[R-sig-ME] aov() -> lme() conversion difficulty
bbolker at gmail.com
Wed Mar 16 16:44:00 CET 2011
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 ...
> 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
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 ... ???
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