[R-sig-ME] Interpreting Mixed Effects Model on Fully Within-Subjects Design

[Charles E. (Ted) Wright]

If I understand your question, the command
 	anova(study.lme)
should give you what you are looking for.

Ted Wright

On Thu, 20 May 2010, Dave Deriso wrote:

> Dear Mixed Models Experts,
>
> I am trying to find the p_value for the overall interaction of
> condition*difficulty, but the lme() seems to output everything but
> this. Can offer some suggestions on how to make sense of this output,
> and where my interaction and main effects are? I read the 'mixed
> effects' section in the R Book (Crawley, 2007) and still can't figure
> it out. Any advice will be very much appreciated.
>
> lme(value~condition*diff,random=~1|subject/rep)
>
> 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
>
>
> Here is a synopsis:
>
> 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 (rep = 1,2,3) were taken for each condition x
> difficulty pair for each subject, making this an entirely
> within-subject design.
>
> Here is the 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)
>
> Best,
> Dave Deriso
> UCSD Psychology
>
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