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.

Regards, Humber


On Fri, Jul 5, 2013 at 11:04 AM, peter dalgaard <pdalgd@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.Iparraguirre@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-bounces@r-project.org [mailto:r-help-bounces@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]]
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> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
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> > PLEASE do read the posting guide
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>
> --
> Peter Dalgaard, Professor
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Email: pd.mes@cbs.dk  Priv: PDalgd@gmail.com
>
>

<https://sites.google.com/site/humberandrade>

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