Dear fellow R users,
Keywords: Kruskal-Wallis, Post-Hoc, pair-wise comparisons,
Nemenyi-Damico-Wolfe-Dunn test, coin package, oneway_test
I am using the "oneway_test" function in the R package "coin" and I am
obtaining results which I cannot believe are accurate. I do not wish to waste
anyone's time and so if the following problem is rather trivial, I apologize,
however I could not seem to resolve my problem with an online search and I am
fresh out of ideas.
I have carried out a Kruskal-Wallis test to compare breeding strategy variance
of my study organisms (rank data, therefore non-parametric, in oder of
increasing degree of "terrestrialization", in this case: adaptations to
breeding on land as opposed to in aquatic habitats) between habitat groups (I,
II and III). Subsequently I would like to do a "Post-Hoc test" or in other
words a set of corrected pair-wise comparisons to test the relationship between
individual groups. For this I would like to use the Nemenyi-Damico-Wolfe-Dunn
test in the "coin" package (aka oneway_test). However, when I apply it to my
data, I receive highly significant differences between all of my groups, which
when looking at my data, cannot be true. I have posted one of my command blocks
below containing my data set as well as the script adapted from the coin
package manual.
library(coin)
library(multcomp)
###this is my data:
mydata <- data.frame(breeding =
c(4,4,4,4,1,1,1,1,8,8,8,8,9,7,7,4,4,4,6,1,1,1,1,1,1,4,1,4,4,1,1,4,4,1,1,1,1,6,6,6,6,6,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,11,7,1,1,1,1,1,4,4,4,4,1,1,1,1,8,8,8,8,8,8,8,8,8,8,8,8,8,9,9,9,9,9,9,7,7,7,7,7,7,7,7,7,7,5,5,5,5,5,4,4,6,6,6,6,6,6,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,3,3,3,1,4,1,7,7,7,7,7,7,12,12,12,12,12,12,1,4,7,4,1,1,1,1,1,1,1,1,4),
habitat = factor(c(rep("I", 68), rep("II", 89),
rep("III", 12))))
###box plot to visualize data
boxplot(breeding~habitat,data=mydata,main="Boxplot of breeding
strategies",ylab="breeding strategy",col="gold",lty=1)
### Kruskal-Wallis test
kruskal_test(breeding ~ habitat, data = mydata, distribution = approximate(B =
9999))
### Nemenyi-Damico-Wolfe-Dunn test (joint ranking)
NDWD <- oneway_test(breeding ~ habitat, data = mydata,
ytrafo = function(data) trafo(data, numeric_trafo = rank),
xtrafo = function(data) trafo(data, factor_trafo = function(x)
model.matrix(~x - 1) %*% t(contrMat(table(x), "Tukey"))),
teststat = "max", distribution = approximate(B = 900000))
### global p-value
print(pvalue(NDWD))
### sites-by-site p values at alpha = 0.01 (page 244)
print(pvalue(NDWD, method = "single-step"))
I should be detecting some non-significance between groups I and III at least,
but the test comes back with extremely low p-values. Where am I going wrong?
Thank you very much for your help.
With kind regards
Christoph
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