R help - Nov 2007 - multiple comparisons/tukey kramer

Hi,

I'm trying to make sense of the options for multiple comparisons
options in R. I've found the following options:

pairwise.t.test, which provides standard t-tests, with options for
choosing an appropriate correction for multiple comparisons

TukeyHSD, which provides the usual Tukey test

glht(package multcomp), which provides a variety of options
>From the help list, it appears that glht is the preferred approach.
However, I don't understand what the options are. ?glht refers to a
very technical paper on the numerical computations involved, and I
couldn't find a description corresponding to the McDermott or AVE
options. I did notice that the Tukey option provides the same result
as TukeyHSD for balanced data. Is this the same as Tukey-Kramer?

As I understand it, there is no universal consensus as to which test
is best. TukeyHSD appears to be appropriate for balanced designs. I
have an unbalanced design to analyze. I can use glht, but can someone
tell me what each option is actually calculating? A reference to a
paper that describes the procedures would be great, but I'm afraid I
the one offered in ?glht[1] is beyond me.

Thanks,

Tyler

[1]  Frank Bretz, Alan Genz and Ludwig A. Hothorn (2001), On the
     numerical  availability of multiple comparison procedures.
     _Biometrical Journal_, *43*(5), 645-656.

Tyler Smith a ?crit :
> Hi,
> 
> I'm trying to make sense of the options for multiple comparisons
> options in R. I've found the following options:
[ Snip ... ]
> As I understand it, there is no universal consensus as to which test
> is best.
There is no such thing. Each of the procedures is aimed at some subset
of the possible contrasts you may want to test. From my limited
knowledge, with some(I hope not too gross) simplifications :

- Tukey HSD will enable you to test the p(p-1)/2 pair differences one
can create with p groups ;
- Dunnett's procedure is made to compare (p-1) "treatments" to a
common
control ;
- Scheff?'s procedure is applicable to *any* ("reasonable") set of
contrasts you can form ;
- Newman-Keuls : aims to create separate subset of groups (but has
serious conceptual and technical flaws ! Don't do that nunless you know
what you're doing...).
- etc ...

Those procedures have the same hypothesis as the base ANOVA :
homoscedasticity, normality of residuals, etc ... Their robustness to a
small departure friom these conditions vary. As a last resort, a set of
non-parametric comparisons with the (overly conservative) Bonferroni
adjustment may help (but less power !).

You'll have to refer to the subject matter to make a choice.
>                                       TukeyHSD appears to be appropriate
for balanced designs. I
> have an unbalanced design to analyze.
Therefore, no aov(..) (hence no TukeyHSD(aov(...))) for you...
>                                        I can use glht, but can someone
> tell me what each option is actually calculating? A reference to a
> paper that describes the procedures would be great, but I'm afraid I
> the one offered in ?glht[1] is beyond me.
Google ("multiple comparisons") will offer you some dubious and quite
a
few good references...

HTH

					Emmanuel Charpentier