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

[Dave Deriso]

Hi Ted,

Thank you for offering this suggestion! However, simply running an
anova on the lme is difficult to interpret. Daniel has suggested doing
this only when comparing models. Can you explain how yours works?

Best,
Dave

On Thu, May 20, 2010 at 11:36 AM, Charles E. (Ted) Wright
<cewright at uci.edu> wrote:
> 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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>>
>>
>