Hi,
I had a first occasion to try multiple comparisons (of intercepts, I
suppose) following a significant result in an ANCOVA. As until now I
was doing this with JMP, I compared my results and the post-hoc
comparisons were different between R and JMP.
I chose to use an example data set from JMP because it was small, so
I can show it here. It is not the best example for an ANCOVA because
the factor "Drug" does not have a significant effect, but it will do.
>drug$x
[1] 11 8 5 14 19 6 10 6 11 3 6 6 7 8 18 8 19 8 5 15 16
13 11 9 21 16 12
[28] 12 7 12
>
> drug$y
[1] 6 0 2 8 11 4 13 1 8 0 0 2 3 1 18 4 14 9 1 9 13
10 18 5 23 12 5
[28] 16 1 20
> drug$Drug
[1] a a a a a a a a a a d d d d d d d d d d f f f f f f f f f f
Levels: a d f
I did not manage to get TukeyHSD to work if I fitted the ANCOVA with
lm, so I used aov:
my.anc <- aov(y~x+Drug, data=drug)
> summary(my.anc)
Df Sum Sq Mean Sq F value Pr(>F)
x 1 802.94 802.94 50.0393 1.639e-07 ***
Drug 2 68.55 34.28 2.1361 0.1384
Residuals 26 417.20 16.05
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
I tried this to compare the Drugs, correcting for the effect of x.
> TukeyHSD(my.anc, "Drug")
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = y ~ x + Drug, data = drug)
$Drug
diff lwr upr p adj
d-a 0.03131758 -4.420216 4.482851 0.9998315
f-a 3.04677613 -1.404758 7.498310 0.2239746
f-d 3.01545855 -1.436075 7.466992 0.2305187
Warning message:
non-factors ignored: x in: replications(paste("~", xx), data = mf)
I am not sure about the Warning, maybe it is the reason the
differences shown here are different from those shown in JMP for the
same analysis. Maybe TukeyHSD is not meant to be used with non-
factors (i.e. not valid for ANCOVAs)?
I just found the package multcomp and am not sure I understand it
well yet, but its Tukey comparisons gave the same results as JMP.
> summary(glht(m3, linfct=mcp(Drug="Tukey")))
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Tukey Contrasts
Fit: aov(formula = y ~ x + Drug, data = drug)
Linear Hypotheses:
Estimate Std. Error t value p value
d - a == 0 0.109 1.795 0.061 0.998
f - a == 0 3.446 1.887 1.826 0.181
f - d == 0 3.337 1.854 1.800 0.189
(Adjusted p values reported)
I would very much like to understand why these two "Tukey" tests gave
different results in R.
Thanks in advance,
Denis
Hi,
I am reposting this as I fear my original post (on Oct. 4th) got
buried by all the excitement of the R 2.6 release...
I had a first occasion to try multiple comparisons (of intercepts, I
suppose) following a significant result in an ANCOVA. As until now I
was doing this with JMP, I compared my results and the post-hoc
comparisons were different between R and JMP.
I chose to use an example data set from JMP because it was small, so
I can show it here. It is not the best example for an ANCOVA because
the factor "Drug" does not have a significant effect, but it will do.
>drug$x
[1] 11 8 5 14 19 6 10 6 11 3 6 6 7 8 18 8 19 8 5 15 16
13 11 9 21 16 12
[28] 12 7 12
>
> drug$y
[1] 6 0 2 8 11 4 13 1 8 0 0 2 3 1 18 4 14 9 1 9 13
10 18 5 23 12 5
[28] 16 1 20
> drug$Drug
[1] a a a a a a a a a a d d d d d d d d d d f f f f f f f f f f
Levels: a d f
I did not manage to get TukeyHSD to work if I fitted the ANCOVA with
lm, so I used aov:
my.anc <- aov(y~x+Drug, data=drug)
> summary(my.anc)
Df Sum Sq Mean Sq F value Pr(>F)
x 1 802.94 802.94 50.0393 1.639e-07 ***
Drug 2 68.55 34.28 2.1361 0.1384
Residuals 26 417.20 16.05
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
I tried this to compare the Drugs, correcting for the effect of x.
> TukeyHSD(my.anc, "Drug")
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = y ~ x + Drug, data = drug)
$Drug
diff lwr upr p adj
d-a 0.03131758 -4.420216 4.482851 0.9998315
f-a 3.04677613 -1.404758 7.498310 0.2239746
f-d 3.01545855 -1.436075 7.466992 0.2305187
Warning message:
non-factors ignored: x in: replications(paste("~", xx), data = mf)
I am not sure about the Warning, maybe it is the reason the
differences shown here are different from those shown in JMP for the
same analysis. Maybe TukeyHSD is not meant to be used with non-
factors (i.e. not valid for ANCOVAs)?
I just found the package multcomp and am not sure I understand it
well yet, but its Tukey comparisons gave the same results as JMP.
> summary(glht(m3, linfct=mcp(Drug="Tukey")))
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Tukey Contrasts
Fit: aov(formula = y ~ x + Drug, data = drug)
Linear Hypotheses:
Estimate Std. Error t value p value
d - a == 0 0.109 1.795 0.061 0.998
f - a == 0 3.446 1.887 1.826 0.181
f - d == 0 3.337 1.854 1.800 0.189
(Adjusted p values reported)
I would very much like to understand why these two "Tukey" tests gave
different results in R.
Thanks in advance,
Denis