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
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