CODEWITHSUNDEEP
r
user2507608
user2507608

Reputation: 365

multivariate Mann-Whitney U test

Good day. I am search for some help/suggestions with a data set I have, for which I want to run a Mann-Whitney U test. A dummy set of the data.frame looks like this:

 Plant R1 R2 R3 R4 R5
     a  1  2  3  4  5
     a  6  7  8  9 10
     a 11 12 13 14 15
     b 16 17 18 19 20
     b 21 22 23 24 25
     b 26 27 28 29 30
     b 31 32 33 34 35
     c 36 37 38 39 40
     c 41 42 43 44 45
     c 46 47 48 49 50
     d 51 52 53 54 55
     d 56 57 58 59 60

I have 26 different plants and I would like to test the hypothesis that between all pairs of plant species (a,b,c...etc) there is no significant difference between the median reflectance of each individual waveband (r1,r2,r3...r400.There are 400 waveband columns). The hypothesis is to be tested 325 times for all possible combination of 26 plant species. The null hypothesis should be tested at significance level of ά = 0.00015 (to correct for the Bonferroni effect, 0.05/325).

I am aware of the wilcox.test command to perform a pairwise comparison. I tried searching the Cran repository and found npmc package, but it is no longer maintained.

I would like the result to look like this:

Comparison   R1   R2   R3   R4   R5
    ab      p-value
    ac
    ad

But I have no idea where to begin. Can anyone offer any suggestions please. Thanks in advance.

Kurt

Upvotes: 2

Views: 4785

Answers (5)

Edmond Geraud Aguilar
Edmond Geraud Aguilar

Reputation: 23

What I suggest, is to run a permutational MANOVA, using some distance, as the euclidean.

Then, if the multivariate assumption is hold, perform a Hotelling T test, if not, you can use the permutational Hotelling test.

Upvotes: 0

Alexander Serebrenik
Alexander Serebrenik

Reputation: 3577

Since you are doing multiple comparisons you can consider multiple contrast test procedures, such as T~ described by Frank Konietschke, Ludwig A. Hothorn, and Edgar Brunner. Since you are interested in comparing all possible pairs you should use Tukey contrasts. Discussion of the statistical machinery behind T~ is, probably, not appropriate for StackOverflow and be better done on Cross Validated. The T~ procedure has been implemented in the nparcomp package. Since T~ respects transitivity, its results can be presented as a simplified graph as suggested by Vasilescu et al.

Upvotes: 2

savinson
savinson

Reputation: 1267

Try to use DepthProc library for R

    library(DepthProc)
    x <- mvrnorm(100, c(0,0), diag(2))
    y <- mvrnorm(100, c(0,0), diag(2)*1.4)
    mWilcoxonTest(x,y)

It's a multivariate analog to Wilcoxon-Mann-Whitney test 3 based on data depth concept 2:
https://projecteuclid.org/download/pdf_1/euclid.aos/1176344722
https://projecteuclid.org/download/pdfview_1/euclid.ss/1113832733

Upvotes: 0

LeeZamparo
LeeZamparo

Reputation: 437

It sounds like you should look into applying Dunn's test. Briefly, Dunn's test is a post-hoc group-by-group difference of location test (using multiple test correction) that can be applied if you reject the null hypothesis in a Kruskal-Wallis test (and determine there is at least one group drawn from a different distribution).

See this answer on Cross Validated for a more detailed example. There is an R-package (dunn.test) that provides a dunn.test method which uses a similar interface to wicox.test

Upvotes: 0

user2507608
user2507608

Reputation: 365

I managed this using

 ttest<- pairwise.wilcox.test(ttest.data[,i],Species,conf.level = 0.95, p.adj = "bonf")
 library(reshape)
 ttest.result<- melt (ttest[[3]])

Upvotes: 1

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