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:
anova(lme(Hits~NumEyesUsed, random=~1|PersonID/NumEyesUsed,data=y))
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