power.tukey.test {PMCMRplus} | R Documentation |
Power Calculations for Balanced Tukey's Multiple Comparison Test
Description
Compute average per-pair power of Tukey's test for multiple comparison of means.
Usage
power.tukey.test(n, groups, delta, within.var, sig.level = 0.05)
Arguments
n |
number of observations (per group) |
groups |
number of groups |
delta |
true difference in means |
within.var |
within group variance |
sig.level |
significance level (Type I error probability) |
Details
The function has implemented the following Eq. to estimate average per-pair power for two-sided tests:
1 - \beta = 1 - t(q_{\alpha v k}/\sqrt{2}, v, \mathrm{ncp}) +
t(-q_{\alpha v k}/\sqrt{2}, v, \mathrm{ncp}),
with q_{\alpha v k}
the upper \alpha
quantile of
the studentised range distribution, with v = k (n - 1)
degree of freedom and k
the number of groups;
and t(. ~\mathrm{ncp})
the probability function of the non-central student t-distribution
with non-centrality parameter
\mathrm{ncp} = |\Delta| / \sqrt{s_{\mathrm{in}}^2 ~ 2 / n }.
Value
Object of class ‘power.htest
’,
a list of the arguments
(including the computed one) augmented with
method
and note
elements.
Source
The Eqs. were taken from Lecture 5, Determining Sample Size, Statistics 514, Fall 2015, Purdue University, IN, USA.
See Also
Examples
power.tukey.test(n = 11, groups = 5, delta = 30,
within.var = 333.7)
## compare with t-test, Bonferroni-correction
power.t.test(n = 11, delta = 30, sd = sqrt(333.7),
sig.level = 0.05 / 10)
## Not run:
powerMCTests(mu = c(rep(0,4), 30), n = 11,
parms = list(mean = 0,sd = sqrt(333.7)),
test = "tukeyTest")
## End(Not run)