Hi Richard,
>> The tests give different Fs and ps. I know this comes up every once in
a
>> while on R-help so I did my homework. I see from these two threads:
This is not so, or it is not necessarily so. The error structure of your two
models is quite different, and this is (one reason) why the F- and p-values
are different.
For instance, try the following comparison:
## Example
require(MASS) ## for oats data set
require(nlme) ## for lme()
require(multcomp) ## for multiple comparison stuff
Aov.mod <- aov(Y ~ N + V + Error(B/V), data = oats)
Lme.mod <- lme(Y ~ N + V, random = ~1 | B/V, data = oats)
summary(Aov.mod)
anova(Lme.mod)
See:
http://www.nabble.com/Tukey-HSD-(or-other-post-hoc-tests)-following-repeated-measures-ANOVA-td17508294.html#a17553029
The example itself is from MASS (Venables & Ripley).
HTH, Mark.
Richard D. Morey wrote:>
> I am doing an analysis and would like to use lme() and the multcomp
> package to do multiple comparisons. My design is a within subjects
> design with three crossed fixed factors (every participant sees every
> combination of three fixed factors A,B,C). Of course, I can use aov() to
> analyze this with an error term (leaving out the obvious bits):
>
> y ~ A*B*C+Error(Subject/(A*B*C))
>
> I'd also like to use lme(), and so I use
>
> y ~ A*B*C, random= ~1|Subject
>
> The tests give different Fs and ps. I know this comes up every once in a
> while on R-help so I did my homework. I see from these two threads:
>
> http://www.biostat.wustl.edu/archives/html/s-news/2002-05/msg00095.html
> http://134.148.236.121/R/help/06/08/32763.html
>
> that this is the expected behavior because of the way grouping works
> with lme(). My questions are:
>
> 1. is this the correct random argument to lmer:
>
> anova(lme(Acc~A*B*C,random=list(Sub=pdBlocked(list(
> pdIdent(~1),
> pdIdent(~A-1),
> pdIdent(~B-1),
> pdIdent(~C-1)))),data=data))
>
> 2. How much do the multiple comparisons depend on the random statement?
>
> 3. I'm also playing with lmer:
>
> Acc~A*B*C+(1|Sub)
>
> Is this the correct lmer call for the crossed factors? If not, can you
> point me towards the right one?
>
> 4. I'm not too concerned with getting "correct" Fs from the
analyses
> (well, except for aov, where it is easy), I just want to make sure that
> I am fitting the same model to the data with all approaches, so that
> when I look at parameter estimates I know they are meaningful. Are the
> multiple comparisons I'll get out of lme and lmer meaningful with fully
> crossed factors, given that they are both "tuned" for nested
factors?
>
> Thanks in advance.
>
> --
> Richard D. Morey
> Assistant Professor
> Psychometrics and Statistics
> Rijksuniversiteit Groningen / University of Groningen
>
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>
>
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