Anne,
Thank you for writing back, and for including your data.
I have two things here. First, I ran an a analysis of your data and have
my observations
on interpretation. Second, I answer your general question about glht and
TukeyHSD when there are interactions.
I illustrate how to get the same answer from glht with an example from your
data.
## install.packages("HH") ## if you do not already have it
library(HH)
Active <- read.table(textConnection("
Treatment Habitat pActive
1G E 0.18541667
1G E 0.02500000
1G E 0.04208333
1G E 0.14847222
1G E 0.08055556
1G E 0.16777778
1G S 0.05111111
1G S 0.19083333
1G S 0.12333333
1G S 0.35722222
1G S 0.43750000
1G S 0.02638889
1R E 0.38736111
1R E 0.51180556
1R E 0.14916667
1R E 0.61041667
1R E 0.36013889
1R E 0.11347222
1R S 0.10805556
1R S 0.18722222
1R S 0.27625000
1R S 0.25236111
1R S 0.18208333
1R S 0.16152778
2G E 0.25916667
2G E 0.37194444
2G E 0.02263889
2G E 0.18402778
2G E 0.45750000
2G E 0.02250000
2G S 0.02958333
2G S 0.10069444
2G S 0.12875000
2G S 0.11361111
2G S 0.13680556
2G S 0.07875000
2R E 0.57513889
2R E 0.12888889
2R E 0.32000000
2R E 0.55736111
2R E 0.78888889
2R E 0.65055556
2R S 0.35527778
2R S 0.48361111
2R S 0.21361111
2R S 0.35277778
2R S 0.52611111
2R S 0.29416667
R+G E 0.37027778
R+G E 0.20263889
R+G E 0.07194444
R+G E 0.49041667
R+G E 0.21847222
R+G E 0.13555556
R+G S 0.20861111
R+G S 0.23986111
R+G S 0.02180556
R+G S 0.23250000
R+G S 0.28916667
R+G S 0.50208333
"), header=TRUE)
## close.connection()
closeAllConnections()
logitAct <- logit(Active$pActive)
model3 <- aov(logitAct ~ Treatment*Habitat, data=Active)
summary(model3)
summary(glht(model3, focus="Treatment", linfct=mcp(Treatment="Tukey")))
TukeyHSD(model3)
with(Active, table(Treatment, Habitat))
par(omd=c(0, .95, 0, 1))
model3.mmc <- glht.mmc(model3, linfct=mcp(Treatment="Tukey"))
plot(model3.mmc, ry=c(-2.75, 0.25), x.offset=.8)
plot.matchMMC(model3.mmc$mca)
## 1G 1R 2G 2R R+G
R.linear <- c(-1, 0, -1, 2, 0 )
names(R.linear) <- c('1G', '1R', '2G', '2R', 'R+G')
lmat.R <- orthog.complete(as.matrix(R.linear))
dimnames(lmat.R)[[2]][1] <- "R.linear"
model3.mmc <- glht.mmc(model3, linfct=mcp(Treatment="Tukey"),
focus.lmat=lmat.R)
plot(model3.mmc, ry=c(-2.75, 0.25), x.offset=.8)
plot.matchMMC(model3.mmc$lmat, col.signif="blue")
Active$Treatment <- factor(Active$Treatment, levels=c('1G', '2G', 'R+G',
'1R', '2R'))
contrasts(Active$Treatment) <- lmat.R[c('1G', '2G', 'R+G', '1R', '2R'),]
model3g <- aov(logitAct ~ Treatment*Habitat, data=Active)
summary(model3g, split=list(Treatment=list(R.linear=1, rest=2:4)),
expand.split=FALSE)
position(Active$Habitat) <- c(2, 4)
interaction2wt(logitAct ~ Treatment*Habitat, data=Active)
## from the above tables and graphs, it looks like the only thing
## going on in the data you posted is the linear effect of R.
## On your questions on glht and TukeyHSD.
## Without interactions or covariates they give the same answer.
## With interactions or covariates the default for glht is to give
different answers.
## The output from glht includes a warning to that effect.
##
## From ?glht
## Warning message:
## In mcp2matrix(model, linfct = linfct) :
## covariate interactions found -- default contrast might be inappropriate
##
## From ?glht, read about the interaction_average argument.
## With the glht argument interaction_average=TRUE, they give the same
answer
## With your example,
summary(glht(model3, linfct=mcp(Treatment="Tukey",
interaction_average=TRUE)))
TukeyHSD(model3, which="Treatment")
Rich
On Tue, Jan 3, 2012 at 7:26 PM, Anne Aubut <an438...@dal.ca> wrote:
Hello Richard,
Thank you so much for getting back to me. In the ?glht example, the
confidence intervals are the same and the p-values are very similar. I ran
a 2-way ANOVA and compared the results for the glht code with "Tukey" and
TukeyHSD for "Treatment", which was a significant main effect (output is
below). I found that the p-values for glht and TukeyHSD differed quite a
bit. If glht with "Tukey" is just another method to run Tukey HSD, I don't
understand why the two methods yeilded different results. If they are not
equivalent, how is glht calculating the p-values? I also ran my 2-way
ANOVA without the Treatment*Habitat interaction and I found that the glht
and TukeyHSD methods did provide the same p-values (I did not include this
output). Does this mean that glht is only equivalent to TukeyHSD when
non-significant interactions are removed? Should I be removing all of my
non-significant interaction terms prior to running post-hoc testing with
glht?
Treatment Habitat pActive
1G E 0.18541667
1G E 0.02500000
1G E 0.04208333
1G E 0.14847222
1G E 0.08055556
1G E 0.16777778
1G S 0.05111111
1G S 0.19083333
1G S 0.12333333
1G S 0.35722222
1G S 0.43750000
1G S 0.02638889
1R E 0.38736111
1R E 0.51180556
1R E 0.14916667
1R E 0.61041667
1R E 0.36013889
1R E 0.11347222
1R S 0.10805556
1R S 0.18722222
1R S 0.27625000
1R S 0.25236111
1R S 0.18208333
1R S 0.16152778
2G E 0.25916667
2G E 0.37194444
2G E 0.02263889
2G E 0.18402778
2G E 0.45750000
2G E 0.02250000
2G S 0.02958333
2G S 0.10069444
2G S 0.12875000
2G S 0.11361111
2G S 0.13680556
2G S 0.07875000
2R E 0.57513889
2R E 0.12888889
2R E 0.32000000
2R E 0.55736111
2R E 0.78888889
2R E 0.65055556
2R S 0.35527778
2R S 0.48361111
2R S 0.21361111
2R S 0.35277778
2R S 0.52611111
2R S 0.29416667
R+G E 0.37027778
R+G E 0.20263889
R+G E 0.07194444
R+G E 0.49041667
R+G E 0.21847222
R+G E 0.13555556
R+G S 0.20861111
R+G S 0.23986111
R+G S 0.02180556
R+G S 0.23250000
R+G S 0.28916667
R+G S 0.50208333
logitpAct<-logit(Active$**pActive)
model3<-aov(logitAct~**Treatment*Habitat,data=Active)
summary(glht(model3, linfct=mcp(Treatment="Tukey"))**)
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Tukey Contrasts
Fit: aov(formula = logitAct ~ Treatment * Habitat, data = Active)
Linear Hypotheses:
Estimate Std. Error t value Pr(>|t|)
1R - 1G == 0 1.6196 0.5936 2.728 0.06369 .
2G - 1G == 0 0.5468 0.5936 0.921 0.88736
2R - 1G == 0 2.3100 0.5936 3.892 0.00264 **
R+G - 1G == 0 1.0713 0.5936 1.805 0.38235
2G - 1R == 0 -1.0728 0.5936 -1.807 0.38095
2R - 1R == 0 0.6904 0.5936 1.163 0.77204
R+G - 1R == 0 -0.5483 0.5936 -0.924 0.88639
2R - 2G == 0 1.7632 0.5936 2.970 0.03516 *
R+G - 2G == 0 0.5245 0.5936 0.884 0.90164
R+G - 2R == 0 -1.2387 0.5936 -2.087 0.24185 <2.087%20%200.24185>
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
(Adjusted p values reported -- single-step method)
Warning message:
In mcp2matrix(model, linfct = linfct) :
covariate interactions found -- default contrast might be inappropriate
TukeyHSD(model3)
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = logitAct ~ Treatment * Habitat, data = Active)
$Treatment
diff lwr upr p adj
1R-1G 0.976208536 -0.2115769 2.1639939 0.1538496
2G-1G 0.008919932 -1.1788655 1.1967053 1.0000000
2R-1G 1.774309645 0.5865243 2.9620950 0.0009174
R+G-1G 0.735518351 -0.4522670 1.9233037 0.4123535
2G-1R -0.967288603 -2.1550740 0.2204968 0.1605251
2R-1R 0.798101110 -0.3896843 1.9858865 0.3299881
R+G-1R -0.240690185 -1.4284756 0.9470952 0.9783561
2R-2G 1.765389713 0.5776043 2.9531751 0.0009817
R+G-2G 0.726598419 -0.4611870 1.9143838 0.4247935
R+G-2R -1.038791294 -2.2265767 0.1489941 0.1128708
Quoting "Richard M. Heiberger" <r...@temple.edu>:
glht is probably what you should be using. Both TukeyHSD and glht give
essesntially identical confidence intervals for
the example in ?glht. What aren't you satisfied with?
amod <- aov(breaks ~ tension, data = warpbreaks)
confint(glht(amod, linfct = mcp(tension = "Tukey")))
TukeyHSD(amod)
On Mon, Jan 2, 2012 at 6:19 PM, Anne Aubut <an438...@dal.ca> wrote:
Hello,
I am trying to determine the most appropriate way to run post-hoc
comparisons on my lme model. I had originally planned to use Tukey HSD
method as I am interested in all possible comparisons between my
treatment
levels. TukeyHSD, however, does not work with lme. The only other code
that I was able to find, and which also seems to be widely used, is glht
specified with Tukey:
summary(glht(model, linfct=mcp(Treatment="Tukey"))****)
Out of curiosity, I ran TukeyHSD and the glht code for a simple ANOVA and
found that they had quite different p-values. If the glht code is not
running TukeyHSD, what does the "Tukey" in the code specify? Is using
glht
code appropriate if I am interested in a substitute for TukeyHSD? Are
there any other options for multiple comparisons for lme models? I am
really interested in knowing if the Tukey contrasts generated from the
glht
code is providing me with appropriate p-values for my post-hoc
comparisons.
I feel like I have reached a dead end and am not sure where else to look
for information on this issue. I would be grateful for any feedback on
this
matter.
Anne Cheverie
M.Sc. Candidate
Dalhousie University
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