




Thank you Ben.

>   Unless there is something non-exchangeable about the individual tests
> (e.g. you think the probability of a hit might depend on the sequence of
> the test), then I don't see why you can't do the paired t-test: that is,
> calculate the differences per individual in the number of two-eye and
> one-eye successes and do a t test against a population mean of zero.

By using a mixed model I hoped it would take care of multiple testing (first paired t test, then another t test) intrinsically. If the differences between individuals were indeed larger than the differences between one vs two eyes for each individual, would it make any sense to run the scond t test, where I were to pool all individuals?

The outcome 0-10 is like a percentage: 10 being 100%, 0 => 0% etc. It has nothing to do with the repeats (which are by the fluke of nature also 10x).

>   Hits ~ b_i + eps_person + eps_(person:numeyes) + eps_resid
> 
>   where the first term is the fixed effect of number of eyes,
> the second is the among-subject effect, the third is the
> among-tests-within-subject effect, and the the fourth is residual variation
> (I'm still not sure if you're using total number of hits per person/eye
> combination as your data (0-10), or whether you're using success on a
> particular trial (0/1).  If the latter, you're violating normality
> pretty badly.  If the former, then the third and fourth terms above are
> completely confounded [as I suggested based on your zero residual error].)

No, the outcome/hit/score is sampled 20x per each individuals: 10x measured for one eye, 10x for two eyes used.

The data I used in the example showed little variation in between individuals, but it was just a trial run for a larger study to follow. This is why I have to decide on the correct model.

Why my aov() -> lme() conversion did not show ~identical p-Values, but one being *twice* as large as the other p-value result, I do not understand. Examples used in the docu and webpage give almost identical p-values for this type of aov() -> lme() conversion.

Thanks again,

Brian

 		 	   		  
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