I've been using SAS PROC MULTTEST to perform multiple comparisons on
data that are not normally distributed by using the stepdown bootstrap
procedures of Westfall and Young (1993). According to the SAS manual,
"the bootstrap method creates pseudo-data sets by sampling observations
with replacement from each within-stratum pool of observations. An
entire data set is thus created, and p-values for all tests are computed
on this pseudo-data set. A counter records whether the minimum p-value
from the pseudo-data set is less than or equal to the actual p-value for
each base test. (If there are R tests, then there are R such counters.)
This process is repeated a large number of times, and the proportion of
resampled data sets where the minimum pseudo-p-value is less than or
equal to an actual p-value is the adjusted p-value reported by PROC
MULTTEST. The algorithms are described by Westfall and Young (1993)."
R has the MULTCOMP package for simultaneous tests and confidence
intervals for general linear hypotheses in parametric models, but I'm
wondering if any one has created a package or routine that would allow
me calculate adjusted p-values in the non-parametric case. In
particular, I'm looking for a way to re-create the stepdown bootstrap
comparisons I performed with SAS using R. I've looked, but haven't
figured out how to do it in yet with R.
Thanks in advance for any insight!
-greg
Gregory Stewart, Ph.D.
Manager of Scientific Computing
The Evergreen State College
Olympia, WA 98505
360-867-6909
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