Thanks, Jim, but I also tried with this recourse, and can't reach to the
outcome I wanted...



Cleber Chaves


On Tue, Jun 17, 2014 at 9:22 PM, Jim Lemon <jim@bitwrit.com.au> wrote:

> On Tue, 17 Jun 2014 08:58:12 PM Cleber Chaves wrote:
> > Dear all,
> > has a long time that I unsuccessfully try to obtain letters of each
> equal
> > means in a Tukey test of a GLM object, in a descending order. Could
> > someone, please, help me?
> >
> > I try this:
> >
> > m1 <- glm(comp~pop)
> > anova(m1,test="F")
> > lsmeans(m1, cld~pop, adjust='tukey')
> >
> > But the letters I obtain are not in descendig order, and I dispend a
> lot of
> > time to organize it when the outcome have many letters (e.c. A to F
> > letters).
> >
> Hi Cleber,
> You may be able to do something like the following that is from the
> example for TukeyHSD:
>
> summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks))
> ...
> TukeyHSD(fm1, "tension", ordered = TRUE)
>   Tukey multiple comparisons of means
>     95% family-wise confidence level
>     factor levels have been ordered
>
> Fit: aov(formula = breaks ~ wool + tension, data = warpbreaks)
>
> $tension
>          diff        lwr      upr     p adj
> M-H  4.722222 -4.6311985 14.07564 0.4474210
> L-H 14.722222  5.3688015 24.07564 0.0011218
> L-M 10.000000  0.6465793 19.35342 0.0336262
>
> # assign this to an object
> thsd<-TukeyHSD(fm1, "tension", ordered = TRUE)
>
> # see if there is a way to reorder the comparisons
> str(thsd)
> List of 1
>  $ tension: num [1:3, 1:4] 4.72 14.72 10 -4.63 5.37 ...
>   ..- attr(*, "dimnames")=List of 2
>   .. ..$ : chr [1:3] "M-H" "L-H" "L-M"
>   .. ..$ : chr [1:4] "diff" "lwr" "upr" "p adj"
>  - attr(*, "class")= chr [1:2] "TukeyHSD" "multicomp"
>  - attr(*, "orig.call")= language aov(formula = breaks ~ wool +
> tension, data = warpbreaks)
>  - attr(*, "conf.level")= num 0.95
>  - attr(*, "ordered")= logi TRUE
>
> # reorder the matrix of comparisons
> thsd$tension<-thsd$tension[order(rownames(thsd$tension)),]
>
> thsd
>   Tukey multiple comparisons of means
>     95% family-wise confidence level
>     factor levels have been ordered
>
> Fit: aov(formula = breaks ~ wool + tension, data = warpbreaks)
>
> $tension
>          diff        lwr      upr     p adj
> L-H 14.722222  5.3688015 24.07564 0.0011218
> L-M 10.000000  0.6465793 19.35342 0.0336262
> M-H  4.722222 -4.6311985 14.07564 0.4474210
>
> Jim
>
>

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