Dear list,
I am facing a problem in my statistical analyses on R.
My experiments are about plants, I record there growth after each cutting (every
3 weeks).
'BC' is for the plant, and '1' to '5' is the time of
cutting and recording.
The data and R script are :
""
BourdCoup <- c(21, 7.2, 9.2, 0, 8.52, 14.7, 8.31, 6.2, 127.05, 115.2, 100.7,
24, 162.64, 136.8, 95.1, 78.93, 104.7, 21.2, 35.8, 21, 66, 103.4, 74.9, 43.8,
32.1, 14.9, 2.1, 14.4, 36.1, 35, 53.2, 28.9, 0,0,0,8.7,2.7,4.2,0,21.2)
groupBC <-
as.factor(rep(c("BC1","BC2","BC3","BC4","BC5"),
times=1, each=8))
BC1 <- BourdCoup[groupBC=="BC1"]
BC2 <- BourdCoup[groupBC=="BC2"]
BC3 <- BourdCoup[groupBC=="BC3"]
BC4 <- BourdCoup[groupBC=="BC4"]
BC5 <- BourdCoup[groupBC=="BC5"]
""
I therefore realise a wilcoxon test for paired series, and compare them one by
one.
""> wilcox.test(BC1,BC2, paired=T)
Wilcoxon signed rank test
data: BC1 and BC2
V = 0, p-value = 0.007813
alternative hypothesis: true location shift is not equal to 0
""
The problem is that it always gives me the same p-value (0.007813), whatever
vectors I try. And when I remove "paired=T" from the script, all
p-values differ.
Could you help me please ?
Juliette BELLAY
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On Aug 8, 2012, at 18:16 , BELLAY Juliette wrote:> Dear list, > > I am facing a problem in my statistical analyses on R. > My experiments are about plants, I record there growth after each cutting (every 3 weeks). > 'BC' is for the plant, and '1' to '5' is the time of cutting and recording. > > The data and R script are : > > "" > BourdCoup <- c(21, 7.2, 9.2, 0, 8.52, 14.7, 8.31, 6.2, 127.05, 115.2, 100.7, 24, 162.64, 136.8, 95.1, 78.93, 104.7, 21.2, 35.8, 21, 66, 103.4, 74.9, 43.8, 32.1, 14.9, 2.1, 14.4, 36.1, 35, 53.2, 28.9, 0,0,0,8.7,2.7,4.2,0,21.2) > > groupBC <- as.factor(rep(c("BC1","BC2","BC3","BC4","BC5"), times=1, each=8)) > > BC1 <- BourdCoup[groupBC=="BC1"] > BC2 <- BourdCoup[groupBC=="BC2"] > BC3 <- BourdCoup[groupBC=="BC3"] > BC4 <- BourdCoup[groupBC=="BC4"] > BC5 <- BourdCoup[groupBC=="BC5"] > "" > > I therefore realise a wilcoxon test for paired series, and compare them one by one. > > "" >> wilcox.test(BC1,BC2, paired=T) > > Wilcoxon signed rank test > > data: BC1 and BC2 > V = 0, p-value = 0.007813 > alternative hypothesis: true location shift is not equal to 0 > "" > > The problem is that it always gives me the same p-value (0.007813), whatever vectors I try. And when I remove "paired=T" from the script, all p-values differ. > > Could you help me please ?First of all, that's not actually true:> wilcox.test(BC1, BC5, paired=TRUE)$p.value[1] 0.3828125> wilcox.test(BC1, BC4, paired=TRUE)$p.value[1] 0.015625 If V=0, then the p-value is 2 * 2^(-8) = 1/128 = 0.0078125. This happens if all differences have the same sign. And....> outer(1:5,1:5, Vectorize(function(i,j) sum(sign(M[,i]-M[,j]))))[,1] [,2] [,3] [,4] [,5] [1,] 0 -8 -8 -6 4 [2,] 8 0 8 8 8 [3,] 8 -8 0 8 8 [4,] 6 -8 -8 0 8 [5,] -4 -8 -8 -8 0 ... that is indeed what happens in 8 out of 10 paired comparisons. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com