On Jul 5, 2013, at 16:34 , Humber Andrade wrote: > Thank you Dr. Dalgaard. I understood. I know that this list is not to discuss > statistics but I would be very glad if you or someone else can give me some > opinion on how to proceed. The kruskal.test says there are differences but > the multiple comparisons do not point out what are the differences. Can you > suggest a suitable way (maybe paired wilcoxon) to infer what are the > differences? I am asking for the hint because I am sure the journal > editor/reviewer will ask me to point out which groups differ from each other. >
I'm afraid it can't be done. You really can be in a situation where you reject the global null hypothesis that all groups are the same, yet cannot point out any two groups that differ from eachother. -pd > Regards, Humber > > > On Fri, Jul 5, 2013 at 11:04 AM, peter dalgaard <pda...@gmail.com> wrote: > > On Jul 5, 2013, at 15:00 , Humber Andrade wrote: > > > Thank you Prof. José Iparraguirre. Maybe I am wrong but I think the issues > > are not the same. His data doesn't showed significant differences after > > kruskal.test(), that was not my case. Anyway follow below my the results > > I've got and the database. > > > > This can happen. It is a matter of probability theory, not of R. The > following is a simplified paraphrase of what is going on: > > # 15 random normals, compare range test to variance test > # Simulate everything for simplicity > > > # Null distribution > M0 <- replicate(10000, rnorm(15)) > vv <- apply(M0,2,var) > rg <- apply(M0,2,range) > rr <- apply(rg,2,diff) > r.95 <- quantile(rr, .95) > v.95 <- quantile(vv, .95) > v.995 <- quantile(vv, .995) > > # Distribution at quadrupled variance > M <- replicate(10000, rnorm(15,sd=2)) > vv <- apply(M,2,var) > rg <- apply(M,2,range) > rr <- apply(rg,2,diff) > plot(rr,vv) > abline(h=c(v.95,v.995),v=r.95, col="red") > > Notice that the two statistics are correlated, but not equivalent. There are > cases where one value is beyond the .95 level and the other is not. Since it > is theoretically optimal to use the variance as the test statistic in this > model, there are quite a few more cases where rr is below the cutoff and vv > is above than the other way around. There are even a sizable number of cases > where vv is beyond v.995 and rr does not reach r.95. > > (The theoretical optimality applies only because I use an increased variance > alternative. For specific alternatives, the picture can change. Try it, for > instance with a single mean substantially different from the others: > > M <- replicate(10000, rnorm(15,mean=rep(c(4,0),c(1,14)))) > > ) > > > > Thank you, > > > > ################# > >> kruskal.test(data$resp,data$group) > > > > Kruskal-Wallis rank sum test > > > > data: data$resp and data$group > > Kruskal-Wallis chi-squared = 32.3546, df = 14, p-value = 0.003566 > > ################ > >> kruskalmc(data$resp,data$group) > > Multiple comparison test after Kruskal-Wallis > > p.value: 0.05 > > Comparisons > > obs.dif critical.dif difference > > A-B 4.8303571 62.37688 FALSE > > A-C 3.8928571 62.37688 FALSE > > A-D 0.4821429 62.37688 FALSE > > ............................................................. > > ............................................................. > > ............................................................. > > M-P 14.2500000 60.26179 FALSE > > N-O 1.3750000 60.26179 FALSE > > N-P 6.1250000 60.26179 FALSE > > O-P 4.7500000 60.26179 FALSE > > > > ############# database > > group resp A 0.1 A 581.8 A 90.5 A 70.1 A 820.1 A 1159.2 A 2478.1 > > A 2475.3 B 351.8 B 370.1 B 326.1 B 751.9 B 931 B 588.2 B 70.1 B > > 1754.9 C 289.8 C 254.1 C 370.3 C 459.8 C 412.5 C 591.5 C 986.9 C 890 > > D 425.6 D 397.4 D 464 D 370.9 D 417.3 D 455 D 568.2 D 599.4 E 405.1 > > E 626.2 E 299 E 493.8 E 362.6 E 309.8 E 522.7 E 433.3 F 698.6 F 42.5 > > F 7.4 F 10.6 F 95.8 F 497.5 F 987.9 F 925.1 G 492.9 G 376 G 413 G > > 278.3 G 344.2 G 292.2 G 429.4 G 368 H 241.6 H 230.5 H 310.4 H 372.5 > > H 366.1 H 307.9 H 480 H 529.8 I 296 I 288.8 I 302.1 I 300.8 I 150.1 > > I 381.9 I 583.1 I 489.4 J 1.2 J 18.6 J 7.7 J 11.6 J 48.1 J 121.8 J > > 1284.1 J 944.7 L 0.5 L 44.4 L 80.9 L 15.3 L 80 L 379.9 L 940.6 L > > 829.3 M 323.6 M 401.5 M 162.1 M 136.5 M 139.4 M 363.3 M 280.7 M > > 356.5 N 197.6 N 245.9 N 221.5 N 224.3 N 185.4 N 265.3 N 304.8 N > > 351.9 O 189.9 O 237.3 O 247.1 O 230.4 O 272.2 O 155.1 O 270.7 O > > 315.2 P 48.4 P 15.5 P 53.1 P 72.8 P 74.8 P 132.3 P 550 P 478.7 > > > > > > On Fri, Jul 5, 2013 at 8:06 AM, Jose Iparraguirre < > > jose.iparragui...@ageuk.org.uk> wrote: > > > >> Humber, > >> Have a look at this: > >> http://r.789695.n4.nabble.com/Multiple-Comparisons-Kruskal-Wallis-Test-kruskal-agricolae-and-kruskalmc-pgirmess-don-t-yield-the-sa-td4639004.html > >> Hope it helps. > >> Kind regards, > >> > >> José > >> > >> Prof. José Iparraguirre > >> Chief Economist > >> Age UK > >> > >> > >> > >> -----Original Message----- > >> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > >> On Behalf Of Humber Andrade > >> Sent: 05 July 2013 11:38 > >> To: r-help@r-project.org > >> Subject: [R] kruskal.test followed by kruskalmc > >> > >> Hi all, > >> > >> After running kruskal.test I have got results (p<0,005) pointing to reject > >> the hypothesis that the samples were draw from the same population. > >> Howerver when I run the kruskalmc there are no significant differences in > >> any of the multiple comparisons. Is that possible? Some clarification? > >> > >> Thanks, Humber > >> > >> > >> <https://sites.google.com/site/humberandrade> > >> > >> [[alternative HTML version deleted]] > >> > >> ______________________________________________ > >> R-help@r-project.org mailing list > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > >> > >> The Wireless from Age UK | Radio for grown-ups. > >> > >> www.ageuk.org.uk/thewireless > >> > >> > >> If you‚re looking for a radio station that offers real variety, tune in to > >> The Wireless from Age UK. > >> Whether you choose to listen through the website at > >> www.ageuk.org.uk/thewireless, on digital radio (currently available in > >> London and Yorkshire) or through our TuneIn Radio app, you can look forward > >> to an inspiring mix of music, conversation and useful information 24 hours > >> a day. > >> > >> > >> > >> > >> ------------------------------- > >> Age UK is a registered charity and company limited by guarantee, > >> (registered charity number 1128267, registered company number 6825798). > >> Registered office: Tavis House, 1-6 Tavistock Square, London WC1H 9NA. > >> > >> For the purposes of promoting Age UK Insurance, Age UK is an Appointed > >> Representative of Age UK Enterprises Limited, Age UK is an Introducer > >> Appointed Representative of JLT Benefit Solutions Limited and Simplyhealth > >> Access for the purposes of introducing potential annuity and health > >> cash plans customers respectively. 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