similar to: R-devel

The function 'wilcox.test' in R and S gives (almost) identical results (see below). 'qwilcox' however, does not: > qwilcox(p,5,5) p: 0.025 0.975 -------------------- R> 3 22 S> 18 37 I originally wanted to ask a questions, but then I found the answer. Given the confusion I run into, I wonder if this experience is worth reporting. The

I have a question concerning the Wilcoxon signed-rank test, and specifically, which R subroutine I should use for my particular dataset. There are three different commands in R (that I'm aware of) that calculate the Wilcoxon signed-rank test; wilcox.test, wilcox.exact, and wilcoxsign_test. When I run the three commands on the same dataset, I get different p-values. I'm hoping that

>>>>> Martin Maechler >>>>> on Thu, 12 Dec 2019 17:20:47 +0100 writes: >>>>> Karolis Koncevi?ius >>>>> on Mon, 9 Dec 2019 23:43:36 +0200 writes: >> So I tried adding Infinity support for all cases. And it >> is (as could be expected) more complicated than I >> thought. > "Of course

>>>>> Karolis Koncevi?ius >>>>> on Sat, 7 Dec 2019 20:55:36 +0200 writes: > Hello, > Writing to share some things I've found about wilcox.test() that seem a > a bit inconsistent. > 1. Inf values are not removed if paired=TRUE > # returns different results (Inf is removed): > wilcox.test(c(1,2,3,4), c(0,9,8,7))

So I tried adding Infinity support for all cases. And it is (as could be expected) more complicated than I thought. It is easy to add Inf support for the test. The problems start with conf.int=TRUE. Currently confidence intervals are computed via `uniroot()` and, in the case of infinities, we are computationally looking for roots over infinite interval which results in an error. I suspect this

Dear List, after updating the exactRanksumTests package I receive a warning that the package is not developed any further and that one should consider the coin package. I don't find the signed rank test in the coin package, only the Wilcoxon Mann Whitney U-Test. I only found a signed rank test in the stats package (wilcox.test) which is able to calculate the exact pvalues but unfortunately

how do I check the rank of a matrix ? say A= 1 0 0 0 1 0 then rank(A)=2 what is this function? thanks I did try help.search("rank"), but all the returned help information seem irrelevant to what I want. I would like to know how people search for help information like this. rank(base) Sample Ranks SignRank(stats) Distribution of the

>>>>> Karolis Koncevi?ius >>>>> on Mon, 9 Dec 2019 23:43:36 +0200 writes: > So I tried adding Infinity support for all cases. > And it is (as could be expected) more complicated than I thought. "Of course !" Thank you, Karolis, in any case! > It is easy to add Inf support for the test. The problems start with conf.int=TRUE.

Help needed! Has anyone access to StatXact? I just hacked exact two-sided p-values for rank tests (for package exactDistr, which will move to CRAN/contrib as exactRankTests soon ;-) and would like to compare the results of my implementation to that of StatXact. Could someone please calculate the exact one-sided (both greater and less) and two-sided p-values? # Data from the StatXact-4 manual,

Hi, I recently ran into the problem that I needed a Siegel-Tukey test for equal variability based on ranks. Maybe there is a package that has it implemented, but I could not find it. So I programmed an R function to do it. The Siegel-Tukey test requires to recode the ranks so that they express variability rather than ascending order. This is essentially what the code further below does. After the

Thank you for a fast response. Nice to see this mailing list being so alive. Regarding Inf issue: I agree with your assessment that Inf should not be removed. The code gave me an impression that Inf values were intentionally removed (since is.finite() was used everywhere, except for paired case). I will try to adjust my patch according to your feedback. One more thing: it seems like you

This is my first post to the mailing list and I guess it's a pretty stupid question but I can't figure it out. I hope this is the right forum for these kind of questions. Before I started using R I was using STATA to run a Wilcoxon signed-rank test on two variables. See data below:

Full_Name: Patrick Hodgson Version: 2.0 OS: solaris 2.9 Submission from: (NULL) (65.94.128.161) The value reported for the parameter W in the function wilcox.exact appears to be incorrect. I have checked the reference in the help file for this function (Myles & Hollander 1973, as well as 2nd ed. 1999 by same authors) and it is clear that W is the sum of the ranks of the data set with the

We've seen the integer overflow problem in ks.test before, easily solved. The help page says x and y must be numeric, so this is user error. I've added tests to the code. Why do people file bug reports without reading the help/man page? On Tue, 14 Jan 2003 bates@stat.wisc.edu wrote: > This was filed as a bug report on the Debian r-base package. It is > more properly a bug

This was filed as a bug report on the Debian r-base package. It is more properly a bug report on the ctest package in R. The default method for wilcox.test manipulates x and y without checking the class or data.class of these objects. Possible solutions are - create wilcox.test.factor (if appropriate) - check the class and/or data.class of x and y in wilcox.test.default and produce error

Dear, I'm using R software to evaluate Wilcoxon Rank Sum Test and I' getting one Warning message as this: > C1dea_com [1] 1.000 0.345 0.200 0.208 0.508 0.480 0.545 0.563 0.451 0.683 0.380 0.913 1.000 0.506 > C1dea_sem [1] 1.000 0.665 0.284 0.394 0.509 0.721 0.545 0.898 0.744 0.683 0.382 0.913 1.000 0.970 > wilcox.test(C1dea_sem,C1dea_com, paired = TRUE, alternative =

Hello, I am hoping someone could shed some light into the Wilcoxon Rank Sum Test for me? In looking through Stats references, the Mann-Whitney U-test and the Wilcoxon Rank Sum Test are statistically equivalent. When using the following dataset: m <- c(2.0863,2.1340,2.1008,1.9565,2.0413,NA,NA) f <- c(1.8938,1.9709,1.8613,2.0836,1.9485,2.0630,1.9143) and the wilcox.test command as

When I use wilcox.test, I get vastly different p-values than the problems from Statistics textbooks. For example: The following problem comes from "Applied Statistics and Probability for Engineers", 2nd Edition, by D. C. Montgomery. Page736, problem 14.7. The problem is to compare the sample data with a population median of 8.5. The book answer is p = 0.25, wilcox.test answer is p =

Given this example #start code a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) wilcox.test(a, b, paired=FALSE) #sum of rank for first sample sum.rank.a <-

Hi, I am using wilcox.test function to test the difference between the means of two samples. The data points are paired, so I am using a paired test. There is one strange case. Sample A has a higher mean than a sample B. However, wilcox.test function says that sample B has a significantly higher "mean rank" than sample A. How is it possible? Here is the code (data file is attached):