What can cause a "Error() model is singular error" in aov when fitting a repeated measures ANOVA? I have the following data set:
                 |       Scenario 1       |     Scenario 2         |
                 |Trial 1|Trial 2| Trial 3|Trial 1|Trial 2| Trial 3|
 -------------------------------------------------------------------
              S1 | ...
 Condition 1  S2 | ...
              S3 |
 -------------------------------------------------------------------
              S5 |
 Condition 2  S6 |
              S7 |

Thus the Trials are nested in the Scenarios and all of them are within subject. I am trying to run an ANOVA on this data set. Here is the model without defining that Scenarios (and Trials) are within subject.
 my_data.aov <- aov(value~Condition*Trial%in%Scenario,data=my_data) #works fine

But when I specify that these are within subject:
my_data.aov <- aov(value~Condition*Trial%in%Scenario+Error(Player/(Trial%in%Scenario)),data=my_data) 

I get the following error 
In aov(value ~ Condition * Trial % in % Scenario + Error(Player/(Trial %in%  :
Error() model is singular

The closest set-up I could find was Split plot in R but there the subjects are nested inside each Trial not in each Condition.
EXAMPLE FILE
Here is an example file in long format.
What about this approach?
If I treat each Trial as a sample, then I can collapse across Scenarios by averaging them, so I will have a simpler model, where each Subject's behavior is described per Scenario. And since I need to analyze the relationship of value~Condition*Scenario I can do so by defining the Error like Error(Subject/Scenario). 
Will this approach invalidate my analysis?
 A: Without an example dataset (say from 6 subjects) it is hard to say for certain.  You might try to use dput towards this end.
The error message Error() model is singular suggests to me that you have insufficient degrees of freedom to calculate your model.  I think it is probably unlikely you meant to use %in% in this context, but maybe I'm wrong.  Look at ?%in%.  %in% produces a logical TRUE/FALSE vector of whether the Trial # matches the Scenario #.  When you do estimate that subject effect then you only have two observations in the cell where Trial # matches the Scenario number and then you won't have enough observations per subject left over to also look at a Condition * Trial %in% Scenario interaction.
Consider:
library(doBy)
summaryBy(value~Condition*Trial%in%Scenario,data=mydata,FUN=c(mean,var,length))

... and be sure it matches your expectations.
A: I found a solution, even though it solves a my bigger problem, but as you could see from my question I went down a road, where I got stuck and I didn't really get an answer how to solve it. So I opted for the lme4 package and used the lmer function to model the relationship in my data. 
BTW, the my approach where I suggest collapsing the data is a traditional approach in psychology, which I found out recently (after asking the question).
Finally the tutorial which saved the day for me (using lmer) is written by Bodo Winter, where he works on a dataset that almost matches mine -- even though it's not so obvious from the first time you read it.
In short my linear model looks like this:
 lmer(value~Condition*Scenario + (1+Scenario|Player) + (1|Scenario/Trial)

This perfectly models my experimental setup.
