| TukeyHSD {stats} | R Documentation |
Create a set of confidence intervals on the differences between the
means of the levels of a factor with the specified family-wise
probability of coverage. The intervals are based on the Studentized
range statistic, Tukey's ‘Honest Significant Difference’
method. There is a plot method.
TukeyHSD(x, which, ordered = FALSE, conf.level = 0.95, ...)
x |
A fitted model object, usually an aov fit. |
which |
A list of terms in the fitted model for which the intervals should be calculated. Defaults to all the terms. |
ordered |
A logical value indicating if the levels of the factor
should be ordered according to increasing average in the sample
before taking differences. If 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. |
conf.level |
A numeric value between zero and one giving the family-wise confidence level to use. |
... |
Optional additional arguments. None are used at present. |
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.
Technically the intervals constructed in this way would only apply 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.
A list 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, and upr giving the upper end point.
Douglas Bates
Miller, R. G. (1981) Simultaneous Statistical Inference. Springer.
Yandell, B. S. (1997) Practical Data Analysis for Designed Experiments. Chapman & Hall.
data(warpbreaks) summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)) TukeyHSD(fm1, "tension", ordered = TRUE) plot(TukeyHSD(fm1, "tension"))