
Thank you, I am sorry the tables got messed up. It was due to the Html->Text conversion.

The hit-score is an integer between 0 and 10, so its not a 0/1 binary outcome but a linear score each time (I guess the binomial wont work here)

I know there might not be any big differences in between individuals (for this dataset), but each individual in my sample is scored 10 times with one eye and 10 times with two eyes used, so I dont think I can do the paired t-test. I also noted that the distribution of scores in not normally distributed (in the population) but I ignore the fact because I have no idea how to do that without a much more complicated model (bootstrapping etc.)

The NumEyesUsed p-value for my lme is 0.3217 and for the equivalent aov it is 0.1772. So the actual question is if my conversion was totally wrong:

i.e

anova(lme(Hits~NumEyesUsed, random=~1|PersonID/NumEyesUsed,data=y))   <totally different>  summary(aov(Hits~NumEyesUsed+Error(PersonID/NumEyesUsed),data=y))

in the lme model you suggest:

random=~NumEyesUsed|PersonID

the p-Value I obtain is: 0.0788

How would you convert your random lme term back into the normal aov formula?

The idea to express/convert the lme -> aov formula in this fashion I found on:

http://blog.gribblelab.org/2009/03/09/repeated-measures-anova-using-r/

Thanks again,

Brian

> Date: Wed, 16 Mar 2011 11:44:00 -0400
> From: bbolker@gmail.com
> To: b.edward@live.com
> CC: r-sig-mixed-models@r-project.org
> Subject: Re: [R-sig-ME] aov() -> lme() conversion difficulty
> 
> On 11-03-16 10:41 AM, Brian Edward wrote:
> >  
> > according to a couple of PDFs and webpages in came up with this aov() <- lme() conversion:
> > 
> >  
> >> 
> 
>   I think you want
> 
>          random = ~NumEyesUsed|PersonID
> >  
> >                      numDF denDF  F-value p-value     
> >        (Intercept)     1    36     56.06667  <.0001  
> >        NumEyesUsed     1     1     3.26667  0.3217
> 
>   Your table got mangled.  Was it supposed to look like this?
> If so, then it's clear from the denominator DF that something is wrong ...
> 
> > 
> > 
> >> summary(aov(Hits~NumEyesUsed+Error(PersonID/NumEyesUsed),data=y))
> 
> Error: PersonID
> >           Df     Sum Sq    Mean Sq F value Pr(>F)Residuals  1 1.7514e-33 1.7514e-33    
> 
>   Also note that your residual errors are essentially zero, which means
> you don't have any variation left after you have included experimental
> blocks -- some are aliased with the residual error.
> 
> > Error: PersonID:NumEyesUsed
> >            Df Sum Sq Mean Sq F value Pr(>F)
> > NumEyesUsed 1    4.9     4.9   12.25 0.1772
> > Residuals   1    0.4     0.4               
> > Error: Within          Df Sum Sq Mean Sq F value Pr(>F)
> > Residuals 36   56.6  1.5722   
> >  
> > In this study each person is allowed try ten times with one eye and then ten times with two eyes to score hits. 
> > The question is if there is a difference in hits between using one or two eyes.
> > 
> > I get different p-values in my aov() <- lme() conversion, which one answers the question more closely?
> 
>   What are your actual response variables?  Number of successes out of
> 10 tries?  In that case I might suggest
> 
>   lmer(cbind(Hits,10-Hits)~NumEyesUsed+(NumEyesUsed|PersonID),
> family=binomial, data= ...)
> 
>   although that will make it hard for you get p-values.
> 
>   For that matter, since you have only two treatment levels, what's
> wrong with a paired t-test ... ???
> 
>   Ben Bolker
> 
> 
> 
 		 	   		  
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