[R] aov, lme, multcomp

[Mark Difford]

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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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> 
> 

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