > ______________________________________________
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