TukeyHSD(x, which, ordered = FALSE, conf.level = 0.95, …)aov fit.ordered is true then
the calculated differences in the means will all be positive. The
significant differences will be those for which the lwr end
point is positive.c("multicomp", "TukeyHSD"),
with one component for each term requested in which.
Each component is a matrix with columns diff giving the
difference in the observed means, lwr giving the lower
end point of the interval, upr giving the upper end point
and p adj giving the p-value after adjustment for the multiple
comparisons. There are print and plot methods for class
"TukeyHSD". The plot method does not accept
xlab, ylab or main arguments and creates its own
values for each plot."aov". When comparing the means for the levels of a factor in an analysis of
variance, a simple comparison using t-tests will inflate the
probability of declaring a significant difference when it is not in
fact present. This because the intervals are calculated with a
given coverage probability for each interval but the interpretation of
the coverage is usually with respect to the entire family of
intervals. John Tukey introduced intervals based on the range of the
sample means rather than the individual differences. The intervals
returned by this function are based on this Studentized range
statistics. The intervals constructed in this way would only apply exactly to
balanced designs where there are the same number of observations made
at each level of the factor. This function incorporates an adjustment
for sample size that produces sensible intervals for mildly unbalanced
designs. If which specifies non-factor terms these will be dropped with
a warning: if no terms are left this is an error.aov, qtukey, model.tables,
glht in package https://CRAN.R-project.org/package=multcomp.require(graphics)
summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks))
TukeyHSD(fm1, "tension", ordered = TRUE)
plot(TukeyHSD(fm1, "tension"))
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