# Repeated measures anova in R

I have a dataset containing percentage scores for 15 participants. There are two observations for every participant, one with an intervention and one with a placebo in a double-blind design. Each participant receives the intervention exactly once, either at the first or second day. The dataset looks like this:

condition     subject  day  score
placebo        1        1    90%
verum          1        2    92%
etc...


I now want to evaluate interactions between "condition" and "day" as well as "score". I've tried setting up a repeated-measures anova in r like this:

my.aov <- with(subjects, aov(score ~ condition + Error(subject / score)))


In the summary it tells me

          Df   Sum Sq  Mean Sq F value Pr(>F)
condition       1 0.000116 0.000116   0.059  0.811
Residuals 14 0.027298 0.001950


Is this an appropriate method for my data? Can I safely understand this to mean that inter-subject differences (my Error) are greater than effects? What further methods should I use to clarify?

library(lme4)

You say want to evaluate interactions between "condition" and "day", but the model you have written does not admit interactions. Within the main effects model you have estimated (no effect for day), the null hypothesis of no effect of condition cannot be rejected at a nominal 5% level.