On Jan 30, 2011, at 9:28 AM, Graham Smith wrote:
I am sure I am opening myself up to looking stupid,
Or exposing a failure to read the help page.
but I have two samples
with medians of 613.5 and 189 (difference in location of 424
compared to
the difference suggested from the wilcoxon of 291.5)
wilcox.test(pipwtCount,pipwdCount, conf.int=TRUE, na.rm=TRUE)
Wilcoxon rank sum test
data: pipwtCount and pipwdCount
W = 822, p-value = 0.01227
alternative hypothesis: true location shift is not equal to 0
95 percent confidence interval:
58 639
sample estimates:
difference in location
291.5
The Wilcoxon two sample test is not a test of equality of medians.
Read the Details help page. The estimated location is a pseudomedian.
> plot(density(pipwtCount))
> plot(density(pipwdCount, na.rm=TRUE))
Both are highly right-skewed.
--
David.
The data is here
pipwtCount
[1] 532 298 215 1588 38 180 284 376 5349 1024 650 605 1307
6147
21
[16] 453 23 1983 1048 464 2183 1028 1361 163 175 5944 569
622 793
70
[31] 67 1188 248 3010 19 2179 1339 408 113 739 2615 4619
pipwdCount
[1] 89 384 12 703 2 138 189 383 314 482 96 907 90
1193
154
[16] 305 61 414 4764 1066 121 143 102 174 44 2896 NA
1103 161
199
median(pipwtCount)
[1] 613.5
median(pipwdCount,na.rm=T)
[1] 189
613.5-189
[1] 424.5
I would appreciate if someone could point out the obvious to me, and
explain
why there is such a large discrepancy in the differences in location.
Many thanks,
Graham
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David Winsemius, MD
West Hartford, CT
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and provide commented, minimal, self-contained, reproducible code.
