So I checked it with the wilcox_test in the coin library, and got the
same result. That makes me more confident that I made a mistake, but
still doesn't help me find it
d =
data.frame(value=c(dropouts,remain),group=c(rep("dropout",length(dropouts)),rep("remain",length(remain))))
wilcox_test(value~group,data=d)
Sam
On Thu, Oct 6, 2011 at 10:35 AM, Sam Stewart <rhelp.st...@gmail.com> wrote:
> Hello List,
>
> I'm trying to prepare some lecture notes on non parametric methods,
> and I can't manually reproduce the results of the wilcox.test function
> for ordinal data.
>
> The data I'm using are from David Howell's website, available here
>
> http://www.uvm.edu/~dhowell/StatPages/More_Stuff/OrdinalChisq/OrdinalChiSq.html
>
> If I run the wilcox.test function on the data I get a p-value of
> .0407, but when I do it myself I get a p-value of 0.0530. It's not so
> much the jump across 0.05, but the fact that I thought I knew what the
> function was doing.
>
> I know from the R help page that there is some controversy about how
> exactly to calculate the test statistic, but that's not what is
> causing the problem, as I can get the same W value. Am I calculating
> the test statistic incorrectly?
>
> Thanks, sample code below
> Sam Stewart
>
> #Ordinal example
> dropouts = c(rep(0,25),rep(3,10),rep(2,9),rep(1,13),rep(4,6))
> remain = c(rep(0,31),rep(3,2),rep(2,6),rep(1,21),rep(4,3))
> tab2 = rbind(table(dropouts),table(remain))
> ordTest = wilcox.test(x=dropouts,y=remain,correct=FALSE,exact=FALSE)
> cumsum(colSums(tab2))
> W =
> max(c(sum(rank(cbind(dropouts,remain))[1:length(dropouts)]),sum(rank(cbind(dropouts,remain))[-(1:length(dropouts))])))
> n1 = length(dropouts)
> n2 = length(remain)
> testStat = (S-n1*(n1+n2+1)/2)/(sqrt(n1*n2*(n1+n2+1)/12))
> 2*(1-pnorm(testStat))
>
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