[R] Mixed Effects Model on Within-Subjects Design

[Dave Deriso]

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 at 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 at u.washington.edu
>
> Center for the Study of Health and Risk Behaviors (CSHRB)
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>
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>
> 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]]
>
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